



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
						import os

						# The slow tests are often failing with OOM error on GPU
						# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
						# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
						UpperCAmelCase_			      =  '''platform'''

						import jax
						import jax.numpy as jnp

						from transformers import MarianTokenizer
						from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right
def   UpperCAmelCase							(		A__				,  A__				,  A__=None				,  A__=None				,  A__=None				,  A__=None				,  A__=None				,  A__=None				,  )							->					Tuple:
	if attention_mask is None:
		_snake_case					:							List[str]					     =   np.where(input_ids != config.pad_token_id				,  1				,  0     )
	if decoder_attention_mask is None:
		_snake_case					:							Optional[int]					     =   np.where(decoder_input_ids != config.pad_token_id				,  1				,  0     )
	if head_mask is None:
		_snake_case					:							Optional[Any]					     =   np.ones((config.encoder_layers, config.encoder_attention_heads)     )
	if decoder_head_mask is None:
		_snake_case					:							Optional[int]					     =   np.ones((config.decoder_layers, config.decoder_attention_heads)     )
	if cross_attn_head_mask is None:
		_snake_case					:							Optional[Any]					     =   np.ones((config.decoder_layers, config.decoder_attention_heads)     )
	return {
	    "input_ids": input_ids,
	    "decoder_input_ids": decoder_input_ids,
	    "attention_mask": attention_mask,
	    "decoder_attention_mask": attention_mask,
	}





class 		__SCREAMING_SNAKE_CASE      :






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=13						,	SCREAMING_SNAKE_CASE__=7						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=99						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=4						,	SCREAMING_SNAKE_CASE__=4						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=32						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__=0.02						,	):






					"""simple docstring"""

					_snake_case					:							Tuple					     =   parent
					_snake_case					:							Dict					     =   batch_size
					_snake_case					:							Any					     =   seq_length
					_snake_case					:							str					     =   is_training
					_snake_case					:							Optional[int]					     =   use_labels
					_snake_case					:							Optional[Any]					     =   vocab_size
					_snake_case					:							int					     =   hidden_size
					_snake_case					:							List[str]					     =   num_hidden_layers
					_snake_case					:							List[str]					     =   num_attention_heads
					_snake_case					:							str					     =   intermediate_size
					_snake_case					:							Union[str, Any]					     =   hidden_act
					_snake_case					:							str					     =   hidden_dropout_prob
					_snake_case					:							List[Any]					     =   attention_probs_dropout_prob
					_snake_case					:							Optional[Any]					     =   max_position_embeddings
					_snake_case					:							List[str]					     =   eos_token_id
					_snake_case					:							List[Any]					     =   pad_token_id
					_snake_case					:							Tuple					     =   bos_token_id
					_snake_case					:							List[str]					     =   initializer_range







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							List[Any]					     =   np.clip(ids_tensor([self.batch_size, self.seq_length - 1]						,	self.vocab_size      )						,	3						,	self.vocab_size      )
					_snake_case					:							Optional[int]					     =   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)						,	dtype=np.intaa      ))						,	-1      )

					_snake_case					:							Optional[Any]					     =   shift_tokens_right(SCREAMING_SNAKE_CASE__						,	1						,	2      )

					_snake_case					:							Union[str, Any]					     =   MarianConfig(
					    vocab_size=self.vocab_size						,	d_model=self.hidden_size						,	encoder_layers=self.num_hidden_layers						,	decoder_layers=self.num_hidden_layers						,	encoder_attention_heads=self.num_attention_heads						,	decoder_attention_heads=self.num_attention_heads						,	encoder_ffn_dim=self.intermediate_size						,	decoder_ffn_dim=self.intermediate_size						,	dropout=self.hidden_dropout_prob						,	attention_dropout=self.attention_probs_dropout_prob						,	max_position_embeddings=self.max_position_embeddings						,	eos_token_id=self.eos_token_id						,	bos_token_id=self.bos_token_id						,	pad_token_id=self.pad_token_id						,	initializer_range=self.initializer_range						,	use_cache=SCREAMING_SNAKE_CASE__						,	)
					_snake_case					:							Any					     =   prepare_marian_inputs_dict(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					return config, inputs_dict







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case     , _snake_case					:							str					     =   self.prepare_config_and_inputs()
					return config, inputs_dict







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Dict					     =   20
					_snake_case					:							Any					     =   model_class_name(SCREAMING_SNAKE_CASE__      )

					_snake_case					:							Tuple					     =   model.encode(inputs_dict["""input_ids"""]      )

					_snake_case     , _snake_case					:							Tuple					     =   (
					    inputs_dict["""decoder_input_ids"""],
					    inputs_dict["""decoder_attention_mask"""],
					)

					_snake_case					:							Union[str, Any]					     =   model.init_cache(decoder_input_ids.shape[0]						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							str					     =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,	dtype="""i4"""      )

					_snake_case					:							str					     =   jnp.broadcast_to(
					    jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]						,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,	)
					_snake_case					:							Dict					     =   model.decode(
					    decoder_input_ids[:, :-1]						,	SCREAMING_SNAKE_CASE__						,	decoder_attention_mask=SCREAMING_SNAKE_CASE__						,	past_key_values=SCREAMING_SNAKE_CASE__						,	decoder_position_ids=SCREAMING_SNAKE_CASE__						,	)

					_snake_case					:							Tuple					     =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,	dtype="""i4"""      )
					_snake_case					:							Any					     =   model.decode(
					    decoder_input_ids[:, -1:]						,	SCREAMING_SNAKE_CASE__						,	decoder_attention_mask=SCREAMING_SNAKE_CASE__						,	past_key_values=outputs_cache.past_key_values						,	decoder_position_ids=SCREAMING_SNAKE_CASE__						,	)

					_snake_case					:							Optional[int]					     =   model.decode(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )

					_snake_case					:							str					     =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
					self.parent.assertTrue(diff < 1e-3						,	msg=f'''Max diff is {diff}'''      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							List[str]					     =   20
					_snake_case					:							List[Any]					     =   model_class_name(SCREAMING_SNAKE_CASE__      )

					_snake_case					:							Optional[int]					     =   model.encode(inputs_dict["""input_ids"""]      )

					_snake_case     , _snake_case					:							Optional[int]					     =   (
					    inputs_dict["""decoder_input_ids"""],
					    inputs_dict["""decoder_attention_mask"""],
					)

					_snake_case					:							Any					     =   jnp.concatenate(
					    [
					        decoder_attention_mask,
					        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])      ),
					    ]						,	axis=-1						,	)

					_snake_case					:							Any					     =   model.init_cache(decoder_input_ids.shape[0]						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Any					     =   jnp.broadcast_to(
					    jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]						,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,	)

					_snake_case					:							int					     =   model.decode(
					    decoder_input_ids[:, :-1]						,	SCREAMING_SNAKE_CASE__						,	decoder_attention_mask=SCREAMING_SNAKE_CASE__						,	past_key_values=SCREAMING_SNAKE_CASE__						,	decoder_position_ids=SCREAMING_SNAKE_CASE__						,	)
					_snake_case					:							str					     =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,	dtype="""i4"""      )
					_snake_case					:							List[Any]					     =   model.decode(
					    decoder_input_ids[:, -1:]						,	SCREAMING_SNAKE_CASE__						,	past_key_values=outputs_cache.past_key_values						,	decoder_attention_mask=SCREAMING_SNAKE_CASE__						,	decoder_position_ids=SCREAMING_SNAKE_CASE__						,	)

					_snake_case					:							Tuple					     =   model.decode(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	decoder_attention_mask=SCREAMING_SNAKE_CASE__      )

					_snake_case					:							Union[str, Any]					     =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
					self.parent.assertTrue(diff < 1e-3						,	msg=f'''Max diff is {diff}'''      )





@require_flax
class 		__SCREAMING_SNAKE_CASE      (				lowercase__					,			unittest.TestCase					,			lowercase__      ):






				"""simple docstring"""

				SCREAMING_SNAKE_CASE_ 				=  True
				SCREAMING_SNAKE_CASE_ 				=  (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
				SCREAMING_SNAKE_CASE_ 				=  (FlaxMarianMTModel,) if is_flax_available() else ()







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							int					     =   FlaxMarianModelTester(self      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case     , _snake_case					:							Dict					     =   self.model_tester.prepare_config_and_inputs()
					for model_class in self.all_model_classes:
						self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case     , _snake_case					:							Dict					     =   self.model_tester.prepare_config_and_inputs()
					for model_class in self.all_model_classes:
						self.model_tester.check_use_cache_forward_with_attn_mask(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case     , _snake_case					:							Optional[Any]					     =   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						with self.subTest(model_class.__name__      ):
							_snake_case					:							Optional[Any]					     =   self._prepare_for_class(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
							_snake_case					:							Optional[int]					     =   model_class(SCREAMING_SNAKE_CASE__      )

							@jax.jit
							def encode_jitted(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None						,	**SCREAMING_SNAKE_CASE__      ):
								return model.encode(input_ids=SCREAMING_SNAKE_CASE__						,	attention_mask=SCREAMING_SNAKE_CASE__      )

							with self.subTest("""JIT Enabled"""      ):
								_snake_case					:							Dict					     =   encode_jitted(**SCREAMING_SNAKE_CASE__      ).to_tuple()

							with self.subTest("""JIT Disabled"""      ):
								with jax.disable_jit():
									_snake_case					:							List[str]					     =   encode_jitted(**SCREAMING_SNAKE_CASE__      ).to_tuple()

							self.assertEqual(len(SCREAMING_SNAKE_CASE__      )						,	len(SCREAMING_SNAKE_CASE__      )      )
							for jitted_output, output in zip(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):
								self.assertEqual(jitted_output.shape						,	output.shape      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case     , _snake_case					:							Dict					     =   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						with self.subTest(model_class.__name__      ):
							_snake_case					:							Union[str, Any]					     =   model_class(SCREAMING_SNAKE_CASE__      )
							_snake_case					:							List[str]					     =   model.encode(inputs_dict["""input_ids"""]						,	inputs_dict["""attention_mask"""]      )

							_snake_case					:							List[str]					     =   {
							    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
							    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
							    """encoder_outputs""": encoder_outputs,
							}

							@jax.jit
							def decode_jitted(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):
								return model.decode(
								    decoder_input_ids=SCREAMING_SNAKE_CASE__						,	decoder_attention_mask=SCREAMING_SNAKE_CASE__						,	encoder_outputs=SCREAMING_SNAKE_CASE__						,	)

							with self.subTest("""JIT Enabled"""      ):
								_snake_case					:							Union[str, Any]					     =   decode_jitted(**SCREAMING_SNAKE_CASE__      ).to_tuple()

							with self.subTest("""JIT Disabled"""      ):
								with jax.disable_jit():
									_snake_case					:							Optional[int]					     =   decode_jitted(**SCREAMING_SNAKE_CASE__      ).to_tuple()

							self.assertEqual(len(SCREAMING_SNAKE_CASE__      )						,	len(SCREAMING_SNAKE_CASE__      )      )
							for jitted_output, output in zip(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):
								self.assertEqual(jitted_output.shape						,	output.shape      )







				@slow
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					for model_class_name in self.all_model_classes:
						_snake_case					:							Optional[int]					     =   model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de"""      )
						# FlaxMarianForSequenceClassification expects eos token in input_ids
						_snake_case					:							Dict					     =   np.ones((1, 1)      ) * model.config.eos_token_id
						_snake_case					:							List[Any]					     =   model(SCREAMING_SNAKE_CASE__      )
						self.assertIsNotNone(SCREAMING_SNAKE_CASE__      )





@require_flax
@require_sentencepiece
@require_tokenizers
class 		__SCREAMING_SNAKE_CASE      (				unittest.TestCase      ):






				"""simple docstring"""

				SCREAMING_SNAKE_CASE_ 				=  None
				SCREAMING_SNAKE_CASE_ 				=  None







				@classmethod
				def 		__lowerCamelCase(				cls      ):






					"""simple docstring"""

					_snake_case					:							Optional[Any]					     =   f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
					return cls







				@cached_property
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					return MarianTokenizer.from_pretrained(self.model_name      )







				@property
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					return self.tokenizer.eos_token_id







				@cached_property
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							FlaxMarianMTModel					     =   FlaxMarianMTModel.from_pretrained(self.model_name      )
					self.assertEqual(model.config.decoder_start_token_id						,	model.config.pad_token_id      )
					return model







				def 		__lowerCamelCase(				self						,	**SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Union[str, Any]					     =   self.translate_src_text(**SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(self.expected_text						,	SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	**SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Optional[Any]					     =   self.tokenizer(self.src_text						,	padding=SCREAMING_SNAKE_CASE__						,	return_tensors="""np"""						,	**SCREAMING_SNAKE_CASE__      )
					_snake_case					:							int					     =   self.model.generate(
					    model_inputs.input_ids						,	attention_mask=model_inputs.attention_mask						,	num_beams=2						,	max_length=1_28						,	).sequences
					_snake_case					:							List[Any]					     =   self.tokenizer.batch_decode(SCREAMING_SNAKE_CASE__						,	skip_special_tokens=SCREAMING_SNAKE_CASE__      )
					return generated_words





@require_flax
@require_sentencepiece
@require_tokenizers
class 		__SCREAMING_SNAKE_CASE      (				lowercase__      ):






				"""simple docstring"""

				SCREAMING_SNAKE_CASE_ 				=  'en'
				SCREAMING_SNAKE_CASE_ 				=  'fr'
				SCREAMING_SNAKE_CASE_ 				=  [
				    'I am a small frog.',
				    'Now I can forget the 100 words of german that I know.',
				]
				SCREAMING_SNAKE_CASE_ 				=  [
				    'Je suis une petite grenouille.',
				    'Maintenant, je peux oublier les 100 mots d\'allemand que je connais.',
				]







				@slow
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 		__SCREAMING_SNAKE_CASE      (				lowercase__      ):






				"""simple docstring"""

				SCREAMING_SNAKE_CASE_ 				=  'fr'
				SCREAMING_SNAKE_CASE_ 				=  'en'
				SCREAMING_SNAKE_CASE_ 				=  [
				    'Donnez moi le micro.',
				    'Tom et Mary étaient assis à une table.',  # Accents
				]
				SCREAMING_SNAKE_CASE_ 				=  [
				    'Give me the microphone.',
				    'Tom and Mary were sitting at a table.',
				]







				@slow
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 		__SCREAMING_SNAKE_CASE      (				lowercase__      ):






				"""simple docstring"""

				SCREAMING_SNAKE_CASE_ 				=  'mt'
				SCREAMING_SNAKE_CASE_ 				=  'en'
				SCREAMING_SNAKE_CASE_ 				=  ['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
				SCREAMING_SNAKE_CASE_ 				=  ['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']







				@slow
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 		__SCREAMING_SNAKE_CASE      (				lowercase__      ):






				"""simple docstring"""

				SCREAMING_SNAKE_CASE_ 				=  'en'
				SCREAMING_SNAKE_CASE_ 				=  'de'
				SCREAMING_SNAKE_CASE_ 				=  [
				    'I am a small frog.',
				    'Now I can forget the 100 words of german that I know.',
				    'Tom asked his teacher for advice.',
				    'That\'s how I would do it.',
				    'Tom really admired Mary\'s courage.',
				    'Turn around and close your eyes.',
				]
				SCREAMING_SNAKE_CASE_ 				=  [
				    'Ich bin ein kleiner Frosch.',
				    'Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.',
				    'Tom bat seinen Lehrer um Rat.',
				    'So würde ich das machen.',
				    'Tom bewunderte Marias Mut wirklich.',
				    'Drehen Sie sich um und schließen Sie die Augen.',
				]







				@slow
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 		__SCREAMING_SNAKE_CASE      (				lowercase__      ):






				"""simple docstring"""

				SCREAMING_SNAKE_CASE_ 				=  'en'
				SCREAMING_SNAKE_CASE_ 				=  'zh'
				SCREAMING_SNAKE_CASE_ 				=  ['My name is Wolfgang and I live in Berlin']
				SCREAMING_SNAKE_CASE_ 				=  ['我叫沃尔夫冈 我住在柏林']







				@slow
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 		__SCREAMING_SNAKE_CASE      (				lowercase__      ):






				"""simple docstring"""

				SCREAMING_SNAKE_CASE_ 				=  'ru'
				SCREAMING_SNAKE_CASE_ 				=  'fr'
				SCREAMING_SNAKE_CASE_ 				=  ['Он показал мне рукопись своей новой пьесы.']
				SCREAMING_SNAKE_CASE_ 				=  ['Il m\'a montré le manuscrit de sa nouvelle pièce.']







				@slow
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 		__SCREAMING_SNAKE_CASE      (				lowercase__      ):






				"""simple docstring"""

				SCREAMING_SNAKE_CASE_ 				=  'en'
				SCREAMING_SNAKE_CASE_ 				=  'ROMANCE'
				SCREAMING_SNAKE_CASE_ 				=  [
				    '>>fr<< Don\'t spend so much time watching TV.',
				    '>>pt<< Your message has been sent.',
				    '>>es<< He\'s two years older than me.',
				]
				SCREAMING_SNAKE_CASE_ 				=  [
				    'Ne passez pas autant de temps à regarder la télé.',
				    'A sua mensagem foi enviada.',
				    'Es dos años más viejo que yo.',
				]







				@slow
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					self._assert_generated_batch_equal_expected()










import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation
def   UpperCAmelCase							(		A__				,  A__				,  A__ = False				,  A__ = 1				,  A__ = 1				,  A__ = 1_00_00				,  )							->					Optional[int]:
	assert len(timesteps.shape     ) == 1, "Timesteps should be a 1d-array"

	_snake_case					:							Tuple					     =   embedding_dim // 2
	_snake_case					:							Optional[Any]					     =   -math.log(A__     ) * torch.arange(
	    start=0				,  end=A__				,  dtype=torch.floataa				,  device=timesteps.device     )
	_snake_case					:							Dict					     =   exponent / (half_dim - downscale_freq_shift)

	_snake_case					:							Optional[int]					     =   torch.exp(A__     )
	_snake_case					:							List[str]					     =   timesteps[:, None].float() * emb[None, :]

	# scale embeddings
	_snake_case					:							Optional[int]					     =   scale * emb

	# concat sine and cosine embeddings
	_snake_case					:							Union[str, Any]					     =   torch.cat([torch.sin(A__     ), torch.cos(A__     )]				,  dim=-1     )

	# flip sine and cosine embeddings
	if flip_sin_to_cos:
		_snake_case					:							Optional[Any]					     =   torch.cat([emb[:, half_dim:], emb[:, :half_dim]]				,  dim=-1     )

	# zero pad
	if embedding_dim % 2 == 1:
		_snake_case					:							Optional[Any]					     =   torch.nn.functional.pad(A__				,  (0, 1, 0, 0)     )
	return emb
def   UpperCAmelCase							(		A__				,  A__				,  A__=False				,  A__=0     )							->					Optional[Any]:
	_snake_case					:							str					     =   np.arange(A__				,  dtype=np.floataa     )
	_snake_case					:							int					     =   np.arange(A__				,  dtype=np.floataa     )
	_snake_case					:							Union[str, Any]					     =   np.meshgrid(A__				,  A__     )  # here w goes first
	_snake_case					:							Optional[int]					     =   np.stack(A__				,  axis=0     )

	_snake_case					:							List[Any]					     =   grid.reshape([2, 1, grid_size, grid_size]     )
	_snake_case					:							Optional[int]					     =   get_ad_sincos_pos_embed_from_grid(A__				,  A__     )
	if cls_token and extra_tokens > 0:
		_snake_case					:							Any					     =   np.concatenate([np.zeros([extra_tokens, embed_dim]     ), pos_embed]				,  axis=0     )
	return pos_embed
def   UpperCAmelCase							(		A__				,  A__     )							->					List[str]:
	if embed_dim % 2 != 0:
		raise ValueError("""embed_dim must be divisible by 2"""     )

	# use half of dimensions to encode grid_h
	_snake_case					:							int					     =   get_ad_sincos_pos_embed_from_grid(embed_dim // 2				,  grid[0]     )  # (H*W, D/2)
	_snake_case					:							List[str]					     =   get_ad_sincos_pos_embed_from_grid(embed_dim // 2				,  grid[1]     )  # (H*W, D/2)

	_snake_case					:							List[Any]					     =   np.concatenate([emb_h, emb_w]				,  axis=1     )  # (H*W, D)
	return emb
def   UpperCAmelCase							(		A__				,  A__     )							->					str:
	if embed_dim % 2 != 0:
		raise ValueError("""embed_dim must be divisible by 2"""     )

	_snake_case					:							Union[str, Any]					     =   np.arange(embed_dim // 2				,  dtype=np.floataa     )
	omega /= embed_dim / 2.0
	_snake_case					:							Optional[Any]					     =   1.0 / 1_00_00**omega  # (D/2,)

	_snake_case					:							Union[str, Any]					     =   pos.reshape(-1     )  # (M,)
	_snake_case					:							Dict					     =   np.einsum("""m,d->md"""				,  A__				,  A__     )  # (M, D/2), outer product

	_snake_case					:							Union[str, Any]					     =   np.sin(A__     )  # (M, D/2)
	_snake_case					:							Tuple					     =   np.cos(A__     )  # (M, D/2)

	_snake_case					:							Union[str, Any]					     =   np.concatenate([emb_sin, emb_cos]				,  axis=1     )  # (M, D)
	return emb





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__=2_24						,	SCREAMING_SNAKE_CASE__=2_24						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=7_68						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	):






					"""simple docstring"""

					super().__init__()

					_snake_case					:							Tuple					     =   (height // patch_size) * (width // patch_size)
					_snake_case					:							Union[str, Any]					     =   flatten
					_snake_case					:							str					     =   layer_norm

					_snake_case					:							List[Any]					     =   nn.Convad(
					    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	kernel_size=(patch_size, patch_size)						,	stride=SCREAMING_SNAKE_CASE__						,	bias=SCREAMING_SNAKE_CASE__      )
					if layer_norm:
						_snake_case					:							Dict					     =   nn.LayerNorm(SCREAMING_SNAKE_CASE__						,	elementwise_affine=SCREAMING_SNAKE_CASE__						,	eps=1e-6      )
					else:
						_snake_case					:							Union[str, Any]					     =   None

					_snake_case					:							Tuple					     =   get_ad_sincos_pos_embed(SCREAMING_SNAKE_CASE__						,	int(num_patches**0.5      )      )
					self.register_buffer("""pos_embed"""						,	torch.from_numpy(SCREAMING_SNAKE_CASE__      ).float().unsqueeze(0      )						,	persistent=SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							List[str]					     =   self.proj(SCREAMING_SNAKE_CASE__      )
					if self.flatten:
						_snake_case					:							Any					     =   latent.flatten(2      ).transpose(1						,	2      )  # BCHW -> BNC
					if self.layer_norm:
						_snake_case					:							Any					     =   self.norm(SCREAMING_SNAKE_CASE__      )
					return latent + self.pos_embed





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = "silu"						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__=None						,	):






					"""simple docstring"""

					super().__init__()

					_snake_case					:							List[Any]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )

					if cond_proj_dim is not None:
						_snake_case					:							Dict					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	bias=SCREAMING_SNAKE_CASE__      )
					else:
						_snake_case					:							Tuple					     =   None

					_snake_case					:							int					     =   get_activation(SCREAMING_SNAKE_CASE__      )

					if out_dim is not None:
						_snake_case					:							Any					     =   out_dim
					else:
						_snake_case					:							int					     =   time_embed_dim
					_snake_case					:							str					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )

					if post_act_fn is None:
						_snake_case					:							Tuple					     =   None
					else:
						_snake_case					:							Optional[Any]					     =   get_activation(SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None      ):






					"""simple docstring"""

					if condition is not None:
						_snake_case					:							int					     =   sample + self.cond_proj(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Dict					     =   self.linear_a(SCREAMING_SNAKE_CASE__      )

					if self.act is not None:
						_snake_case					:							Optional[int]					     =   self.act(SCREAMING_SNAKE_CASE__      )

					_snake_case					:							Any					     =   self.linear_a(SCREAMING_SNAKE_CASE__      )

					if self.post_act is not None:
						_snake_case					:							str					     =   self.post_act(SCREAMING_SNAKE_CASE__      )
					return sample





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							List[str]					     =   num_channels
					_snake_case					:							List[Any]					     =   flip_sin_to_cos
					_snake_case					:							List[str]					     =   downscale_freq_shift







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Any					     =   get_timestep_embedding(
					    SCREAMING_SNAKE_CASE__						,	self.num_channels						,	flip_sin_to_cos=self.flip_sin_to_cos						,	downscale_freq_shift=self.downscale_freq_shift						,	)
					return t_emb





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__ = 2_56						,	SCREAMING_SNAKE_CASE__ = 1.0						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=False      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							Any					     =   nn.Parameter(torch.randn(SCREAMING_SNAKE_CASE__      ) * scale						,	requires_grad=SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Union[str, Any]					     =   log
					_snake_case					:							Any					     =   flip_sin_to_cos

					if set_W_to_weight:
						# to delete later
						_snake_case					:							int					     =   nn.Parameter(torch.randn(SCREAMING_SNAKE_CASE__      ) * scale						,	requires_grad=SCREAMING_SNAKE_CASE__      )

						_snake_case					:							List[Any]					     =   self.W







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					if self.log:
						_snake_case					:							Any					     =   torch.log(SCREAMING_SNAKE_CASE__      )

					_snake_case					:							Any					     =   x[:, None] * self.weight[None, :] * 2 * np.pi

					if self.flip_sin_to_cos:
						_snake_case					:							Dict					     =   torch.cat([torch.cos(SCREAMING_SNAKE_CASE__      ), torch.sin(SCREAMING_SNAKE_CASE__      )]						,	dim=-1      )
					else:
						_snake_case					:							int					     =   torch.cat([torch.sin(SCREAMING_SNAKE_CASE__      ), torch.cos(SCREAMING_SNAKE_CASE__      )]						,	dim=-1      )
					return out





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	):






					"""simple docstring"""

					super().__init__()

					_snake_case					:							List[Any]					     =   height
					_snake_case					:							Optional[int]					     =   width
					_snake_case					:							Any					     =   num_embed
					_snake_case					:							int					     =   embed_dim

					_snake_case					:							Optional[Any]					     =   nn.Embedding(self.num_embed						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Any					     =   nn.Embedding(self.height						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							List[Any]					     =   nn.Embedding(self.width						,	SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Any					     =   self.emb(SCREAMING_SNAKE_CASE__      )

					_snake_case					:							List[str]					     =   self.height_emb(torch.arange(self.height						,	device=index.device      ).view(1						,	self.height      )      )

					# 1 x H x D -> 1 x H x 1 x D
					_snake_case					:							List[str]					     =   height_emb.unsqueeze(2      )

					_snake_case					:							List[Any]					     =   self.width_emb(torch.arange(self.width						,	device=index.device      ).view(1						,	self.width      )      )

					# 1 x W x D -> 1 x 1 x W x D
					_snake_case					:							List[str]					     =   width_emb.unsqueeze(1      )

					_snake_case					:							int					     =   height_emb + width_emb

					# 1 x H x W x D -> 1 x L xD
					_snake_case					:							int					     =   pos_emb.view(1						,	self.height * self.width						,	-1      )

					_snake_case					:							str					     =   emb + pos_emb[:, : emb.shape[1], :]

					return emb





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							Any					     =   dropout_prob > 0
					_snake_case					:							Optional[int]					     =   nn.Embedding(num_classes + use_cfg_embedding						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Union[str, Any]					     =   num_classes
					_snake_case					:							Tuple					     =   dropout_prob







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None      ):






					"""simple docstring"""

					if force_drop_ids is None:
						_snake_case					:							Dict					     =   torch.rand(labels.shape[0]						,	device=labels.device      ) < self.dropout_prob
					else:
						_snake_case					:							List[str]					     =   torch.tensor(force_drop_ids == 1      )
					_snake_case					:							List[Any]					     =   torch.where(SCREAMING_SNAKE_CASE__						,	self.num_classes						,	SCREAMING_SNAKE_CASE__      )
					return labels







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None      ):






					"""simple docstring"""

					_snake_case					:							Union[str, Any]					     =   self.dropout_prob > 0
					if (self.training and use_dropout) or (force_drop_ids is not None):
						_snake_case					:							Dict					     =   self.token_drop(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[Any]					     =   self.embedding_table(SCREAMING_SNAKE_CASE__      )
					return embeddings





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__ = 10_24						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 10						,	):






					"""simple docstring"""

					super().__init__()

					_snake_case					:							str					     =   num_image_text_embeds
					_snake_case					:							Optional[Any]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	self.num_image_text_embeds * cross_attention_dim      )
					_snake_case					:							Optional[Any]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   text_embeds.shape[0]

					# image
					_snake_case					:							Optional[Any]					     =   self.image_embeds(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							List[str]					     =   image_text_embeds.reshape(SCREAMING_SNAKE_CASE__						,	self.num_image_text_embeds						,	-1      )

					# text
					_snake_case					:							Dict					     =   self.text_proj(SCREAMING_SNAKE_CASE__      )

					return torch.cat([image_text_embeds, text_embeds]						,	dim=1      )





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 32						,	):






					"""simple docstring"""

					super().__init__()

					_snake_case					:							Dict					     =   num_image_text_embeds
					_snake_case					:							int					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	self.num_image_text_embeds * cross_attention_dim      )
					_snake_case					:							Optional[int]					     =   nn.LayerNorm(SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Tuple					     =   image_embeds.shape[0]

					# image
					_snake_case					:							Tuple					     =   self.image_embeds(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Tuple					     =   image_embeds.reshape(SCREAMING_SNAKE_CASE__						,	self.num_image_text_embeds						,	-1      )
					_snake_case					:							Union[str, Any]					     =   self.norm(SCREAMING_SNAKE_CASE__      )
					return image_embeds





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=0.1      ):






					"""simple docstring"""

					super().__init__()

					_snake_case					:							Optional[Any]					     =   Timesteps(num_channels=2_56						,	flip_sin_to_cos=SCREAMING_SNAKE_CASE__						,	downscale_freq_shift=1      )
					_snake_case					:							Optional[Any]					     =   TimestepEmbedding(in_channels=2_56						,	time_embed_dim=SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Dict					     =   LabelEmbedding(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None      ):






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   self.time_proj(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[int]					     =   self.timestep_embedder(timesteps_proj.to(dtype=SCREAMING_SNAKE_CASE__      )      )  # (N, D)

					_snake_case					:							Tuple					     =   self.class_embedder(SCREAMING_SNAKE_CASE__      )  # (N, D)

					_snake_case					:							Tuple					     =   timesteps_emb + class_labels  # (N, D)

					return conditioning





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = 64      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							List[Any]					     =   nn.LayerNorm(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Any					     =   AttentionPooling(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							List[Any]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[Any]					     =   nn.LayerNorm(SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							str					     =   self.norma(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Any					     =   self.pool(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[Any]					     =   self.proj(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Tuple					     =   self.norma(SCREAMING_SNAKE_CASE__      )
					return hidden_states





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 15_36      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							Optional[Any]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							str					     =   nn.LayerNorm(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[int]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							int					     =   self.text_proj(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Union[str, Any]					     =   self.text_norm(SCREAMING_SNAKE_CASE__      )

					# image
					_snake_case					:							Any					     =   self.image_proj(SCREAMING_SNAKE_CASE__      )

					return time_image_embeds + time_text_embeds





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 15_36      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							Union[str, Any]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Tuple					     =   nn.LayerNorm(SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   self.image_proj(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Any					     =   self.image_norm(SCREAMING_SNAKE_CASE__      )
					return time_image_embeds





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 15_36      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							Optional[Any]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[int]					     =   nn.LayerNorm(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Any					     =   nn.Sequential(
					    nn.Convad(3						,	16						,	3						,	padding=1      )						,	nn.SiLU()						,	nn.Convad(16						,	16						,	3						,	padding=1      )						,	nn.SiLU()						,	nn.Convad(16						,	32						,	3						,	padding=1						,	stride=2      )						,	nn.SiLU()						,	nn.Convad(32						,	32						,	3						,	padding=1      )						,	nn.SiLU()						,	nn.Convad(32						,	96						,	3						,	padding=1						,	stride=2      )						,	nn.SiLU()						,	nn.Convad(96						,	96						,	3						,	padding=1      )						,	nn.SiLU()						,	nn.Convad(96						,	2_56						,	3						,	padding=1						,	stride=2      )						,	nn.SiLU()						,	nn.Convad(2_56						,	4						,	3						,	padding=1      )						,	)







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Dict					     =   self.image_proj(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							int					     =   self.image_norm(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[Any]					     =   self.input_hint_block(SCREAMING_SNAKE_CASE__      )
					return time_image_embeds, hint





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							Union[str, Any]					     =   dtype
					_snake_case					:							Optional[int]					     =   nn.Parameter(torch.randn(1						,	SCREAMING_SNAKE_CASE__      ) / embed_dim**0.5      )
					_snake_case					:							Optional[int]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	dtype=self.dtype      )
					_snake_case					:							Dict					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	dtype=self.dtype      )
					_snake_case					:							Optional[int]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	dtype=self.dtype      )
					_snake_case					:							Dict					     =   num_heads
					_snake_case					:							Tuple					     =   embed_dim // self.num_heads







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case     , _snake_case     , _snake_case					:							Dict					     =   x.size()

					def shape(SCREAMING_SNAKE_CASE__      ):
						# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
						_snake_case					:							Tuple					     =   x.view(SCREAMING_SNAKE_CASE__						,	-1						,	self.num_heads						,	self.dim_per_head      )
						# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
						_snake_case					:							Optional[int]					     =   x.transpose(1						,	2      )
						# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
						_snake_case					:							Optional[int]					     =   x.reshape(bs * self.num_heads						,	-1						,	self.dim_per_head      )
						# (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
						_snake_case					:							Optional[int]					     =   x.transpose(1						,	2      )
						return x

					_snake_case					:							int					     =   x.mean(dim=1						,	keepdim=SCREAMING_SNAKE_CASE__      ) + self.positional_embedding.to(x.dtype      )
					_snake_case					:							Any					     =   torch.cat([class_token, x]						,	dim=1      )  # (bs, length+1, width)

					# (bs*n_heads, class_token_length, dim_per_head)
					_snake_case					:							Optional[int]					     =   shape(self.q_proj(SCREAMING_SNAKE_CASE__      )      )
					# (bs*n_heads, length+class_token_length, dim_per_head)
					_snake_case					:							str					     =   shape(self.k_proj(SCREAMING_SNAKE_CASE__      )      )
					_snake_case					:							List[str]					     =   shape(self.v_proj(SCREAMING_SNAKE_CASE__      )      )

					# (bs*n_heads, class_token_length, length+class_token_length):
					_snake_case					:							Dict					     =   1 / math.sqrt(math.sqrt(self.dim_per_head      )      )
					_snake_case					:							Any					     =   torch.einsum("""bct,bcs->bts"""						,	q * scale						,	k * scale      )  # More stable with f16 than dividing afterwards
					_snake_case					:							Tuple					     =   torch.softmax(weight.float()						,	dim=-1      ).type(weight.dtype      )

					# (bs*n_heads, dim_per_head, class_token_length)
					_snake_case					:							Optional[int]					     =   torch.einsum("""bts,bcs->bct"""						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )

					# (bs, length+1, width)
					_snake_case					:							Tuple					     =   a.reshape(SCREAMING_SNAKE_CASE__						,	-1						,	1      ).transpose(1						,	2      )

					return a[:, 0, :]  # cls_token





