



'''simple docstring'''







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
							SCREAMING_SNAKE_CASE__     : Union[str, Any]				=   """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  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE=None ,		SCREAMING_SNAKE_CASE=None ,		SCREAMING_SNAKE_CASE=None ,		SCREAMING_SNAKE_CASE=None ,		SCREAMING_SNAKE_CASE=None ,		SCREAMING_SNAKE_CASE=None ,		):
		if attention_mask is None:
				SCREAMING_SNAKE_CASE_						:int										=      np.where(input_ids != config.pad_token_id ,		1 ,		0     )
		if decoder_attention_mask is None:
				SCREAMING_SNAKE_CASE_						:str										=      np.where(decoder_input_ids != config.pad_token_id ,		1 ,		0     )
		if head_mask is None:
				SCREAMING_SNAKE_CASE_						:Any										=      np.ones((config.encoder_layers, config.encoder_attention_heads)     )
		if decoder_head_mask is None:
				SCREAMING_SNAKE_CASE_						:int										=      np.ones((config.decoder_layers, config.decoder_attention_heads)     )
		if cross_attn_head_mask is None:
				SCREAMING_SNAKE_CASE_						: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 				__lowerCAmelCase:






			def __init__(   self  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							str      ,       SCREAMING_SNAKE_CASE  :							List[str]=13      ,       SCREAMING_SNAKE_CASE  :							List[Any]=7      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=True      ,       SCREAMING_SNAKE_CASE  :							Tuple=False      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=99      ,       SCREAMING_SNAKE_CASE  :							List[Any]=16      ,       SCREAMING_SNAKE_CASE  :							str=2      ,       SCREAMING_SNAKE_CASE  :							List[Any]=4      ,       SCREAMING_SNAKE_CASE  :							List[Any]=4      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]="gelu"      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=0.1      ,       SCREAMING_SNAKE_CASE  :							List[str]=0.1      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=32      ,       SCREAMING_SNAKE_CASE  :							List[Any]=2      ,       SCREAMING_SNAKE_CASE  :							Any=1      ,       SCREAMING_SNAKE_CASE  :							int=0      ,       SCREAMING_SNAKE_CASE  :							str=0.02      ,       ):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:str										=      np.clip(ids_tensor([self.batch_size, self.seq_length - 1]      ,       self.vocab_size	)      ,       3      ,       self.vocab_size	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)      ,       dtype=np.intaa	))      ,       -1	)

					SCREAMING_SNAKE_CASE_						:Tuple										=      shift_tokens_right(SCREAMING_SNAKE_CASE      ,       1      ,       2	)

					SCREAMING_SNAKE_CASE_						:int										=      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      ,       )
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      prepare_marian_inputs_dict(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
					return config, inputs_dict






			def 				_lowercase	(   self  :							str	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:int										=      self.prepare_config_and_inputs()
					return config, inputs_dict






			def 				_lowercase	(   self  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      20
					SCREAMING_SNAKE_CASE_						:List[Any]										=      model_class_name(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Dict										=      model.encode(inputs_dict['input_ids']	)

					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Dict										=      (
					    inputs_dict['decoder_input_ids'],
					    inputs_dict['decoder_attention_mask'],
					)

					SCREAMING_SNAKE_CASE_						:Any										=      model.init_cache(decoder_input_ids.shape[0]      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Any										=      jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,       dtype='i4'	)

					SCREAMING_SNAKE_CASE_						:Optional[int]										=      jnp.broadcast_to(
					    jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]      ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,       )
					SCREAMING_SNAKE_CASE_						:Tuple										=      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      ,       )

					SCREAMING_SNAKE_CASE_						:List[str]										=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,       dtype='i4'	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      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      ,       )

					SCREAMING_SNAKE_CASE_						:str										=      model.decode(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)

					SCREAMING_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}'	)






			def 				_lowercase	(   self  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							str      ,       SCREAMING_SNAKE_CASE  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      20
					SCREAMING_SNAKE_CASE_						:int										=      model_class_name(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      model.encode(inputs_dict['input_ids']	)

					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[int]										=      (
					    inputs_dict['decoder_input_ids'],
					    inputs_dict['decoder_attention_mask'],
					)

					SCREAMING_SNAKE_CASE_						:List[str]										=      jnp.concatenate(
					    [
					        decoder_attention_mask,
					        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])	),
					    ]      ,       axis=-1      ,       )

					SCREAMING_SNAKE_CASE_						:Tuple										=      model.init_cache(decoder_input_ids.shape[0]      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
					SCREAMING_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)      ,       )

					SCREAMING_SNAKE_CASE_						:Tuple										=      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      ,       )
					SCREAMING_SNAKE_CASE_						:Any										=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,       dtype='i4'	)
					SCREAMING_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      ,       )

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      model.decode(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       decoder_attention_mask=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Tuple										=      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 				__lowerCAmelCase(							lowerCAmelCase__		,			unittest.TestCase		,			lowerCAmelCase__      ):
			__snake_case  :		Any		=      True
			__snake_case  :		Dict		=      (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
			__snake_case  :		Any		=      (FlaxMarianMTModel,) if is_flax_available() else ()






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Any										=      FlaxMarianModelTester(self	)






			def 				_lowercase	(   self  :							List[str]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      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 				_lowercase	(   self  :							List[str]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						,		SCREAMING_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 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__	):
									SCREAMING_SNAKE_CASE_						:Tuple										=      self._prepare_for_class(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
									SCREAMING_SNAKE_CASE_						:Dict										=      model_class(SCREAMING_SNAKE_CASE	)

									@jax.jit
									def encode_jitted(SCREAMING_SNAKE_CASE  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							Any=None      ,       **SCREAMING_SNAKE_CASE  :							List[str]	):
											return model.encode(input_ids=SCREAMING_SNAKE_CASE      ,       attention_mask=SCREAMING_SNAKE_CASE	)

									with self.subTest('JIT Enabled'	):
											SCREAMING_SNAKE_CASE_						:int										=      encode_jitted(**SCREAMING_SNAKE_CASE	).to_tuple()

									with self.subTest('JIT Disabled'	):
											with jax.disable_jit():
													SCREAMING_SNAKE_CASE_						: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 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						,		SCREAMING_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__	):
									SCREAMING_SNAKE_CASE_						:int										=      model_class(SCREAMING_SNAKE_CASE	)
									SCREAMING_SNAKE_CASE_						:int										=      model.encode(inputs_dict['input_ids']      ,       inputs_dict['attention_mask']	)

									SCREAMING_SNAKE_CASE_						:Any										=      {
									    '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  :							Dict      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							int	):
											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'	):
											SCREAMING_SNAKE_CASE_						:Tuple										=      decode_jitted(**SCREAMING_SNAKE_CASE	).to_tuple()

									with self.subTest('JIT Disabled'	):
											with jax.disable_jit():
													SCREAMING_SNAKE_CASE_						:List[Any]										=      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 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					for model_class_name in self.all_model_classes:
							SCREAMING_SNAKE_CASE_						:Dict										=      model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de'	)
							# FlaxMarianForSequenceClassification expects eos token in input_ids
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      np.ones((1, 1)	) * model.config.eos_token_id
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      model(SCREAMING_SNAKE_CASE	)
							self.assertIsNotNone(SCREAMING_SNAKE_CASE	)




@require_flax
@require_sentencepiece
@require_tokenizers
class 				__lowerCAmelCase(							unittest.TestCase      ):
			__snake_case  :		List[str]		=      None
			__snake_case  :		Optional[int]		=      None






			@classmethod
			def 				_lowercase	(   cls  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
					return cls






			@cached_property
			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					return MarianTokenizer.from_pretrained(self.model_name	)






			@property
			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					return self.tokenizer.eos_token_id






			@cached_property
			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					SCREAMING_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 				_lowercase	(   self  :							Any      ,       **SCREAMING_SNAKE_CASE  :							List[str]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      self.translate_src_text(**SCREAMING_SNAKE_CASE	)
					self.assertListEqual(self.expected_text      ,       SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							Optional[Any]      ,       **SCREAMING_SNAKE_CASE  :							str	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      self.tokenizer(self.src_text      ,       padding=SCREAMING_SNAKE_CASE      ,       return_tensors='np'      ,       **SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      self.model.generate(
					    model_inputs.input_ids      ,       attention_mask=model_inputs.attention_mask      ,       num_beams=2      ,       max_length=128      ,       ).sequences
					SCREAMING_SNAKE_CASE_						:Any										=      self.tokenizer.batch_decode(SCREAMING_SNAKE_CASE      ,       skip_special_tokens=SCREAMING_SNAKE_CASE	)
					return generated_words




@require_flax
@require_sentencepiece
@require_tokenizers
class 				__lowerCAmelCase(							lowerCAmelCase__      ):
			__snake_case  :		int		=      'en'
			__snake_case  :		Optional[int]		=      'fr'
			__snake_case  :		Tuple		=      [
			    'I am a small frog.',
			    'Now I can forget the 100 words of german that I know.',
			]
			__snake_case  :		Tuple		=      [
			    'Je suis une petite grenouille.',
			    'Maintenant, je peux oublier les 100 mots d\'allemand que je connais.',
			]






			@slow
			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 				__lowerCAmelCase(							lowerCAmelCase__      ):
			__snake_case  :		Tuple		=      'fr'
			__snake_case  :		Union[str, Any]		=      'en'
			__snake_case  :		List[Any]		=      [
			    'Donnez moi le micro.',
			    'Tom et Mary étaient assis à une table.',  # Accents
			]
			__snake_case  :		str		=      [
			    'Give me the microphone.',
			    'Tom and Mary were sitting at a table.',
			]






			@slow
			def 				_lowercase	(   self  :							List[str]	):




					"""simple docstring"""







					self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 				__lowerCAmelCase(							lowerCAmelCase__      ):
			__snake_case  :		Union[str, Any]		=      'mt'
			__snake_case  :		Tuple		=      'en'
			__snake_case  :		Optional[Any]		=      ['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
			__snake_case  :		Dict		=      ['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']






			@slow
			def 				_lowercase	(   self  :							str	):




					"""simple docstring"""







					self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 				__lowerCAmelCase(							lowerCAmelCase__      ):
			__snake_case  :		str		=      'en'
			__snake_case  :		str		=      'de'
			__snake_case  :		Any		=      [
			    '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.',
			]
			__snake_case  :		List[Any]		=      [
			    '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 				_lowercase	(   self  :							List[str]	):




					"""simple docstring"""







					self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 				__lowerCAmelCase(							lowerCAmelCase__      ):
			__snake_case  :		int		=      'en'
			__snake_case  :		Optional[int]		=      'zh'
			__snake_case  :		Dict		=      ['My name is Wolfgang and I live in Berlin']
			__snake_case  :		Optional[int]		=      ['我叫沃尔夫冈 我住在柏林']






			@slow
			def 				_lowercase	(   self  :							Union[str, Any]	):




					"""simple docstring"""







					self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 				__lowerCAmelCase(							lowerCAmelCase__      ):
			__snake_case  :		Dict		=      'ru'
			__snake_case  :		Any		=      'fr'
			__snake_case  :		List[Any]		=      ['Он показал мне рукопись своей новой пьесы.']
			__snake_case  :		Optional[Any]		=      ['Il m\'a montré le manuscrit de sa nouvelle pièce.']






			@slow
			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 				__lowerCAmelCase(							lowerCAmelCase__      ):
			__snake_case  :		List[str]		=      'en'
			__snake_case  :		str		=      'ROMANCE'
			__snake_case  :		Union[str, Any]		=      [
			    '>>fr<< Don\'t spend so much time watching TV.',
			    '>>pt<< Your message has been sent.',
			    '>>es<< He\'s two years older than me.',
			]
			__snake_case  :		str		=      [
			    'Ne passez pas autant de temps à regarder la télé.',
			    'A sua mensagem foi enviada.',
			    'Es dos años más viejo que yo.',
			]






			@slow
			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					self._assert_generated_batch_equal_expected()


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel

def    UpperCamelCase_ (	a_  ,				a_		)     ->Optional[int]:
		A				=UNetaDModel(**a_		)
		A				=checkpoint["all_modules.0.W"].data
		A				=checkpoint["all_modules.0.W"].data
		A				=checkpoint["all_modules.1.weight"].data
		A				=checkpoint["all_modules.1.bias"].data

		A				=checkpoint["all_modules.2.weight"].data
		A				=checkpoint["all_modules.2.bias"].data

		A				=checkpoint["all_modules.3.weight"].data
		A				=checkpoint["all_modules.3.bias"].data

		A				=checkpoint[list(checkpoint.keys()		)[-4]].data
		A				=checkpoint[list(checkpoint.keys()		)[-3]].data
		A				=checkpoint[list(checkpoint.keys()		)[-2]].data
		A				=checkpoint[list(checkpoint.keys()		)[-1]].data

		A				=4

		def set_attention_weights(a_  ,				a_  ,				a_		):
				A				=old_checkpoint[f'''all_modules.{index}.NIN_0.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_1.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_2.W'''].data.T

				A				=old_checkpoint[f'''all_modules.{index}.NIN_0.b'''].data
				A				=old_checkpoint[f'''all_modules.{index}.NIN_1.b'''].data
				A				=old_checkpoint[f'''all_modules.{index}.NIN_2.b'''].data

				A				=old_checkpoint[f'''all_modules.{index}.NIN_3.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_3.b'''].data

				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

		def set_resnet_weights(a_  ,				a_  ,				a_		):
				A				=old_checkpoint[f'''all_modules.{index}.Conv_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.Conv_0.bias'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

				A				=old_checkpoint[f'''all_modules.{index}.Conv_1.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.Conv_1.bias'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_1.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_1.bias'''].data

				A				=old_checkpoint[f'''all_modules.{index}.Dense_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.Dense_0.bias'''].data

				if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
						A				=old_checkpoint[f'''all_modules.{index}.Conv_2.weight'''].data
						A				=old_checkpoint[f'''all_modules.{index}.Conv_2.bias'''].data

		for i, block in enumerate(new_model_architecture.downsample_blocks		):
				A				=hasattr(a_  ,				"attentions"		)
				for j in range(len(block.resnets		)		):
						set_resnet_weights(block.resnets[j]  ,				a_  ,				a_		)
						module_index += 1
						if has_attentions:
								set_attention_weights(block.attentions[j]  ,				a_  ,				a_		)
								module_index += 1

				if hasattr(a_  ,				"downsamplers"		) and block.downsamplers is not None:
						set_resnet_weights(block.resnet_down  ,				a_  ,				a_		)
						module_index += 1
						A				=checkpoint[f'''all_modules.{module_index}.Conv_0.weight'''].data
						A				=checkpoint[f'''all_modules.{module_index}.Conv_0.bias'''].data
						module_index += 1

		set_resnet_weights(new_model_architecture.mid_block.resnets[0]  ,				a_  ,				a_		)
		module_index += 1
		set_attention_weights(new_model_architecture.mid_block.attentions[0]  ,				a_  ,				a_		)
		module_index += 1
		set_resnet_weights(new_model_architecture.mid_block.resnets[1]  ,				a_  ,				a_		)
		module_index += 1

		for i, block in enumerate(new_model_architecture.up_blocks		):
				A				=hasattr(a_  ,				"attentions"		)
				for j in range(len(block.resnets		)		):
						set_resnet_weights(block.resnets[j]  ,				a_  ,				a_		)
						module_index += 1
				if has_attentions:
						set_attention_weights(
						    block.attentions[0]  ,				a_  ,				a_		)  # why can there only be a single attention layer for up?
						module_index += 1

				if hasattr(a_  ,				"resnet_up"		) and block.resnet_up is not None:
						A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
						A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
						module_index += 1
						A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
						A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
						module_index += 1
						set_resnet_weights(block.resnet_up  ,				a_  ,				a_		)
						module_index += 1

		A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
		A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
		module_index += 1
		A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
		A				=checkpoint[f'''all_modules.{module_index}.bias'''].data

		return new_model_architecture.state_dict()


if __name__ == "__main__":
	__a							  =						argparse.ArgumentParser()

	parser.add_argument(
	    """--checkpoint_path""",
	    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin""",
	    type=str,
	    required=False,
	    help="""Path to the checkpoint to convert.""",
	)

	parser.add_argument(
	    """--config_file""",
	    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/config.json""",
	    type=str,
	    required=False,
	    help="""The config json file corresponding to the architecture.""",
	)

	parser.add_argument(
	    """--dump_path""",
	    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt""",
	    type=str,
	    required=False,
	    help="""Path to the output model.""",
	)

	__a							  =						parser.parse_args()

	__a							  =						torch.load(args.checkpoint_path, map_location="""cpu""")

	with open(args.config_file) as f:
		__a							  =						json.loads(f.read())

	__a							  =						convert_ncsnpp_checkpoint(
	    checkpoint,
	    config,
	)

	if "sde" in config:
		del config["sde"]

	__a							  =						UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

	try:
		__a							  =						ScoreSdeVeScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

		__a							  =						ScoreSdeVePipeline(unet=model, scheduler=scheduler)
		pipe.save_pretrained(args.dump_path)
	except:  # noqa: E722
		model.save_pretrained(args.dump_path)




