
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_UpperCAmelCase   :					List[Any]    =			logging.get_logger(__name__)

_UpperCAmelCase   :					Optional[Any]    =			{
    'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}


class 							lowercase   (						_UpperCamelCase  ):
		__lowercase			:			List[Any]      	=							"""mbart"""
		__lowercase			:			Union[str, Any]      	=							["""past_key_values"""]
		__lowercase			:			Union[str, Any]      	=							{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



		def __init__(					self							, A_=50_265							, A_=1_024							, A_=12							, A_=4_096							, A_=16							, A_=12							, A_=4_096							, A_=16							, A_=0.0							, A_=0.0							, A_=True							, A_=True							, A_="gelu"							, A_=1_024							, A_=0.1							, A_=0.0							, A_=0.0							, A_=0.02							, A_=0.0							, A_=False							, A_=1							, A_=0							, A_=2							, A_=2							, **A_							, )    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase            =	vocab_size
				UpperCamelCase            =	max_position_embeddings
				UpperCamelCase            =	d_model
				UpperCamelCase            =	encoder_ffn_dim
				UpperCamelCase            =	encoder_layers
				UpperCamelCase            =	encoder_attention_heads
				UpperCamelCase            =	decoder_ffn_dim
				UpperCamelCase            =	decoder_layers
				UpperCamelCase            =	decoder_attention_heads
				UpperCamelCase            =	dropout
				UpperCamelCase            =	attention_dropout
				UpperCamelCase            =	activation_dropout
				UpperCamelCase            =	activation_function
				UpperCamelCase            =	init_std
				UpperCamelCase            =	encoder_layerdrop
				UpperCamelCase            =	decoder_layerdrop
				UpperCamelCase            =	classifier_dropout
				UpperCamelCase            =	use_cache
				UpperCamelCase            =	encoder_layers
				UpperCamelCase            =	scale_embedding  # scale factor will be sqrt(d_model) if True
				super().__init__(
				    pad_token_id=__lowerCamelCase							, bos_token_id=__lowerCamelCase							, eos_token_id=__lowerCamelCase							, is_encoder_decoder=__lowerCamelCase							, forced_eos_token_id=__lowerCamelCase							, **__lowerCamelCase							, )






class 							lowercase   (						_UpperCamelCase  ):



		@property
		def __UpperCamelCase					(					self			)    ->   Mapping[str, Mapping[int, str]]:
				"""simple docstring"""
				if self.task in ["default", "seq2seq-lm"]:
						UpperCamelCase            =	OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
						    ]			)

						if self.use_past:
								UpperCamelCase            =	{0: 'batch'}
								UpperCamelCase            =	{0: 'batch', 1: 'past_decoder_sequence + sequence'}
						else:
								UpperCamelCase            =	{0: 'batch', 1: 'decoder_sequence'}
								UpperCamelCase            =	{0: 'batch', 1: 'decoder_sequence'}

						if self.use_past:
								self.fill_with_past_key_values_(__lowerCamelCase							, direction='inputs'			)
				elif self.task == "causal-lm":
						# TODO: figure this case out.
						UpperCamelCase            =	OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
						    ]			)
						if self.use_past:
								UpperCamelCase      ,    UpperCamelCase            =	self.num_layers
								for i in range(__lowerCamelCase			):
										UpperCamelCase            =	{0: 'batch', 2: 'past_sequence + sequence'}
										UpperCamelCase            =	{0: 'batch', 2: 'past_sequence + sequence'}
				else:
						UpperCamelCase            =	OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
						        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
						        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
						    ]			)

				return common_inputs



		@property
		def __UpperCamelCase					(					self			)    ->   Mapping[str, Mapping[int, str]]:
				"""simple docstring"""
				if self.task in ["default", "seq2seq-lm"]:
						UpperCamelCase            =	super().outputs
				else:
						UpperCamelCase            =	super(__lowerCamelCase							, self			).outputs
						if self.use_past:
								UpperCamelCase      ,    UpperCamelCase            =	self.num_layers
								for i in range(__lowerCamelCase			):
										UpperCamelCase            =	{0: 'batch', 2: 'past_sequence + sequence'}
										UpperCamelCase            =	{0: 'batch', 2: 'past_sequence + sequence'}
				return common_outputs



		def __UpperCamelCase					(					self							, A_							, A_ = -1							, A_ = -1							, A_ = False							, A_ = None							, )    ->   Mapping[str, Any]:
				"""simple docstring"""
				UpperCamelCase            =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    __lowerCamelCase							, __lowerCamelCase							, __lowerCamelCase							, __lowerCamelCase							, __lowerCamelCase			)

				# Generate decoder inputs
				UpperCamelCase            =	seq_length if not self.use_past else 1
				UpperCamelCase            =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    __lowerCamelCase							, __lowerCamelCase							, __lowerCamelCase							, __lowerCamelCase							, __lowerCamelCase			)
				UpperCamelCase            =	{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
				UpperCamelCase            =	dict(**__lowerCamelCase							, **__lowerCamelCase			)

				if self.use_past:
						if not is_torch_available():
								raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'			)
						else:
								import torch
						UpperCamelCase      ,    UpperCamelCase            =	common_inputs['input_ids'].shape
						UpperCamelCase            =	common_inputs['decoder_input_ids'].shape[1]
						UpperCamelCase      ,    UpperCamelCase            =	self.num_attention_heads
						UpperCamelCase            =	(
						    batch,
						    num_encoder_attention_heads,
						    encoder_seq_length,
						    self._config.hidden_size // num_encoder_attention_heads,
						)
						UpperCamelCase            =	decoder_seq_length + 3
						UpperCamelCase            =	(
						    batch,
						    num_decoder_attention_heads,
						    decoder_past_length,
						    self._config.hidden_size // num_decoder_attention_heads,
						)

						UpperCamelCase            =	torch.cat(
						    [common_inputs['decoder_attention_mask'], torch.ones(__lowerCamelCase							, __lowerCamelCase			)]							, dim=1			)

						UpperCamelCase            =	[]
						# If the number of encoder and decoder layers are present in the model configuration, both are considered
						UpperCamelCase      ,    UpperCamelCase            =	self.num_layers
						UpperCamelCase            =	min(__lowerCamelCase							, __lowerCamelCase			)
						UpperCamelCase            =	max(__lowerCamelCase							, __lowerCamelCase			) - min_num_layers
						UpperCamelCase            =	'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

						for _ in range(__lowerCamelCase			):
								common_inputs["past_key_values"].append(
								    (
								        torch.zeros(__lowerCamelCase			),
								        torch.zeros(__lowerCamelCase			),
								        torch.zeros(__lowerCamelCase			),
								        torch.zeros(__lowerCamelCase			),
								    )			)
						# TODO: test this.
						UpperCamelCase            =	encoder_shape if remaining_side_name == 'encoder' else decoder_shape
						for _ in range(__lowerCamelCase							, __lowerCamelCase			):
								common_inputs["past_key_values"].append((torch.zeros(__lowerCamelCase			), torch.zeros(__lowerCamelCase			))			)
				return common_inputs



		def __UpperCamelCase					(					self							, A_							, A_ = -1							, A_ = -1							, A_ = False							, A_ = None							, )    ->   Mapping[str, Any]:
				"""simple docstring"""
				UpperCamelCase            =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    __lowerCamelCase							, __lowerCamelCase							, __lowerCamelCase							, __lowerCamelCase							, __lowerCamelCase			)

				if self.use_past:
						if not is_torch_available():
								raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'			)
						else:
								import torch
						UpperCamelCase      ,    UpperCamelCase            =	common_inputs['input_ids'].shape
						# Not using the same length for past_key_values
						UpperCamelCase            =	seqlen + 2
						UpperCamelCase      ,    UpperCamelCase            =	self.num_layers
						UpperCamelCase      ,    UpperCamelCase            =	self.num_attention_heads
						UpperCamelCase            =	(
						    batch,
						    num_encoder_attention_heads,
						    past_key_values_length,
						    self._config.hidden_size // num_encoder_attention_heads,
						)

						UpperCamelCase            =	common_inputs['attention_mask'].dtype
						UpperCamelCase            =	torch.cat(
						    [common_inputs['attention_mask'], torch.ones(__lowerCamelCase							, __lowerCamelCase							, dtype=__lowerCamelCase			)]							, dim=1			)
						UpperCamelCase            =	[
						    (torch.zeros(__lowerCamelCase			), torch.zeros(__lowerCamelCase			)) for _ in range(__lowerCamelCase			)
						]
				return common_inputs



		def __UpperCamelCase					(					self							, A_							, A_ = -1							, A_ = -1							, A_ = False							, A_ = None							, )    ->   Mapping[str, Any]:
				"""simple docstring"""
				# Copied from OnnxConfig.generate_dummy_inputs
				# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
				UpperCamelCase            =	compute_effective_axis_dimension(
				    __lowerCamelCase							, fixed_dimension=OnnxConfig.default_fixed_batch							, num_token_to_add=0			)

				# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
				UpperCamelCase            =	tokenizer.num_special_tokens_to_add(__lowerCamelCase			)
				UpperCamelCase            =	compute_effective_axis_dimension(
				    __lowerCamelCase							, fixed_dimension=OnnxConfig.default_fixed_sequence							, num_token_to_add=__lowerCamelCase			)

				# Generate dummy inputs according to compute batch and sequence
				UpperCamelCase            =	[' '.join([tokenizer.unk_token]			) * seq_length] * batch_size
				UpperCamelCase            =	dict(tokenizer(__lowerCamelCase							, return_tensors=__lowerCamelCase			)			)
				return common_inputs



		def __UpperCamelCase					(					self							, A_							, A_ = -1							, A_ = -1							, A_ = False							, A_ = None							, )    ->   Mapping[str, Any]:
				"""simple docstring"""
				if self.task in ["default", "seq2seq-lm"]:
						UpperCamelCase            =	self._generate_dummy_inputs_for_default_and_seqaseq_lm(
						    __lowerCamelCase							, batch_size=__lowerCamelCase							, seq_length=__lowerCamelCase							, is_pair=__lowerCamelCase							, framework=__lowerCamelCase			)

				elif self.task == "causal-lm":
						UpperCamelCase            =	self._generate_dummy_inputs_for_causal_lm(
						    __lowerCamelCase							, batch_size=__lowerCamelCase							, seq_length=__lowerCamelCase							, is_pair=__lowerCamelCase							, framework=__lowerCamelCase			)
				else:
						UpperCamelCase            =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    __lowerCamelCase							, batch_size=__lowerCamelCase							, seq_length=__lowerCamelCase							, is_pair=__lowerCamelCase							, framework=__lowerCamelCase			)

				return common_inputs



		def __UpperCamelCase					(					self							, A_							, A_							, A_							, A_			)    ->   List[str]:
				"""simple docstring"""
				if self.task in ["default", "seq2seq-lm"]:
						UpperCamelCase            =	super()._flatten_past_key_values_(__lowerCamelCase							, __lowerCamelCase							, __lowerCamelCase							, __lowerCamelCase			)
				else:
						UpperCamelCase            =	super(__lowerCamelCase							, self			)._flatten_past_key_values_(
						    __lowerCamelCase							, __lowerCamelCase							, __lowerCamelCase							, __lowerCamelCase			)






import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()




class        snake_case__				(unittest.TestCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Optional[int]									=   """hf-internal-testing/tiny-stable-diffusion-pipe"""
				SCREAMING_SNAKE_CASE_			:				str									=   1
				SCREAMING_SNAKE_CASE_			:				Dict									=   77
				SCREAMING_SNAKE_CASE_			:				List[str]									=   32
				SCREAMING_SNAKE_CASE_			:				List[str]									=   4
				SCREAMING_SNAKE_CASE_			:				Union[str, Any]									=   64
				SCREAMING_SNAKE_CASE_			:				int									=   torch.manual_seed(0  )





				def 				__UpperCAmelCase				( self							:	List[Any]   , __lowerCamelCase							:	List[Any]=0.9_999				)   ->	Tuple:
							a       =     UNetaDConditionModel.from_pretrained(self.model_id   , subfolder="unet"				)
							a       =     unet.to(__lowerCamelCase				)
							a       =     EMAModel(unet.parameters()   , decay=__lowerCamelCase   , model_cls=__lowerCamelCase   , model_config=unet.config				)
							return unet, ema_unet





				def 				__UpperCAmelCase				( self							:	Tuple				)   ->	Any:
							a       =     torch.randn(
							    self.batch_size   , self.num_in_channels   , self.latent_height   , self.latent_width   , generator=self.generator				).to(__lowerCamelCase				)
							a       =     torch.randint(0   , 10_00   , size=(self.batch_size,)   , generator=self.generator				).to(__lowerCamelCase				)
							a       =     torch.randn(
							    self.batch_size   , self.prompt_length   , self.text_encoder_hidden_dim   , generator=self.generator				).to(__lowerCamelCase				)
							return noisy_latents, timesteps, encoder_hidden_states





				def 				__UpperCAmelCase				( self							:	Optional[int]   , __lowerCamelCase							:	Any				)   ->	Optional[int]:
							a       =     {}
							for k, param in unet.state_dict().items():
										a       =     torch.randn_like(__lowerCamelCase				) + (param * torch.randn_like(__lowerCamelCase				))
										updated_state_dict.update({k: updated_param}				)
							unet.load_state_dict(__lowerCamelCase				)
							return unet





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	Union[str, Any]:
							a       ,						a       =     self.get_models()
							# Take the first (hypothetical) EMA step.
							ema_unet.step(unet.parameters()				)
							assert ema_unet.optimization_step == 1

							# Take two more.
							for _ in range(2				):
										ema_unet.step(unet.parameters()				)
							assert ema_unet.optimization_step == 3





				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	Optional[int]:
							a       ,						a       =     self.get_models()
							# Since the `unet` is not being updated (i.e., backprop'd)
							# there won't be any difference between the `params` of `unet`
							# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
							ema_unet.step(unet.parameters()				)
							a       =     list(unet.parameters()				)
							for s_param, param in zip(ema_unet.shadow_params   , __lowerCamelCase				):
										assert torch.allclose(__lowerCamelCase   , __lowerCamelCase				)

							# The above holds true even if we call `ema.step()` multiple times since
							# `unet` params are still not being updated.
							for _ in range(4				):
										ema_unet.step(unet.parameters()				)
							for s_param, param in zip(ema_unet.shadow_params   , __lowerCamelCase				):
										assert torch.allclose(__lowerCamelCase   , __lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Any				)   ->	Dict:
							a       ,						a       =     self.get_models()
							# Here we simulate the parameter updates for `unet`. Since there might
							# be some parameters which are initialized to zero we take extra care to
							# initialize their values to something non-zero before the multiplication.
							a       =     self.simulate_backprop(__lowerCamelCase				)

							# Take the EMA step.
							ema_unet.step(unet_pseudo_updated_step_one.parameters()				)

							# Now the EMA'd parameters won't be equal to the original model parameters.
							a       =     list(unet_pseudo_updated_step_one.parameters()				)
							for s_param, param in zip(ema_unet.shadow_params   , __lowerCamelCase				):
										assert ~torch.allclose(__lowerCamelCase   , __lowerCamelCase				)

							# Ensure this is the case when we take multiple EMA steps.
							for _ in range(4				):
										ema_unet.step(unet.parameters()				)
							for s_param, param in zip(ema_unet.shadow_params   , __lowerCamelCase				):
										assert ~torch.allclose(__lowerCamelCase   , __lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	Any:
							# If we call EMA step after a backpropagation consecutively for two times,
							# the shadow params from those two steps should be different.
							a       ,						a       =     self.get_models()

							# First backprop + EMA
							a       =     self.simulate_backprop(__lowerCamelCase				)
							ema_unet.step(unet_step_one.parameters()				)
							a       =     ema_unet.shadow_params

							# Second backprop + EMA
							a       =     self.simulate_backprop(__lowerCamelCase				)
							ema_unet.step(unet_step_two.parameters()				)
							a       =     ema_unet.shadow_params

							for step_one, step_two in zip(__lowerCamelCase   , __lowerCamelCase				):
										assert ~torch.allclose(__lowerCamelCase   , __lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Any				)   ->	List[Any]:
							# If there's no decay even if there are backprops, EMA steps
							# won't take any effect i.e., the shadow params would remain the
							# same.
							a       ,						a       =     self.get_models(decay=0.0				)
							a       =     self.simulate_backprop(__lowerCamelCase				)
							ema_unet.step(unet_step_one.parameters()				)
							a       =     ema_unet.shadow_params

							a       =     self.simulate_backprop(__lowerCamelCase				)
							ema_unet.step(unet_step_two.parameters()				)
							a       =     ema_unet.shadow_params

							for step_one, step_two in zip(__lowerCamelCase   , __lowerCamelCase				):
										assert torch.allclose(__lowerCamelCase   , __lowerCamelCase				)





				@skip_mps
				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	Dict:
							a       ,						a       =     self.get_models()
							a       ,						a       ,						a       =     self.get_dummy_inputs()

							with tempfile.TemporaryDirectory() as tmpdir:
										ema_unet.save_pretrained(__lowerCamelCase				)
										a       =     UNetaDConditionModel.from_pretrained(__lowerCamelCase   , model_cls=__lowerCamelCase				)
										a       =     loaded_unet.to(unet.device				)

							# Since no EMA step has been performed the outputs should match.
							a       =     unet(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				).sample
							a       =     loaded_unet(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				).sample

							assert torch.allclose(__lowerCamelCase   , __lowerCamelCase   , atol=1e-4				)






