
import warnings
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


_lowerCamelCase       :				Optional[Any]       =			logging.get_logger(__name__)

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




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		Dict       =					"bart"
	_UpperCAmelCase       :		int       =					["past_key_values"]
	_UpperCAmelCase       :		Union[str, Any]       =					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



	def __init__(  self	:    List[Any]			,  lowercase	:    Optional[int]=50_265			,  lowercase	:    List[Any]=1_024			,  lowercase	:    Tuple=12			,  lowercase	:    Optional[Any]=4_096			,  lowercase	:    str=16			,  lowercase	:    Any=12			,  lowercase	:    int=4_096			,  lowercase	:    List[str]=16			,  lowercase	:    Any=0.0			,  lowercase	:    Any=0.0			,  lowercase	:    Dict="gelu"			,  lowercase	:    str=1_024			,  lowercase	:    Optional[int]=0.1			,  lowercase	:    List[str]=0.0			,  lowercase	:    List[str]=0.0			,  lowercase	:    Tuple=0.02			,  lowercase	:    Optional[Any]=0.0			,  lowercase	:    Any=False			,  lowercase	:    List[Any]=True			,  lowercase	:    Optional[Any]=3			,  lowercase	:    List[Any]=1			,  lowercase	:    List[str]=0			,  lowercase	:    Dict=2			,  lowercase	:    List[str]=True			,  lowercase	:    List[str]=2			,  lowercase	:    Union[str, Any]=2			,  **lowercase	:    int			,  ):


				'''simple docstring'''


				_snake_case		=			vocab_size
				_snake_case		=			max_position_embeddings
				_snake_case		=			d_model
				_snake_case		=			encoder_ffn_dim
				_snake_case		=			encoder_layers
				_snake_case		=			encoder_attention_heads
				_snake_case		=			decoder_ffn_dim
				_snake_case		=			decoder_layers
				_snake_case		=			decoder_attention_heads
				_snake_case		=			dropout
				_snake_case		=			attention_dropout
				_snake_case		=			activation_dropout
				_snake_case		=			activation_function
				_snake_case		=			init_std
				_snake_case		=			encoder_layerdrop
				_snake_case		=			decoder_layerdrop
				_snake_case		=			classifier_dropout
				_snake_case		=			use_cache
				_snake_case		=			encoder_layers
				_snake_case		=			scale_embedding  # scale factor will be sqrt(d_model) if True

				super().__init__(
				    num_labels=lowercase			,  pad_token_id=lowercase			,  bos_token_id=lowercase			,  eos_token_id=lowercase			,  is_encoder_decoder=lowercase			,  decoder_start_token_id=lowercase			,  forced_eos_token_id=lowercase			,  **lowercase			,  )

				# ensure backward compatibility for BART CNN models
				if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'			,  lowercase	):
							_snake_case		=			self.bos_token_id
							warnings.warn(
							    f'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
							    'The config can simply be saved and uploaded again to be fixed.'	)




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	@property
	def  A							(  self	:    int	):


				'''simple docstring'''


				if self.task in ["default", "seq2seq-lm"]:
							_snake_case		=			OrderedDict(
							    [
							        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
							        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
							    ]	)

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

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


				'''simple docstring'''


				if self.task in ["default", "seq2seq-lm"]:
							_snake_case		=			super().outputs
				else:
							_snake_case		=			super(lowercase			,  self	).outputs
							if self.use_past:
										_snake_case	,					_snake_case		=			self.num_layers
										for i in range(lowercase	):
													_snake_case		=			{0: 'batch', 2: 'past_sequence + sequence'}
													_snake_case		=			{0: 'batch', 2: 'past_sequence + sequence'}
				return common_outputs



	def  A							(  self	:    Optional[int]			,  lowercase	:    PreTrainedTokenizer			,  lowercase	:    int = -1			,  lowercase	:    int = -1			,  lowercase	:    bool = False			,  lowercase	:    Optional[TensorType] = None			,  ):


				'''simple docstring'''


				_snake_case		=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase	)

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

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

							_snake_case		=			torch.cat(
							    [common_inputs['decoder_attention_mask'], torch.ones(lowercase			,  lowercase	)]			,  dim=1	)

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

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



	def  A							(  self	:    List[str]			,  lowercase	:    PreTrainedTokenizer			,  lowercase	:    int = -1			,  lowercase	:    int = -1			,  lowercase	:    bool = False			,  lowercase	:    Optional[TensorType] = None			,  ):


				'''simple docstring'''


				_snake_case		=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase	)

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

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



	def  A							(  self	:    List[str]			,  lowercase	:    PreTrainedTokenizer			,  lowercase	:    int = -1			,  lowercase	:    int = -1			,  lowercase	:    bool = False			,  lowercase	:    Optional[TensorType] = None			,  ):


				'''simple docstring'''


				_snake_case		=			compute_effective_axis_dimension(
				    lowercase			,  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
				_snake_case		=			tokenizer.num_special_tokens_to_add(lowercase	)
				_snake_case		=			compute_effective_axis_dimension(
				    lowercase			,  fixed_dimension=OnnxConfig.default_fixed_sequence			,  num_token_to_add=lowercase	)

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



	def  A							(  self	:    List[Any]			,  lowercase	:    PreTrainedTokenizer			,  lowercase	:    int = -1			,  lowercase	:    int = -1			,  lowercase	:    bool = False			,  lowercase	:    Optional[TensorType] = None			,  ):


				'''simple docstring'''


				if self.task in ["default", "seq2seq-lm"]:
							_snake_case		=			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
							    lowercase			,  batch_size=lowercase			,  seq_length=lowercase			,  is_pair=lowercase			,  framework=lowercase	)

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

				return common_inputs





	def  A							(  self	:    Tuple			,  lowercase	:    Union[str, Any]			,  lowercase	:    Tuple			,  lowercase	:    int			,  lowercase	:    int	):


				'''simple docstring'''


				if self.task in ["default", "seq2seq-lm"]:
							_snake_case		=			super()._flatten_past_key_values_(lowercase			,  lowercase			,  lowercase			,  lowercase	)
				else:
							_snake_case		=			super(lowercase			,  self	)._flatten_past_key_values_(
							    lowercase			,  lowercase			,  lowercase			,  lowercase	)

import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_lowerCamelCase       :				int       =			False




class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    Union[str, Any]			,  lowercase	:    Optional[int]=32	):


				'''simple docstring'''


				set_seed(0	)
				_snake_case		=			UNetaDModel(sample_size=lowercase			,  in_channels=3			,  out_channels=3	)
				_snake_case		=			torch.optim.SGD(model.parameters()			,  lr=0.0001	)
				return model, optimizer





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


				'''simple docstring'''


				_snake_case		=			'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				_snake_case		=			DDPMScheduler(
				    num_train_timesteps=1_000			,  beta_start=0.0001			,  beta_end=0.02			,  beta_schedule='linear'			,  clip_sample=lowercase			,  )
				_snake_case		=			DDIMScheduler(
				    num_train_timesteps=1_000			,  beta_start=0.0001			,  beta_end=0.02			,  beta_schedule='linear'			,  clip_sample=lowercase			,  )

				assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

				# shared batches for DDPM and DDIM
				set_seed(0	)
				_snake_case		=			[torch.randn((4, 3, 32, 32)	).clip(-1			,  1	).to(lowercase	) for _ in range(4	)]
				_snake_case		=			[torch.randn((4, 3, 32, 32)	).to(lowercase	) for _ in range(4	)]
				_snake_case		=			[torch.randint(0			,  1_000			,  (4,)	).long().to(lowercase	) for _ in range(4	)]

				# train with a DDPM scheduler
				_snake_case	,					_snake_case		=			self.get_model_optimizer(resolution=32	)
				model.train().to(lowercase	)
				for i in range(4	):
							optimizer.zero_grad()
							_snake_case		=			ddpm_scheduler.add_noise(clean_images[i]			,  noise[i]			,  timesteps[i]	)
							_snake_case		=			model(lowercase			,  timesteps[i]	).sample
							_snake_case		=			torch.nn.functional.mse_loss(lowercase			,  noise[i]	)
							loss.backward()
							optimizer.step()
				del model, optimizer

				# recreate the model and optimizer, and retry with DDIM
				_snake_case	,					_snake_case		=			self.get_model_optimizer(resolution=32	)
				model.train().to(lowercase	)
				for i in range(4	):
							optimizer.zero_grad()
							_snake_case		=			ddim_scheduler.add_noise(clean_images[i]			,  noise[i]			,  timesteps[i]	)
							_snake_case		=			model(lowercase			,  timesteps[i]	).sample
							_snake_case		=			torch.nn.functional.mse_loss(lowercase			,  noise[i]	)
							loss.backward()
							optimizer.step()
				del model, optimizer

				self.assertTrue(torch.allclose(lowercase			,  lowercase			,  atol=1E-5	)	)
				self.assertTrue(torch.allclose(lowercase			,  lowercase			,  atol=1E-5	)	)