
"""simple docstring"""






import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


lowerCamelCase__ :       Dict						     =			"true"





def __A						(   a_ :							Dict		,		a_ :							Optional[Any]=82		,		a_ :							List[str]=16     )->							Union[str, Any]:




		'''simple docstring'''






		set_seed(42     )
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= RegressionModel()
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= deepcopy(a_     )
		SCREAMING_SNAKE_CASE       :     int				= RegressionDataset(length=a_     )
		SCREAMING_SNAKE_CASE       :     str				= DataLoader(a_		,		batch_size=a_     )
		model.to(accelerator.device     )
		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Any				= accelerator.prepare(a_		,		a_     )
		return model, ddp_model, dataloader





def __A						(   a_ :							Accelerator		,		a_ :							Tuple=False     )->							Optional[Any]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[Any]				= AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''     )
		SCREAMING_SNAKE_CASE       :     int				= load_dataset('''glue'''		,		'''mrpc'''		,		split='''validation'''     )

		def tokenize_function(a_ :							List[Any]     ):
				SCREAMING_SNAKE_CASE       :     int				= tokenizer(examples['''sentence1''']		,		examples['''sentence2''']		,		truncation=a_		,		max_length=a_     )
				return outputs

		with accelerator.main_process_first():
				SCREAMING_SNAKE_CASE       :     int				= dataset.map(
				    a_		,		batched=a_		,		remove_columns=['''idx''', '''sentence1''', '''sentence2''']		,		)

		SCREAMING_SNAKE_CASE       :     Optional[int]				= tokenized_datasets.rename_column('''label'''		,		'''labels'''     )

		def collate_fn(a_ :							Dict     ):
				if use_longest:
						return tokenizer.pad(a_		,		padding='''longest'''		,		return_tensors='''pt'''     )
				return tokenizer.pad(a_		,		padding='''max_length'''		,		max_length=1_28		,		return_tensors='''pt'''     )

		return DataLoader(a_		,		shuffle=a_		,		collate_fn=a_		,		batch_size=16     )





def __A						(   a_ :							Tuple		,		a_ :							List[str]     )->							Optional[Any]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     List[str]				= Accelerator(dispatch_batches=a_		,		split_batches=a_     )
		SCREAMING_SNAKE_CASE       :     Dict				= get_dataloader(a_		,		not dispatch_batches     )
		SCREAMING_SNAKE_CASE       :     str				= AutoModelForSequenceClassification.from_pretrained(
		    '''hf-internal-testing/mrpc-bert-base-cased'''		,		return_dict=a_     )
		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[str]				= accelerator.prepare(a_		,		a_     )
		return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator





def __A						(   a_ :							Tuple		,		a_ :							Tuple		,		a_ :							List[str]     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     List[str]				= []
		for batch in dataloader:
				SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Optional[int]				= batch.values()
				with torch.no_grad():
						SCREAMING_SNAKE_CASE       :     int				= model(a_     )
						SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Optional[Any]				= accelerator.gather_for_metrics((logit, target)     )
						logits_and_targets.append((logit, target)     )
		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Optional[int]				= [], []
		for logit, targ in logits_and_targets:
				logits.append(a_     )
				targs.append(a_     )
		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Optional[Any]				= torch.cat(a_     ), torch.cat(a_     )
		return logits, targs





def __A						(   a_ :							Accelerator		,		a_ :							int=82		,		a_ :							List[Any]=False		,		a_ :							Union[str, Any]=False		,		a_ :							Any=16     )->							List[Any]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Tuple				= get_basic_setup(a_		,		a_		,		a_     )
		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Optional[Any]				= generate_predictions(a_		,		a_		,		a_     )
		assert (
		    len(a_     ) == num_samples
		), F"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(a_     )}"





def __A						(   a_ :							bool = False		,		a_ :							bool = False     )->							Dict:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     List[str]				= evaluate.load('''glue'''		,		'''mrpc'''     )
		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Tuple				= get_mrpc_setup(a_		,		a_     )
		# First do baseline
		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Optional[int]				= setup['''no''']
		model.to(a_     )
		model.eval()
		for batch in dataloader:
				batch.to(a_     )
				with torch.inference_mode():
						SCREAMING_SNAKE_CASE       :     Optional[Any]				= model(**a_     )
				SCREAMING_SNAKE_CASE       :     List[Any]				= outputs.logits.argmax(dim=-1     )
				metric.add_batch(predictions=a_		,		references=batch['''labels''']     )
		SCREAMING_SNAKE_CASE       :     Dict				= metric.compute()

		# Then do distributed
		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     int				= setup['''ddp''']
		model.eval()
		for batch in dataloader:
				with torch.inference_mode():
						SCREAMING_SNAKE_CASE       :     List[str]				= model(**a_     )
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= outputs.logits.argmax(dim=-1     )
				SCREAMING_SNAKE_CASE       :     Dict				= batch['''labels''']
				SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Tuple				= accelerator.gather_for_metrics((preds, references)     )
				metric.add_batch(predictions=a_		,		references=a_     )
		SCREAMING_SNAKE_CASE       :     int				= metric.compute()

		for key in "accuracy f1".split():
				assert math.isclose(
				    baseline[key]		,		distributed[key]     ), F"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"





def __A						(   )->							Any:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     List[str]				= Accelerator(split_batches=a_		,		dispatch_batches=a_     )
		if accelerator.is_local_main_process:
				datasets.utils.logging.set_verbosity_warning()
				transformers.utils.logging.set_verbosity_warning()
		else:
				datasets.utils.logging.set_verbosity_error()
				transformers.utils.logging.set_verbosity_error()
		# These are a bit slower so they should only be ran on the GPU or TPU
		if torch.cuda.is_available() or is_tpu_available():
				if accelerator.is_local_main_process:
						print('''**Testing gather_for_metrics**'''     )
				for split_batches in [True, False]:
						for dispatch_batches in [True, False]:
								if accelerator.is_local_main_process:
										print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"     )
								test_mrpc(a_		,		a_     )
								accelerator.state._reset_state()
		if accelerator.is_local_main_process:
				print('''**Test torch metrics**'''     )
		for split_batches in [True, False]:
				for dispatch_batches in [True, False]:
						SCREAMING_SNAKE_CASE       :     Optional[int]				= Accelerator(split_batches=a_		,		dispatch_batches=a_     )
						if accelerator.is_local_main_process:
								print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"     )
						test_torch_metrics(a_		,		99     )
						accelerator.state._reset_state()
		if accelerator.is_local_main_process:
				print('''**Test last batch is not dropped when perfectly divisible**'''     )
		SCREAMING_SNAKE_CASE       :     Any				= Accelerator()
		test_torch_metrics(a_		,		5_12     )
		accelerator.state._reset_state()





def __A						(   a_ :							List[str]     )->							List[Any]:




		'''simple docstring'''






		main()


if __name__ == "__main__":
					main()







"""simple docstring"""






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

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


lowerCamelCase__ :       Tuple						     =			logging.get_logger(__name__)

lowerCamelCase__ :       str						     =			{
    "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """blenderbot-small"""
	UpperCamelCase				=  ["""past_key_values"""]
	UpperCamelCase				=  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}





	def __init__(						self    :Any		, lowerCamelCase_    :Dict=5_02_65		, lowerCamelCase_    :str=5_12		, lowerCamelCase_    :Tuple=8		, lowerCamelCase_    :int=20_48		, lowerCamelCase_    :str=16		, lowerCamelCase_    :Optional[int]=8		, lowerCamelCase_    :str=20_48		, lowerCamelCase_    :Optional[Any]=16		, lowerCamelCase_    :Union[str, Any]=0.0		, lowerCamelCase_    :List[str]=0.0		, lowerCamelCase_    :Tuple=True		, lowerCamelCase_    :Tuple=True		, lowerCamelCase_    :int="gelu"		, lowerCamelCase_    :Tuple=5_12		, lowerCamelCase_    :Optional[int]=0.1		, lowerCamelCase_    :int=0.0		, lowerCamelCase_    :Tuple=0.0		, lowerCamelCase_    :Optional[int]=0.0_2		, lowerCamelCase_    :Union[str, Any]=1		, lowerCamelCase_    :Dict=False		, lowerCamelCase_    :Optional[int]=0		, lowerCamelCase_    :List[Any]=1		, lowerCamelCase_    :Any=2		, lowerCamelCase_    :Optional[Any]=2		, **lowerCamelCase_    :Dict		, )							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= vocab_size
			SCREAMING_SNAKE_CASE       :     List[str]				= max_position_embeddings
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= d_model
			SCREAMING_SNAKE_CASE       :     Dict				= encoder_ffn_dim
			SCREAMING_SNAKE_CASE       :     Tuple				= encoder_layers
			SCREAMING_SNAKE_CASE       :     Dict				= encoder_attention_heads
			SCREAMING_SNAKE_CASE       :     Any				= decoder_ffn_dim
			SCREAMING_SNAKE_CASE       :     str				= decoder_layers
			SCREAMING_SNAKE_CASE       :     str				= decoder_attention_heads
			SCREAMING_SNAKE_CASE       :     List[Any]				= dropout
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= attention_dropout
			SCREAMING_SNAKE_CASE       :     Any				= activation_dropout
			SCREAMING_SNAKE_CASE       :     List[str]				= activation_function
			SCREAMING_SNAKE_CASE       :     Optional[int]				= init_std
			SCREAMING_SNAKE_CASE       :     List[Any]				= encoder_layerdrop
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= decoder_layerdrop
			SCREAMING_SNAKE_CASE       :     List[Any]				= use_cache
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= encoder_layers
			SCREAMING_SNAKE_CASE       :     List[Any]				= 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_		, decoder_start_token_id=lowerCamelCase_		, forced_eos_token_id=lowerCamelCase_		, **lowerCamelCase_		, )





class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	@property
	def   __lowerCAmelCase (						self    :Any					)							->			Mapping[str, Mapping[int, str]]:







			'''simple docstring'''




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

					if self.use_past:
							SCREAMING_SNAKE_CASE       :     Union[str, Any]				= {0: '''batch'''}
							SCREAMING_SNAKE_CASE       :     List[Any]				= {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
					else:
							SCREAMING_SNAKE_CASE       :     Optional[Any]				= {0: '''batch''', 1: '''decoder_sequence'''}
							SCREAMING_SNAKE_CASE       :     Union[str, Any]				= {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.
					SCREAMING_SNAKE_CASE       :     Tuple				= OrderedDict(
					    [
					        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
					        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
					    ]					)
					if self.use_past:
							SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[Any]				= self.num_layers
							for i in range(lowerCamelCase_					):
									SCREAMING_SNAKE_CASE       :     int				= {0: '''batch''', 2: '''past_sequence + sequence'''}
									SCREAMING_SNAKE_CASE       :     List[str]				= {0: '''batch''', 2: '''past_sequence + sequence'''}
			else:
					SCREAMING_SNAKE_CASE       :     Any				= 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   __lowerCAmelCase (						self    :List[str]					)							->			Mapping[str, Mapping[int, str]]:







			'''simple docstring'''




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





	def   __lowerCAmelCase (						self    :int		, lowerCamelCase_    :PreTrainedTokenizer		, lowerCamelCase_    :int = -1		, lowerCamelCase_    :int = -1		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :Optional[TensorType] = None		, )							->			Mapping[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
			    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)

			# Generate decoder inputs
			SCREAMING_SNAKE_CASE       :     Optional[int]				= seq_length if not self.use_past else 1
			SCREAMING_SNAKE_CASE       :     Optional[int]				= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
			    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
			SCREAMING_SNAKE_CASE       :     str				= 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
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Any				= common_inputs['''input_ids'''].shape
					SCREAMING_SNAKE_CASE       :     str				= common_inputs['''decoder_input_ids'''].shape[1]
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Any				= self.num_attention_heads
					SCREAMING_SNAKE_CASE       :     str				= (
					    batch,
					    num_encoder_attention_heads,
					    encoder_seq_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= decoder_seq_length + 3
					SCREAMING_SNAKE_CASE       :     int				= (
					    batch,
					    num_decoder_attention_heads,
					    decoder_past_length,
					    self._config.hidden_size // num_decoder_attention_heads,
					)

					SCREAMING_SNAKE_CASE       :     List[Any]				= torch.cat(
					    [common_inputs['''decoder_attention_mask'''], torch.ones(lowerCamelCase_		, lowerCamelCase_					)]		, dim=1					)

					SCREAMING_SNAKE_CASE       :     Optional[int]				= []
					# If the number of encoder and decoder layers are present in the model configuration, both are considered
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Optional[int]				= self.num_layers
					SCREAMING_SNAKE_CASE       :     int				= min(lowerCamelCase_		, lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     Optional[int]				= max(lowerCamelCase_		, lowerCamelCase_					) - min_num_layers
					SCREAMING_SNAKE_CASE       :     Tuple				= '''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.
					SCREAMING_SNAKE_CASE       :     int				= 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   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :PreTrainedTokenizer		, lowerCamelCase_    :int = -1		, lowerCamelCase_    :int = -1		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :Optional[TensorType] = None		, )							->			Mapping[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= 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
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Tuple				= common_inputs['''input_ids'''].shape
					# Not using the same length for past_key_values
					SCREAMING_SNAKE_CASE       :     List[str]				= seqlen + 2
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Dict				= self.num_layers
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[Any]				= self.num_attention_heads
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= (
					    batch,
					    num_encoder_attention_heads,
					    past_key_values_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)

					SCREAMING_SNAKE_CASE       :     Tuple				= common_inputs['''attention_mask'''].dtype
					SCREAMING_SNAKE_CASE       :     Any				= torch.cat(
					    [common_inputs['''attention_mask'''], torch.ones(lowerCamelCase_		, lowerCamelCase_		, dtype=lowerCamelCase_					)]		, dim=1					)
					SCREAMING_SNAKE_CASE       :     Optional[int]				= [
					    (torch.zeros(lowerCamelCase_					), torch.zeros(lowerCamelCase_					)) for _ in range(lowerCamelCase_					)
					]
			return common_inputs





	def   __lowerCAmelCase (						self    :Union[str, Any]		, lowerCamelCase_    :PreTrainedTokenizer		, lowerCamelCase_    :int = -1		, lowerCamelCase_    :int = -1		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :Optional[TensorType] = None		, )							->			Mapping[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= 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
			SCREAMING_SNAKE_CASE       :     int				= tokenizer.num_special_tokens_to_add(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     int				= 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
			SCREAMING_SNAKE_CASE       :     Tuple				= [''' '''.join([tokenizer.unk_token]					) * seq_length] * batch_size
			SCREAMING_SNAKE_CASE       :     Any				= dict(tokenizer(lowerCamelCase_		, return_tensors=lowerCamelCase_					)					)
			return common_inputs





	def   __lowerCAmelCase (						self    :List[Any]		, lowerCamelCase_    :PreTrainedTokenizer		, lowerCamelCase_    :int = -1		, lowerCamelCase_    :int = -1		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :Optional[TensorType] = None		, )							->			Mapping[str, Any]:







			'''simple docstring'''




			if self.task in ["default", "seq2seq-lm"]:
					SCREAMING_SNAKE_CASE       :     Dict				= 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":
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self._generate_dummy_inputs_for_causal_lm(
					    lowerCamelCase_		, batch_size=lowerCamelCase_		, seq_length=lowerCamelCase_		, is_pair=lowerCamelCase_		, framework=lowerCamelCase_					)
			else:
					SCREAMING_SNAKE_CASE       :     List[Any]				= 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   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :int		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :Dict					)							->			List[Any]:







			'''simple docstring'''




			if self.task in ["default", "seq2seq-lm"]:
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= super()._flatten_past_key_values_(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)
			else:
					SCREAMING_SNAKE_CASE       :     Tuple				= super(lowerCamelCase_		, self					)._flatten_past_key_values_(
					    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)





