
"""simple docstring"""






import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCamelCase__ :       List[str]						     =			16
lowerCamelCase__ :       Union[str, Any]						     =			32





def __A						(   a_ :							Any		,		a_ :							str = 16     )->							Dict:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     List[str]				= AutoTokenizer.from_pretrained('''bert-base-cased'''     )
		SCREAMING_SNAKE_CASE       :     Tuple				= load_dataset('''glue'''		,		'''mrpc'''     )

		def tokenize_function(a_ :							str     ):
				# max_length=None => use the model max length (it's actually the default)
				SCREAMING_SNAKE_CASE       :     Tuple				= tokenizer(examples['''sentence1''']		,		examples['''sentence2''']		,		truncation=__UpperCamelCase		,		max_length=__UpperCamelCase     )
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		# starting with the main process first:
		with accelerator.main_process_first():
				SCREAMING_SNAKE_CASE       :     str				= datasets.map(
				    __UpperCamelCase		,		batched=__UpperCamelCase		,		remove_columns=['''idx''', '''sentence1''', '''sentence2''']		,		)

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		SCREAMING_SNAKE_CASE       :     Dict				= tokenized_datasets.rename_column('''label'''		,		'''labels'''     )

		def collate_fn(a_ :							List[str]     ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				SCREAMING_SNAKE_CASE       :     List[Any]				= 1_28 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						SCREAMING_SNAKE_CASE       :     str				= 16
				elif accelerator.mixed_precision != "no":
						SCREAMING_SNAKE_CASE       :     Any				= 8
				else:
						SCREAMING_SNAKE_CASE       :     int				= None

				return tokenizer.pad(
				    __UpperCamelCase		,		padding='''longest'''		,		max_length=__UpperCamelCase		,		pad_to_multiple_of=__UpperCamelCase		,		return_tensors='''pt'''		,		)

		# Instantiate dataloaders.
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= DataLoader(
		    tokenized_datasets['''train''']		,		shuffle=__UpperCamelCase		,		collate_fn=__UpperCamelCase		,		batch_size=__UpperCamelCase     )
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= DataLoader(
		    tokenized_datasets['''validation''']		,		shuffle=__UpperCamelCase		,		collate_fn=__UpperCamelCase		,		batch_size=__UpperCamelCase     )

		return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
					from accelerate.test_utils.training import mocked_dataloaders

					lowerCamelCase__ :       int						     =			mocked_dataloaders  # noqa: F811





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




		'''simple docstring'''






		if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,		__UpperCamelCase     ) == "1":
				SCREAMING_SNAKE_CASE       :     str				= 2
		# New Code #
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= int(args.gradient_accumulation_steps     )
		SCREAMING_SNAKE_CASE       :     int				= int(args.local_sgd_steps     )
		# Initialize accelerator
		SCREAMING_SNAKE_CASE       :     str				= Accelerator(
		    cpu=args.cpu		,		mixed_precision=args.mixed_precision		,		gradient_accumulation_steps=__UpperCamelCase     )
		if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
				raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''     )
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		SCREAMING_SNAKE_CASE       :     Any				= config['''lr''']
		SCREAMING_SNAKE_CASE       :     Optional[int]				= int(config['''num_epochs''']     )
		SCREAMING_SNAKE_CASE       :     Tuple				= int(config['''seed''']     )
		SCREAMING_SNAKE_CASE       :     Any				= int(config['''batch_size''']     )

		SCREAMING_SNAKE_CASE       :     Optional[Any]				= evaluate.load('''glue'''		,		'''mrpc'''     )

		set_seed(__UpperCamelCase     )
		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Dict				= get_dataloaders(__UpperCamelCase		,		__UpperCamelCase     )
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''		,		return_dict=__UpperCamelCase     )

		# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
		# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
		# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
		SCREAMING_SNAKE_CASE       :     str				= model.to(accelerator.device     )

		# Instantiate optimizer
		SCREAMING_SNAKE_CASE       :     Dict				= AdamW(params=model.parameters()		,		lr=__UpperCamelCase     )

		# Instantiate scheduler
		SCREAMING_SNAKE_CASE       :     List[Any]				= get_linear_schedule_with_warmup(
		    optimizer=__UpperCamelCase		,		num_warmup_steps=1_00		,		num_training_steps=(len(__UpperCamelCase     ) * num_epochs)		,		)

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.
		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= accelerator.prepare(
		    __UpperCamelCase		,		__UpperCamelCase		,		__UpperCamelCase		,		__UpperCamelCase		,		__UpperCamelCase     )

		# Now we train the model
		for epoch in range(__UpperCamelCase     ):
				model.train()
				with LocalSGD(
				    accelerator=__UpperCamelCase		,		model=__UpperCamelCase		,		local_sgd_steps=__UpperCamelCase		,		enabled=local_sgd_steps is not None     ) as local_sgd:
						for step, batch in enumerate(__UpperCamelCase     ):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device     )
								# New code #
								# We use the new `accumulate` context manager to perform gradient accumulation
								# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
								with accelerator.accumulate(__UpperCamelCase     ):
										SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model(**__UpperCamelCase     )
										SCREAMING_SNAKE_CASE       :     Any				= output.loss
										accelerator.backward(__UpperCamelCase     )
										optimizer.step()
										lr_scheduler.step()
										optimizer.zero_grad()
										# LocalSGD-specific line
										local_sgd.step()

				model.eval()
				for step, batch in enumerate(__UpperCamelCase     ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device     )
						with torch.no_grad():
								SCREAMING_SNAKE_CASE       :     str				= model(**__UpperCamelCase     )
						SCREAMING_SNAKE_CASE       :     str				= outputs.logits.argmax(dim=-1     )
						SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[str]				= accelerator.gather_for_metrics((predictions, batch['''labels'''])     )
						metric.add_batch(
						    predictions=__UpperCamelCase		,		references=__UpperCamelCase		,		)

				SCREAMING_SNAKE_CASE       :     List[Any]				= metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(F"epoch {epoch}:"		,		__UpperCamelCase     )





def __A						(   )->							List[Any]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= argparse.ArgumentParser(description='''Simple example of training script.'''     )
		parser.add_argument(
		    '''--mixed_precision'''		,		type=__UpperCamelCase		,		default=__UpperCamelCase		,		choices=['''no''', '''fp16''', '''bf16''', '''fp8''']		,		help='''Whether to use mixed precision. Choose'''
		    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
		    '''and an Nvidia Ampere GPU.'''		,		)
		# New Code #
		parser.add_argument(
		    '''--gradient_accumulation_steps'''		,		type=__UpperCamelCase		,		default=1		,		help='''The number of minibatches to be ran before gradients are accumulated.'''		,		)
		parser.add_argument(
		    '''--local_sgd_steps'''		,		type=__UpperCamelCase		,		default=8		,		help='''Number of local SGD steps or None to disable local SGD'''     )
		parser.add_argument('''--cpu'''		,		action='''store_true'''		,		help='''If passed, will train on the CPU.'''     )
		SCREAMING_SNAKE_CASE       :     Dict				= parser.parse_args()
		SCREAMING_SNAKE_CASE       :     List[str]				= {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(__UpperCamelCase		,		__UpperCamelCase     )


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_					)





