



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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

__SCREAMING_SNAKE_CASE     =	16
__SCREAMING_SNAKE_CASE     =	32




def 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase = 16     ):
			A				:       Optional[int]														=    AutoTokenizer.from_pretrained("bert-base-cased"     )
			A				:       Union[str, Any]														=    load_dataset("glue"			,     "mrpc"     )

			def tokenize_function(_lowerCamelCase     ):
						# max_length=None => use the model max length (it's actually the default)
						A				:       List[Any]														=    tokenizer(examples["sentence1"]			,     examples["sentence2"]			,     truncation=_lowerCamelCase			,     max_length=_lowerCamelCase     )
						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():
						A				:       Any														=    datasets.map(
						    _lowerCamelCase			,     batched=_lowerCamelCase			,     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
			A				:       Any														=    tokenized_datasets.rename_column("label"			,     "labels"     )

			def collate_fn(_lowerCamelCase     ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						A				:       Any														=    128 if accelerator.distributed_type == DistributedType.TPU else None
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									A				:       int														=    16
						elif accelerator.mixed_precision != "no":
									A				:       Optional[int]														=    8
						else:
									A				:       str														=    None

						return tokenizer.pad(
						    _lowerCamelCase			,     padding="longest"			,     max_length=_lowerCamelCase			,     pad_to_multiple_of=_lowerCamelCase			,     return_tensors="pt"			,     )

			# Instantiate dataloaders.
			A				:       Union[str, Any]														=    DataLoader(
			    tokenized_datasets["train"]			,     shuffle=_lowerCamelCase			,     collate_fn=_lowerCamelCase			,     batch_size=_lowerCamelCase     )
			A				:       int														=    DataLoader(
			    tokenized_datasets["validation"]			,     shuffle=_lowerCamelCase			,     collate_fn=_lowerCamelCase			,     batch_size=_lowerCamelCase     )

			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

							__SCREAMING_SNAKE_CASE     =	mocked_dataloaders  # noqa: F811




def 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase     ):
			# For testing only
			if os.environ.get("TESTING_MOCKED_DATALOADERS"			,     _lowerCamelCase     ) == "1":
						A				:       Optional[int]														=    2
			# Initialize accelerator
			A				:       List[Any]														=    Accelerator(cpu=args.cpu			,     mixed_precision=args.mixed_precision     )
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			A				:       Any														=    config["lr"]
			A				:       Union[str, Any]														=    int(config["num_epochs"]     )
			A				:       int														=    int(config["seed"]     )
			A				:       Optional[Any]														=    int(config["batch_size"]     )

			# New Code #
			# Parse out whether we are saving every epoch or after a certain number of batches
			if hasattr(args.checkpointing_steps			,     "isdigit"     ):
						if args.checkpointing_steps == "epoch":
									A				:       Any														=    args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									A				:       Any														=    int(args.checkpointing_steps     )
						else:
									raise ValueError(
									    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""     )
			else:
						A				:       int														=    None

			set_seed(_lowerCamelCase     )

			A							,			A				:       Optional[Any]														=    get_dataloaders(_lowerCamelCase			,     _lowerCamelCase     )
			A				:       int														=    evaluate.load("glue"			,     "mrpc"     )

			# If the batch size is too big we use gradient accumulation
			A				:       List[str]														=    1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						A				:       Union[str, Any]														=    batch_size // MAX_GPU_BATCH_SIZE
						A				:       Optional[int]														=    MAX_GPU_BATCH_SIZE

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			A				:       Tuple														=    AutoModelForSequenceClassification.from_pretrained("bert-base-cased"			,     return_dict=_lowerCamelCase     )

			# 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).
			A				:       int														=    model.to(accelerator.device     )

			# Instantiate optimizer
			A				:       Any														=    AdamW(params=model.parameters()			,     lr=_lowerCamelCase     )

			# Instantiate scheduler
			A				:       Optional[Any]														=    get_linear_schedule_with_warmup(
			    optimizer=_lowerCamelCase			,     num_warmup_steps=100			,     num_training_steps=(len(_lowerCamelCase     ) * num_epochs) // gradient_accumulation_steps			,     )

			# 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.
			A							,			A							,			A							,			A							,			A				:       List[str]														=    accelerator.prepare(
			    _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase     )

			# New Code #
			# We need to keep track of how many total steps we have iterated over
			A				:       str														=    0
			# We also need to keep track of the stating epoch so files are named properly
			A				:       Optional[int]														=    0

			# We need to load the checkpoint back in before training here with `load_state`
			# The total number of epochs is adjusted based on where the state is being loaded from,
			# as we assume continuation of the same training script
			if args.resume_from_checkpoint:
						if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
									accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}"""     )
									accelerator.load_state(args.resume_from_checkpoint     )
									A				:       str														=    os.path.basename(args.resume_from_checkpoint     )
						else:
									# Get the most recent checkpoint
									A				:       Optional[Any]														=    [f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
									dirs.sort(key=os.path.getctime     )
									A				:       str														=    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract `epoch_{i}` or `step_{i}`
						A				:       Union[str, Any]														=    os.path.splitext(_lowerCamelCase     )[0]

						if "epoch" in training_difference:
									A				:       Dict														=    int(training_difference.replace("epoch_"			,     ""     )     ) + 1
									A				:       List[Any]														=    None
						else:
									A				:       Dict														=    int(training_difference.replace("step_"			,     ""     )     )
									A				:       Optional[int]														=    resume_step // len(_lowerCamelCase     )
									resume_step -= starting_epoch * len(_lowerCamelCase     )

    # Now we train the model
			for epoch in range(_lowerCamelCase			,     _lowerCamelCase     ):
						model.train()
						# New Code #
						if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
									# We need to skip steps until we reach the resumed step
									A				:       Dict														=    accelerator.skip_first_batches(_lowerCamelCase			,     _lowerCamelCase     )
									overall_step += resume_step
						else:
									# After the first iteration though, we need to go back to the original dataloader
									A				:       str														=    train_dataloader
						for step, batch in enumerate(_lowerCamelCase     ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device     )
									A				:       Optional[Any]														=    model(**_lowerCamelCase     )
									A				:       Optional[int]														=    outputs.loss
									A				:       Optional[Any]														=    loss / gradient_accumulation_steps
									accelerator.backward(_lowerCamelCase     )
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()
									# New Code #
									overall_step += 1

									# New Code #
									# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
									# These are saved to folders named `step_{overall_step}`
									# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
									# If mixed precision was used, will also save a "scalar.bin" file
									if isinstance(_lowerCamelCase			,     _lowerCamelCase     ):
												A				:       Tuple														=    f"""step_{overall_step}"""
												if overall_step % checkpointing_steps == 0:
															if args.output_dir is not None:
																		A				:       str														=    os.path.join(args.output_dir			,     _lowerCamelCase     )
															accelerator.save_state(_lowerCamelCase     )

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

						A				:       int														=    metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f"""epoch {epoch}:"""			,     _lowerCamelCase     )

						# New Code #
						# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
						# These are saved to folders named `epoch_{epoch}`
						# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
						# If mixed precision was used, will also save a "scalar.bin" file
						if checkpointing_steps == "epoch":
									A				:       Dict														=    f"""epoch_{epoch}"""
									if args.output_dir is not None:
												A				:       str														=    os.path.join(args.output_dir			,     _lowerCamelCase     )
									accelerator.save_state(_lowerCamelCase     )




def 						UpperCAmelCase (    ):
			A				:       Optional[Any]														=    argparse.ArgumentParser(description="Simple example of training script."     )
			parser.add_argument(
			    "--mixed_precision"			,     type=_lowerCamelCase			,     default=_lowerCamelCase			,     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."			,     )
			parser.add_argument("--cpu"			,     action="store_true"			,     help="If passed, will train on the CPU."     )
			parser.add_argument(
			    "--checkpointing_steps"			,     type=_lowerCamelCase			,     default=_lowerCamelCase			,     help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."			,     )
			parser.add_argument(
			    "--output_dir"			,     type=_lowerCamelCase			,     default="."			,     help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."			,     )
			parser.add_argument(
			    "--resume_from_checkpoint"			,     type=_lowerCamelCase			,     default=_lowerCamelCase			,     help="If the training should continue from a checkpoint folder."			,     )
			A				:       Optional[Any]														=    parser.parse_args()
			A				:       Tuple														=    {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
			training_function(_lowerCamelCase			,     _lowerCamelCase     )


if __name__ == "__main__":
							main()




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


__SCREAMING_SNAKE_CASE     =	logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     =	{
    """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 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''
						a__ =	"mbart"
						a__ =	["past_key_values"]
						a__ =	{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







						def __init__(						self   :      int						,						__lowerCamelCase   :      Optional[int]=5_02_65						,						__lowerCamelCase   :      str=10_24						,						__lowerCamelCase   :      str=12						,						__lowerCamelCase   :      Dict=40_96						,						__lowerCamelCase   :      str=16						,						__lowerCamelCase   :      int=12						,						__lowerCamelCase   :      Optional[Any]=40_96						,						__lowerCamelCase   :      Any=16						,						__lowerCamelCase   :      Optional[Any]=0.0						,						__lowerCamelCase   :      List[Any]=0.0						,						__lowerCamelCase   :      Dict=True						,						__lowerCamelCase   :      List[str]=True						,						__lowerCamelCase   :      Optional[int]="gelu"						,						__lowerCamelCase   :      List[Any]=10_24						,						__lowerCamelCase   :      Dict=0.1						,						__lowerCamelCase   :      Tuple=0.0						,						__lowerCamelCase   :      List[str]=0.0						,						__lowerCamelCase   :      Union[str, Any]=0.02						,						__lowerCamelCase   :      List[Any]=0.0						,						__lowerCamelCase   :      List[Any]=False						,						__lowerCamelCase   :      Optional[Any]=1						,						__lowerCamelCase   :      str=0						,						__lowerCamelCase   :      Optional[Any]=2						,						__lowerCamelCase   :      Dict=2						,						**__lowerCamelCase   :      int						,						)   ->					Union[str, Any]:
									A				:       Union[str, Any]														=    vocab_size
									A				:       List[str]														=    max_position_embeddings
									A				:       Optional[int]														=    d_model
									A				:       Any														=    encoder_ffn_dim
									A				:       Union[str, Any]														=    encoder_layers
									A				:       int														=    encoder_attention_heads
									A				:       Union[str, Any]														=    decoder_ffn_dim
									A				:       Any														=    decoder_layers
									A				:       Tuple														=    decoder_attention_heads
									A				:       Any														=    dropout
									A				:       Union[str, Any]														=    attention_dropout
									A				:       str														=    activation_dropout
									A				:       Dict														=    activation_function
									A				:       Optional[int]														=    init_std
									A				:       Optional[Any]														=    encoder_layerdrop
									A				:       Union[str, Any]														=    decoder_layerdrop
									A				:       Tuple														=    classifier_dropout
									A				:       Union[str, Any]														=    use_cache
									A				:       List[str]														=    encoder_layers
									A				:       List[str]														=    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 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						@property
						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					Mapping[str, Mapping[int, str]]:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       List[str]														=    OrderedDict(
												    [
												        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
												        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
												    ]			)

												if self.use_past:
															A				:       str														=    {0: "batch"}
															A				:       Optional[int]														=    {0: "batch", 1: "past_decoder_sequence + sequence"}
												else:
															A				:       Optional[int]														=    {0: "batch", 1: "decoder_sequence"}
															A				:       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.
												A				:       Dict														=    OrderedDict(
												    [
												        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
												        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
												    ]			)
												if self.use_past:
															A							,			A				:       Union[str, Any]														=    self.num_layers
															for i in range(__lowerCamelCase			):
																		A				:       List[str]														=    {0: "batch", 2: "past_sequence + sequence"}
																		A				:       int														=    {0: "batch", 2: "past_sequence + sequence"}
									else:
												A				:       Optional[int]														=    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       SCREAMING_SNAKE_CASE__		(						self   :      int			)   ->					Mapping[str, Mapping[int, str]]:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       str														=    super().outputs
									else:
												A				:       int														=    super(__lowerCamelCase						,						self			).outputs
												if self.use_past:
															A							,			A				:       Optional[int]														=    self.num_layers
															for i in range(__lowerCamelCase			):
																		A				:       List[Any]														=    {0: "batch", 2: "past_sequence + sequence"}
																		A				:       Tuple														=    {0: "batch", 2: "past_sequence + sequence"}
									return common_outputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      int						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									A				:       Tuple														=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)

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

												A				:       int														=    torch.cat(
												    [common_inputs["decoder_attention_mask"], torch.ones(__lowerCamelCase						,						__lowerCamelCase			)]						,						dim=1			)

												A				:       Optional[int]														=    []
												# If the number of encoder and decoder layers are present in the model configuration, both are considered
												A							,			A				:       Dict														=    self.num_layers
												A				:       Dict														=    min(__lowerCamelCase						,						__lowerCamelCase			)
												A				:       Optional[Any]														=    max(__lowerCamelCase						,						__lowerCamelCase			) - min_num_layers
												A				:       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.
												A				:       List[str]														=    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       SCREAMING_SNAKE_CASE__		(						self   :      str						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									A				:       List[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
												A							,			A				:       List[Any]														=    common_inputs["input_ids"].shape
												# Not using the same length for past_key_values
												A				:       Any														=    seqlen + 2
												A							,			A				:       List[Any]														=    self.num_layers
												A							,			A				:       int														=    self.num_attention_heads
												A				:       int														=    (
												    batch,
												    num_encoder_attention_heads,
												    past_key_values_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)

												A				:       Tuple														=    common_inputs["attention_mask"].dtype
												A				:       Dict														=    torch.cat(
												    [common_inputs["attention_mask"], torch.ones(__lowerCamelCase						,						__lowerCamelCase						,						dtype=__lowerCamelCase			)]						,						dim=1			)
												A				:       Optional[Any]														=    [
												    (torch.zeros(__lowerCamelCase			), torch.zeros(__lowerCamelCase			)) for _ in range(__lowerCamelCase			)
												]
									return common_inputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									# 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
									A				:       Any														=    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
									A				:       Any														=    tokenizer.num_special_tokens_to_add(__lowerCamelCase			)
									A				:       Tuple														=    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
									A				:       Dict														=    [" ".join([tokenizer.unk_token]			) * seq_length] * batch_size
									A				:       Tuple														=    dict(tokenizer(__lowerCamelCase						,						return_tensors=__lowerCamelCase			)			)
									return common_inputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       List[Any]														=    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":
												A				:       Tuple														=    self._generate_dummy_inputs_for_causal_lm(
												    __lowerCamelCase						,						batch_size=__lowerCamelCase						,						seq_length=__lowerCamelCase						,						is_pair=__lowerCamelCase						,						framework=__lowerCamelCase			)
									else:
												A				:       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       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      str						,						__lowerCamelCase   :      Union[str, Any]						,						__lowerCamelCase   :      Any						,						__lowerCamelCase   :      List[Any]			)   ->					Dict:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       Any														=    super()._flatten_past_key_values_(__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)
									else:
												A				:       Optional[Any]														=    super(__lowerCamelCase						,						self			)._flatten_past_key_values_(
												    __lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)