





'''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


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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__	:					Dict	     =       16
lowerCAmelCase__	:					List[str]	     =       32


def 		__UpperCamelCase		(       _UpperCAmelCase,    _UpperCAmelCase   ):

		if args.with_tracking:
				__UpperCAmelCase     :				Any					   =			Accelerator(
				    cpu=args.cpu,    mixed_precision=args.mixed_precision,    log_with="all",    project_dir=args.project_dir   )
		else:
				__UpperCAmelCase     :				List[str]					   =			Accelerator(cpu=args.cpu,    mixed_precision=args.mixed_precision   )

		if hasattr(args.checkpointing_steps,    "isdigit"   ):
				if args.checkpointing_steps == "epoch":
						__UpperCAmelCase     :				List[Any]					   =			args.checkpointing_steps
				elif args.checkpointing_steps.isdigit():
						__UpperCAmelCase     :				List[str]					   =			int(args.checkpointing_steps   )
				else:
						raise ValueError(
						    F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."   )
		else:
				__UpperCAmelCase     :				Optional[int]					   =			None
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		__UpperCAmelCase     :				str					   =			config["lr"]
		__UpperCAmelCase     :				Union[str, Any]					   =			int(config["num_epochs"]   )
		__UpperCAmelCase     :				Dict					   =			int(config["seed"]   )
		__UpperCAmelCase     :				int					   =			int(config["batch_size"]   )

		# We need to initialize the trackers we use, and also store our configuration
		if args.with_tracking:
				__UpperCAmelCase     :				Optional[int]					   =			os.path.split(A__   )[-1].split("."   )[0]
				accelerator.init_trackers(A__,    A__   )

		__UpperCAmelCase     :				Any					   =			AutoTokenizer.from_pretrained("bert-base-cased"   )
		__UpperCAmelCase     :				List[str]					   =			load_dataset("glue",    "mrpc"   )
		__UpperCAmelCase     :				Optional[Any]					   =			evaluate.load("glue",    "mrpc"   )

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

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

		def collate_fn(_UpperCAmelCase   ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				__UpperCAmelCase     :				List[str]					   =			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":
						__UpperCAmelCase     :				Union[str, Any]					   =			16
				elif accelerator.mixed_precision != "no":
						__UpperCAmelCase     :				Dict					   =			8
				else:
						__UpperCAmelCase     :				Any					   =			None

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

		# Instantiate dataloaders.
		__UpperCAmelCase     :				Optional[int]					   =			DataLoader(
		    tokenized_datasets["train"],    shuffle=A__,    collate_fn=A__,    batch_size=A__   )
		__UpperCAmelCase     :				Tuple					   =			DataLoader(
		    tokenized_datasets["validation"],    shuffle=A__,    collate_fn=A__,    batch_size=A__   )

		set_seed(A__   )

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

		# 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).
		__UpperCAmelCase     :				Optional[int]					   =			model.to(accelerator.device   )

		# Instantiate optimizer
		__UpperCAmelCase     :				List[Any]					   =			AdamW(params=model.parameters(),    lr=A__   )

		# Instantiate scheduler
		__UpperCAmelCase     :				Optional[int]					   =			get_linear_schedule_with_warmup(
		    optimizer=A__,    num_warmup_steps=100,    num_training_steps=(len(A__   ) * 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.
		__UpperCAmelCase   ,       __UpperCAmelCase   ,       __UpperCAmelCase   ,       __UpperCAmelCase   ,       __UpperCAmelCase     :				Union[str, Any]					   =			accelerator.prepare(
		    A__,    A__,    A__,    A__,    A__   )

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

		# Potentially load in the weights and states from a previous save
		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   )
						__UpperCAmelCase     :				int					   =			os.path.basename(args.resume_from_checkpoint   )
				else:
						# Get the most recent checkpoint
						__UpperCAmelCase     :				List[Any]					   =			[f.name for f in os.scandir(os.getcwd()   ) if f.is_dir()]
						dirs.sort(key=os.path.getctime   )
						__UpperCAmelCase     :				Dict					   =			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
				# Extract `epoch_{i}` or `step_{i}`
				__UpperCAmelCase     :				Tuple					   =			os.path.splitext(A__   )[0]

				if "epoch" in training_difference:
						__UpperCAmelCase     :				Union[str, Any]					   =			int(training_difference.replace("epoch_",    ""   )   ) + 1
						__UpperCAmelCase     :				Optional[int]					   =			None
				else:
						__UpperCAmelCase     :				str					   =			int(training_difference.replace("step_",    ""   )   )
						__UpperCAmelCase     :				str					   =			resume_step // len(A__   )
						resume_step -= starting_epoch * len(A__   )

    # Now we train the model
		for epoch in range(A__,    A__   ):
				model.train()
				if args.with_tracking:
						__UpperCAmelCase     :				Union[str, Any]					   =			0
				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
						__UpperCAmelCase     :				Optional[Any]					   =			accelerator.skip_first_batches(A__,    A__   )
						overall_step += resume_step
				else:
						# After the first iteration though, we need to go back to the original dataloader
						__UpperCAmelCase     :				Any					   =			train_dataloader
				for step, batch in enumerate(A__   ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device   )
						__UpperCAmelCase     :				List[str]					   =			model(**A__   )
						__UpperCAmelCase     :				List[str]					   =			outputs.loss
						__UpperCAmelCase     :				Any					   =			loss / gradient_accumulation_steps
						# We keep track of the loss at each epoch
						if args.with_tracking:
								total_loss += loss.detach().float()
						accelerator.backward(A__   )
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1

						if isinstance(A__,    A__   ):
								__UpperCAmelCase     :				Dict					   =			F"step_{overall_step}"
								if overall_step % checkpointing_steps == 0:
										if args.output_dir is not None:
												__UpperCAmelCase     :				Union[str, Any]					   =			os.path.join(args.output_dir,    A__   )
										accelerator.save_state(A__   )

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

				__UpperCAmelCase     :				str					   =			metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(F"epoch {epoch}:",    A__   )
				if args.with_tracking:
						accelerator.log(
						    {
						        "accuracy": eval_metric["accuracy"],
						        "f1": eval_metric["f1"],
						        "train_loss": total_loss.item() / len(A__   ),
						        "epoch": epoch,
						    },    step=A__,    )

				if checkpointing_steps == "epoch":
						__UpperCAmelCase     :				Optional[int]					   =			F"epoch_{epoch}"
						if args.output_dir is not None:
								__UpperCAmelCase     :				Dict					   =			os.path.join(args.output_dir,    A__   )
						accelerator.save_state(A__   )

		if args.with_tracking:
				accelerator.end_training()


def 		__UpperCamelCase		(       ):
		__UpperCAmelCase     :				List[Any]					   =			argparse.ArgumentParser(description="Simple example of training script."   )
		parser.add_argument(
		    "--mixed_precision",    type=A__,    default=A__,    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=A__,    default=A__,    help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",    )
		parser.add_argument(
		    "--resume_from_checkpoint",    type=A__,    default=A__,    help="If the training should continue from a checkpoint folder.",    )
		parser.add_argument(
		    "--with_tracking",    action="store_true",    help="Whether to load in all available experiment trackers from the environment and use them for logging.",    )
		parser.add_argument(
		    "--output_dir",    type=A__,    default=".",    help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",    )
		parser.add_argument(
		    "--project_dir",    type=A__,    default="logs",    help="Location on where to store experiment tracking logs` and relevent project information",    )
		__UpperCAmelCase     :				List[str]					   =			parser.parse_args()
		__UpperCAmelCase     :				Optional[Any]					   =			{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
		training_function(A__,    A__   )


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


_A  					= logging.get_logger(__name__)

_A  					= {
    "google/bigbird-pegasus-large-arxiv": (
        "https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"
    ),
    "google/bigbird-pegasus-large-pubmed": (
        "https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"
    ),
    "google/bigbird-pegasus-large-bigpatent": (
        "https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							List[Any]           =		"bigbird_pegasus"
     UpperCAmelCase__				:							Tuple           =		["past_key_values"]
     UpperCAmelCase__				:							Optional[Any]           =		{
         "num_attention_heads": "encoder_attention_heads",
         "hidden_size": "d_model",
         "attention_probs_dropout_prob": "attention_dropout",
     }





     def __init__(self      :    Any				,    _A      :    List[str]=9_6_1_0_3				,    _A      :    List[Any]=4_0_9_6				,    _A      :    str=1_6				,    _A      :    Tuple=4_0_9_6				,    _A      :    List[str]=1_6				,    _A      :    Optional[int]=1_6				,    _A      :    List[Any]=4_0_9_6				,    _A      :    Dict=1_6				,    _A      :    Tuple=0.0				,    _A      :    List[str]=0.0				,    _A      :    Dict=True				,    _A      :    int=True				,    _A      :    List[Any]="gelu_new"				,    _A      :    int=1_0_2_4				,    _A      :    int=0.1				,    _A      :    List[Any]=0.0				,    _A      :    Optional[int]=0.0				,    _A      :    Any=0.02				,    _A      :    Dict=2				,    _A      :    List[Any]=0.0				,    _A      :    Any=True				,    _A      :    str=0				,    _A      :    Dict=2				,    _A      :    Any=1				,    _A      :    List[str]="block_sparse"				,    _A      :    int=6_4				,    _A      :    Optional[int]=3				,    _A      :    List[Any]=False				,    **_A      :    Tuple				,    )						->						Any:
         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

         # extra config
         snake_case      =					attention_type
         snake_case      =					block_size
         snake_case      =					num_random_blocks
         snake_case      =					use_bias

         super().__init__(
             pad_token_id=_A				,    bos_token_id=_A				,    eos_token_id=_A				,    is_encoder_decoder=_A				,    decoder_start_token_id=_A				,    **_A				,    )







class      lowerCamelCase		(  A_  ):





     @property
     def 		UpperCAmelCase(self      :    Any   )						->						Mapping[str, Mapping[int, str]]:
         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_(_A				,    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(_A   ):
                     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 		UpperCAmelCase(self      :    str   )						->						Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
             snake_case      =					super().outputs
         else:
             snake_case      =					super(_A				,    self   ).outputs
             if self.use_past:
                 snake_case     ,							snake_case      =					self.num_layers
                 for i in range(_A   ):
                     snake_case      =					{0: "batch", 2: "past_sequence + sequence"}
                     snake_case      =					{0: "batch", 2: "past_sequence + sequence"}
         return common_outputs





     def 		UpperCAmelCase(self      :    Any				,    _A      :    PreTrainedTokenizer				,    _A      :    int = -1				,    _A      :    int = -1				,    _A      :    bool = False				,    _A      :    Optional[TensorType] = None				,    )						->						Mapping[str, Any]:
         snake_case      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _A				,    _A				,    _A				,    _A				,    _A   )

         # 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(
             _A				,    _A				,    _A				,    _A				,    _A   )
         snake_case      =					{f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
         snake_case      =					dict(**_A				,    **_A   )

         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(_A				,    _A   )]				,    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(_A				,    _A   )
             snake_case      =					max(_A				,    _A   ) - min_num_layers
             snake_case      =					"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

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





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

         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(_A				,    _A				,    dtype=_A   )]				,    dim=1   )
             snake_case      =					[
                 (torch.zeros(_A   ), torch.zeros(_A   )) for _ in range(_A   )
             ]
         return common_inputs





     def 		UpperCAmelCase(self      :    Dict				,    _A      :    PreTrainedTokenizer				,    _A      :    int = -1				,    _A      :    int = -1				,    _A      :    bool = False				,    _A      :    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
         snake_case      =					compute_effective_axis_dimension(
             _A				,    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(_A   )
         snake_case      =					compute_effective_axis_dimension(
             _A				,    fixed_dimension=OnnxConfig.default_fixed_sequence				,    num_token_to_add=_A   )

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





     def 		UpperCAmelCase(self      :    Any				,    _A      :    PreTrainedTokenizer				,    _A      :    int = -1				,    _A      :    int = -1				,    _A      :    bool = False				,    _A      :    Optional[TensorType] = None				,    )						->						Mapping[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
             snake_case      =					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 _A				,    batch_size=_A				,    seq_length=_A				,    is_pair=_A				,    framework=_A   )

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

         return common_inputs





     def 		UpperCAmelCase(self      :    List[Any]				,    _A      :    str				,    _A      :    Union[str, Any]				,    _A      :    Tuple				,    _A      :    List[Any]   )						->						Any:
         if self.task in ["default", "seq2seq-lm"]:
             snake_case      =					super()._flatten_past_key_values_(_A				,    _A				,    _A				,    _A   )
         else:
             snake_case      =					super(_A				,    self   )._flatten_past_key_values_(
                 _A				,    _A				,    _A				,    _A   )

