




'''simple docstring'''







import logging
import os
import sys
from dataclasses import dataclass, field
from typing import List, Optional

import torch
from datasets import Dataset
from torch import nn
from tqdm.auto import tqdm

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
    utils,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


_lowerCamelCase							:						int							     =  '\nDistills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class\nnames. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but\nwhen only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled\ndataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided\nclass names.\n'

_lowerCamelCase							:						Any							     =  logging.getLogger(__name__)





@dataclass
class 					lowerCamelCase_							:




					'''simple docstring'''






					lowercase__   =			field(
					    default="""roberta-large-mnli""",	metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}							)
					lowercase__   =			field(
					    default="""This example is {}.""",	metadata={
					        """help""": (
					            """Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}}"""
					            """where class name is inserted."""
					        )
					    },	)
					lowercase__   =			field(
					    default=3_2,	metadata={"""help""": """Batch size for generating teacher predictions."""}							)
					lowercase__   =			field(
					    default=UpperCAmelCase_,	metadata={
					        """help""": (
					            """Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called"""
					            """multi-class multi-label classification)."""
					        )
					    },	)
					lowercase__   =			field(
					    default=1.0,	metadata={"""help""": """Temperature applied to teacher softmax for distillation."""}							)





@dataclass
class 					lowerCamelCase_							:




					'''simple docstring'''






					lowercase__   =			field(
					    default="""distilbert-base-uncased""",	metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}							)





@dataclass
class 					lowerCamelCase_							:




					'''simple docstring'''






					lowercase__   =			field(metadata={"""help""": """Text file with one unlabeled instance per line."""}							)
					lowercase__   =			field(metadata={"""help""": """Text file with one class name per line."""}							)
					lowercase__   =			field(
					    default=UpperCAmelCase_,	metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not."""},	)





@dataclass
class 					lowerCamelCase_							(	UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__   =			field(
					    default=UpperCAmelCase_,	metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""},	)
					lowercase__   =			field(
					    default=3_2,	metadata={"""help""": """Batch size per GPU/TPU core/CPU for training."""}							)
					lowercase__   =			field(
					    default=1_2_8,	metadata={"""help""": """Batch size per GPU/TPU core/CPU for evaluation."""}							)
					lowercase__   =			field(default=1.0,	metadata={"""help""": """Total number of training epochs to perform."""}							)
					lowercase__   =			field(default=UpperCAmelCase_,	metadata={"""help""": """Whether to run training of student model."""}							)
					lowercase__   =			field(
					    default=UpperCAmelCase_,	metadata={
					        """help""": (
					            """Whether to evaluate the agreement of the final student predictions and the teacher predictions"""
					            """after training."""
					        )
					    },	)
					lowercase__   =			field(
					    default=0,	metadata={
					        """help""": (
					            """Limit the total amount of checkpoints."""
					            """Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints)."""
					        )
					    },	)





class 					lowerCamelCase_							(	UpperCAmelCase_							):




					'''simple docstring'''






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase=False			):
							_snake_case     	=	inputs["labels"]
							_snake_case     	=	model(inputs["input_ids"],  attention_mask=inputs["attention_mask"]			)
							_snake_case     	=	outputs[0]

							_snake_case     	=	-torch.sum(target_p * logits.log_softmax(dim=-1			),  axis=-1			).mean()

							if return_outputs:
									return loss, outputs

							return loss






def 		_lowercase		(     lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	[]
		with open(lowercase_						,							"r"  ) as f:
				for line in f:
						_snake_case     	=	line.strip()
						if len(lowercase_  ) > 0:
								lines.append(lowercase_  )
		return lines






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	[]
		_snake_case     	=	[]
		for example in examples:
				for name in class_names:
						premises.append(lowercase_  )
						hypotheses.append(hypothesis_template.format(lowercase_  )  )
		return premises, hypotheses






def 		_lowercase		(     lowercase_  ):



		'''simple docstring'''







		for label, ind in config.labelaid.items():
				if label.lower().startswith("entail"  ):
						return ind
		logger.warning("Could not identify entailment dimension from teacher config label2id. Setting to -1."  )
		return -1






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							):



		'''simple docstring'''







		_snake_case     	=	AutoModelForSequenceClassification.from_pretrained(lowercase_  )
		_snake_case     	=	model.config
		if not no_cuda and torch.cuda.is_available():
				_snake_case     	=	nn.DataParallel(model.cuda()  )
				batch_size *= len(model.device_ids  )
		_snake_case     	=	AutoTokenizer.from_pretrained(lowercase_						,							use_fast=lowercase_  )

		_snake_case	,   _snake_case     	=	get_premise_hypothesis_pairs(lowercase_						,							lowercase_						,							lowercase_  )
		_snake_case     	=	[]

		for i in tqdm(range(0						,							len(lowercase_  )						,							lowercase_  )  ):
				_snake_case     	=	premises[i : i + batch_size]
				_snake_case     	=	hypotheses[i : i + batch_size]

				_snake_case     	=	tokenizer(
				    lowercase_						,							lowercase_						,							padding=lowercase_						,							truncation="only_first"						,							return_tensors="pt"						,							)

				with torch.cuda.amp.autocast(enabled=lowercase_  ):
						with torch.no_grad():
								_snake_case     	=	model(**lowercase_  )
				logits.append(outputs.logits.detach().cpu().float()  )

		_snake_case     	=	get_entailment_id(lowercase_  )
		_snake_case     	=	-1 if entail_id == 0 else 0
		_snake_case     	=	torch.cat(lowercase_						,							dim=0  )  # N*K x 3
		_snake_case     	=	logits.reshape(len(lowercase_  )						,							len(lowercase_  )						,							-1  )[..., [contr_id, entail_id]]  # N x K x 2

		if multi_label:
				# softmax over (contr, entail) logits for each class independently
				_snake_case     	=	(nli_logits / temperature).softmax(-1  )
		else:
				# softmax over entail logits across classes s.t. class probabilities sum to 1.
				_snake_case     	=	(nli_logits / temperature).softmax(1  )

		return nli_prob[..., 1]  # N x K






def 		_lowercase		(     ):



		'''simple docstring'''







		_snake_case     	=	HfArgumentParser(
		    (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)						,							description=lowercase_						,							)

		if len(sys.argv  ) == 2 and sys.argv[1].endswith(".json"  ):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				_snake_case	,   _snake_case	,   _snake_case	,   _snake_case     	=	parser.parse_json_file(
				    json_file=os.path.abspath(sys.argv[1]  )  )
		else:
				_snake_case	,   _snake_case	,   _snake_case	,   _snake_case     	=	parser.parse_args_into_dataclasses()

		# Detecting last checkpoint.
		_snake_case     	=	None
		if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
				_snake_case     	=	get_last_checkpoint(training_args.output_dir  )
				if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
						raise ValueError(
						    f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
						    "Use --overwrite_output_dir to overcome."  )
				elif last_checkpoint is not None:
						logger.info(
						    f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
						    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."  )

    # Setup logging
		logging.basicConfig(
		    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"						,							datefmt="%m/%d/%Y %H:%M:%S"						,							handlers=[logging.StreamHandler(sys.stdout  )]						,							)
		logger.setLevel(logging.INFO if is_main_process(training_args.local_rank  ) else logging.WARN  )

		# Log on each process the small summary:
		logger.warning(
		    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
		    + f'''distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}'''  )
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(training_args.local_rank  ):
				utils.logging.set_verbosity_info()
				utils.logging.enable_default_handler()
				utils.logging.enable_explicit_format()

		if training_args.local_rank != -1:
				raise ValueError("Distributed training is not currently supported."  )
		if training_args.tpu_num_cores is not None:
				raise ValueError("TPU acceleration is not currently supported."  )

		logger.info(f'''Training/evaluation parameters {training_args}'''  )

		# Set seed before initializing model.
		set_seed(training_args.seed  )

		# 1. read in data
		_snake_case     	=	read_lines(data_args.data_file  )
		_snake_case     	=	read_lines(data_args.class_names_file  )

		# 2. get teacher predictions and load into dataset
		logger.info("Generating predictions from zero-shot teacher model"  )
		_snake_case     	=	get_teacher_predictions(
		    teacher_args.teacher_name_or_path						,							lowercase_						,							lowercase_						,							teacher_args.hypothesis_template						,							teacher_args.teacher_batch_size						,							teacher_args.temperature						,							teacher_args.multi_label						,							data_args.use_fast_tokenizer						,							training_args.no_cuda						,							training_args.fpaa						,							)
		_snake_case     	=	Dataset.from_dict(
		    {
		        "text": examples,
		        "labels": teacher_soft_preds,
		    }  )

		# 3. create student
		logger.info("Initializing student model"  )
		_snake_case     	=	AutoModelForSequenceClassification.from_pretrained(
		    student_args.student_name_or_path						,							num_labels=len(lowercase_  )  )
		_snake_case     	=	AutoTokenizer.from_pretrained(student_args.student_name_or_path						,							use_fast=data_args.use_fast_tokenizer  )
		_snake_case     	=	dict(enumerate(lowercase_  )  )
		_snake_case     	=	{label: i for i, label in enumerate(lowercase_  )}

		# 4. train student on teacher predictions
		_snake_case     	=	dataset.map(lowercase_						,							input_columns="text"  )
		dataset.set_format("torch"  )

		def compute_metrics(lowercase_						,							lowercase_=False  ):
				_snake_case     	=	p.predictions.argmax(-1  )
				_snake_case     	=	p.label_ids.argmax(-1  )  # "label_ids" are actually distributions
				return {"agreement": (preds == proxy_labels).mean().item()}

		_snake_case     	=	DistillationTrainer(
		    model=lowercase_						,							tokenizer=lowercase_						,							args=lowercase_						,							train_dataset=lowercase_						,							compute_metrics=lowercase_						,							)

		if training_args.do_train:
				logger.info("Training student model on teacher predictions"  )
				trainer.train()

		if training_args.do_eval:
				_snake_case     	=	trainer.evaluate(eval_dataset=lowercase_  )["eval_agreement"]
				logger.info(f'''Agreement of student and teacher predictions: {agreement * 100:0.2f}%'''  )

		trainer.save_model()


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 ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_lowerCamelCase							:						Any							     =  logging.get_logger(__name__)

_lowerCamelCase							:						List[str]							     =  {
    '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_							(	UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__   =			"""bigbird_pegasus"""
					lowercase__   =			["""past_key_values"""]
					lowercase__   =			{
					    """num_attention_heads""": """encoder_attention_heads""",
					    """hidden_size""": """d_model""",
					    """attention_probs_dropout_prob""": """attention_dropout""",
					}






					def __init__(   self,  __UpperCamelCase=96_103,  __UpperCamelCase=4_096,  __UpperCamelCase=16,  __UpperCamelCase=4_096,  __UpperCamelCase=16,  __UpperCamelCase=16,  __UpperCamelCase=4_096,  __UpperCamelCase=16,  __UpperCamelCase=0.0,  __UpperCamelCase=0.0,  __UpperCamelCase=True,  __UpperCamelCase=True,  __UpperCamelCase="gelu_new",  __UpperCamelCase=1_024,  __UpperCamelCase=0.1,  __UpperCamelCase=0.0,  __UpperCamelCase=0.0,  __UpperCamelCase=0.02,  __UpperCamelCase=2,  __UpperCamelCase=0.0,  __UpperCamelCase=True,  __UpperCamelCase=0,  __UpperCamelCase=2,  __UpperCamelCase=1,  __UpperCamelCase="block_sparse",  __UpperCamelCase=64,  __UpperCamelCase=3,  __UpperCamelCase=False,  **__UpperCamelCase,  ):
							_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=__UpperCamelCase,  bos_token_id=__UpperCamelCase,  eos_token_id=__UpperCamelCase,  is_encoder_decoder=__UpperCamelCase,  decoder_start_token_id=__UpperCamelCase,  **__UpperCamelCase,  )





class 					lowerCamelCase_							(	UpperCAmelCase_							):




					'''simple docstring'''






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






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase,  __UpperCamelCase = -1,  __UpperCamelCase = -1,  __UpperCamelCase = False,  __UpperCamelCase = None,  ):
							_snake_case     	=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase			)

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

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

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






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase,  __UpperCamelCase = -1,  __UpperCamelCase = -1,  __UpperCamelCase = False,  __UpperCamelCase = None,  ):
							_snake_case     	=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase			)

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






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase,  __UpperCamelCase = -1,  __UpperCamelCase = -1,  __UpperCamelCase = False,  __UpperCamelCase = None,  ):
							# 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(
							    __UpperCamelCase,  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(__UpperCamelCase			)
							_snake_case     	=	compute_effective_axis_dimension(
							    __UpperCamelCase,  fixed_dimension=OnnxConfig.default_fixed_sequence,  num_token_to_add=__UpperCamelCase			)

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






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase,  __UpperCamelCase = -1,  __UpperCamelCase = -1,  __UpperCamelCase = False,  __UpperCamelCase = None,  ):
							if self.task in ["default", "seq2seq-lm"]:
									_snake_case     	=	self._generate_dummy_inputs_for_default_and_seqaseq_lm(
									    __UpperCamelCase,  batch_size=__UpperCamelCase,  seq_length=__UpperCamelCase,  is_pair=__UpperCamelCase,  framework=__UpperCamelCase			)

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

							return common_inputs






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





