




'''simple docstring'''

import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


a  :				List[str]									=  logging.getLogger(__name__)

a  :				int									=  'pytorch_model.bin'






@dataclasses.dataclass
class 						a				:
				snake_case_      =		dataclasses.field(
				    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."}     )
				snake_case_      =		dataclasses.field(
				    default=_lowerCamelCase   ,     metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."}   ,     )







@dataclasses.dataclass
class 						a				:
				snake_case_      =		dataclasses.field(metadata={"help": "A csv or a json file containing the training data."}     )
				snake_case_      =		dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."}     )
				snake_case_      =		dataclasses.field(
				    default=_lowerCamelCase   ,     metadata={"help": "A csv or a json file containing the validation data."}     )
				snake_case_      =		dataclasses.field(
				    default=_lowerCamelCase   ,     metadata={"help": "The name of the task to train on."}   ,     )
				snake_case_      =		dataclasses.field(
				    default=_lowerCamelCase   ,     metadata={"help": "The list of labels for the task."}     )







@dataclasses.dataclass
class 						a				:
				snake_case_      =		dataclasses.field(
				    metadata={"help": "The output directory where the model predictions and checkpoints will be written."}     )
				snake_case_      =		dataclasses.field(
				    default="accuracy"   ,     metadata={"help": "The evaluation metric used for the task."}     )
				snake_case_      =		dataclasses.field(
				    default="no"   ,     metadata={
				        "help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
				    }   ,     )
				snake_case_      =		dataclasses.field(
				    default=10   ,     metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}   ,     )
				snake_case_      =		dataclasses.field(
				    default=0.0   ,     metadata={
				        "help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
				    }   ,     )
				snake_case_      =		dataclasses.field(
				    default=_lowerCamelCase   ,     metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."}   ,     )
				snake_case_      =		dataclasses.field(
				    default=_lowerCamelCase   ,     metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."}   ,     )
				snake_case_      =		dataclasses.field(
				    default=_lowerCamelCase   ,     metadata={"help": "Whether to fine-tune on labeled data after pseudo training."}   ,     )
				snake_case_      =		dataclasses.field(
				    default=0.0   ,     metadata={"help": "Confidence threshold for pseudo-labeled data filtering."}   ,     )
				snake_case_      =		dataclasses.field(
				    default=100   ,     metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}   ,     )
				snake_case_      =		dataclasses.field(
				    default=_lowerCamelCase   ,     metadata={"help": "Random seed for initialization."}   ,     )







def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_									=				datasets.concatenate_datasets([infer_input, infer_output],      axis=1	)

					if args.do_filter_by_confidence:
										snake_case_									=				dataset.filter(lambda __UpperCAmelCase	: example["probability"] > args.confidence_threshold	)

					if args.do_filter_by_val_performance:
										assert eval_result >= 0.0 and eval_result <= 1.0
										snake_case_									=				int(eval_result * len(__UpperCAmelCase	)	)
										print(__UpperCAmelCase	)
										snake_case_									=				dataset.sort('''probability''',      reverse=__UpperCAmelCase	)
										snake_case_									=				dataset.select(range(__UpperCAmelCase	)	)

					snake_case_									=				dataset.remove_columns(['''label''', '''probability''']	)
					snake_case_									=				dataset.rename_column('''prediction''',      '''label'''	)
					snake_case_									=				dataset.map(lambda __UpperCAmelCase	: {"label": idalabel[example["label"]]}	)
					snake_case_									=				dataset.shuffle(seed=args.seed	)

					snake_case_									=				os.path.join(__UpperCAmelCase,      F"train_pseudo.{args.data_file_extension}"	)
					if args.data_file_extension == "csv":
										dataset.to_csv(__UpperCAmelCase,      index=__UpperCAmelCase	)
					else:
										dataset.to_json(__UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      **__UpperCAmelCase	)		->					List[Any]:

					'''simple docstring'''

					snake_case_									=				Accelerator()
					# Make one log on every process with the configuration for debugging.
					logging.basicConfig(
					    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',      datefmt='''%m/%d/%Y %H:%M:%S''',      level=logging.INFO,      )
					logger.info(accelerator.state	)

					# Setup logging, we only want one process per machine to log things on the
					# screen. accelerator.is_local_main_process is only True for one process per
					# machine.
					logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR	)

					if accelerator.is_local_main_process:
										datasets.utils.logging.set_verbosity_warning()
										transformers.utils.logging.set_verbosity_info()
					else:
										datasets.utils.logging.set_verbosity_error()
										transformers.utils.logging.set_verbosity_error()

					snake_case_									=				STModelArguments(model_name_or_path=__UpperCAmelCase	)
					snake_case_									=				STDataArguments(train_file=__UpperCAmelCase,      infer_file=__UpperCAmelCase	)
					snake_case_									=				STTrainingArguments(output_dir=__UpperCAmelCase	)
					snake_case_									=				argparse.Namespace()

					for arg_class in (model_args, data_args, training_args):
										for key, value in vars(__UpperCAmelCase	).items():
															setattr(__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)

					for key, value in kwargs.items():
										if hasattr(__UpperCAmelCase,      __UpperCAmelCase	):
															setattr(__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)

    # Sanity checks
					snake_case_									=				{}
					snake_case_									=				None

					# You need to provide the training data and the data to predict on
					assert args.train_file is not None
					assert args.infer_file is not None
					snake_case_									=				args.train_file
					snake_case_									=				args.infer_file

					if args.evaluation_strategy != IntervalStrategy.NO.value:
										assert args.eval_file is not None
										snake_case_									=				args.eval_file

					for key in data_files:
										snake_case_									=				data_files[key].split('''.'''	)[-1]
										assert extension in ["csv", "json"], F"`{key}_file` should be a csv or a json file."
										if args.data_file_extension is None:
															snake_case_									=				extension
										else:
															assert extension == args.data_file_extension, F"`{key}_file` should be a {args.data_file_extension} file`."

					assert (
					    args.eval_metric in datasets.list_metrics()
					), F"{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."

					# If passed along, set the training seed now.
					if args.seed is not None:
										set_seed(args.seed	)

					logger.info('''Creating the initial data directory for self-training...'''	)
					snake_case_									=				F"{args.output_dir}/self-train_iter-{{}}".format
					snake_case_									=				data_dir_format(0	)

					if accelerator.is_main_process:
										if args.output_dir is not None:
															os.makedirs(args.output_dir,      exist_ok=__UpperCAmelCase	)
															os.makedirs(__UpperCAmelCase,      exist_ok=__UpperCAmelCase	)
					accelerator.wait_for_everyone()

					snake_case_									=				None
					snake_case_									=				None
					snake_case_									=				0
					snake_case_									=				False
					# Show the progress bar
					snake_case_									=				tqdm(range(args.max_selftrain_iterations	),      disable=not accelerator.is_local_main_process	)

					# Self-train
					for iteration in range(0,      int(args.max_selftrain_iterations	)	):
										snake_case_									=				data_dir_format(__UpperCAmelCase	)
										assert os.path.exists(__UpperCAmelCase	)

										# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
										# iteration > 0
										snake_case_									=				os.path.join(__UpperCAmelCase,      '''stage-1'''	)
										snake_case_									=				{
										    '''accelerator''': accelerator,
										    '''model_name_or_path''': args.model_name_or_path,
										    '''cache_dir''': args.cache_dir,
										    '''do_train''': True,
										    '''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
										    '''do_eval''': True if args.eval_file is not None else False,
										    '''eval_file''': data_files['''eval'''],
										    '''do_predict''': True,
										    '''infer_file''': data_files['''infer'''],
										    '''task_name''': args.task_name,
										    '''label_list''': args.label_list,
										    '''output_dir''': current_output_dir,
										    '''eval_metric''': args.eval_metric,
										    '''evaluation_strategy''': args.evaluation_strategy,
										    '''early_stopping_patience''': args.early_stopping_patience,
										    '''early_stopping_threshold''': args.early_stopping_threshold,
										    '''seed''': args.seed,
										}
										# Add additional training arguments
										for key, value in kwargs.items():
															if key not in arguments_dict and not hasattr(__UpperCAmelCase,      __UpperCAmelCase	):
																				arguments_dict.update({key: value}	)

										snake_case_									=				os.path.join(__UpperCAmelCase,      '''best-checkpoint''',      __UpperCAmelCase	)
										if os.path.exists(__UpperCAmelCase	):
															logger.info(
															    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.''',      __UpperCAmelCase,      __UpperCAmelCase,      )
										else:
															logger.info('''***** Running self-training: iteration: %d, stage: 1 *****''',      __UpperCAmelCase	)
															finetune(**__UpperCAmelCase	)
															accelerator.wait_for_everyone()
															assert os.path.exists(__UpperCAmelCase	)
															logger.info('''Self-training job completed: iteration: %d, stage: 1.''',      __UpperCAmelCase	)

										if iteration > 0 and args.finetune_on_labeled_data:
															# Stage 2 (optional): fine-tuning on the original labeled data
															snake_case_									=				os.path.join(__UpperCAmelCase,      '''best-checkpoint'''	)
															snake_case_									=				os.path.join(__UpperCAmelCase,      '''stage-2'''	)
															# Update arguments_dict
															snake_case_									=				model_path
															snake_case_									=				data_files['''train''']
															snake_case_									=				current_output_dir

															snake_case_									=				os.path.join(__UpperCAmelCase,      '''best-checkpoint''',      __UpperCAmelCase	)
															if os.path.exists(__UpperCAmelCase	):
																				logger.info(
																				    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.''',      __UpperCAmelCase,      __UpperCAmelCase,      )
															else:
																				logger.info('''***** Running self-training: iteration: %d, stage: 2 *****''',      __UpperCAmelCase	)
																				finetune(**__UpperCAmelCase	)
																				accelerator.wait_for_everyone()
																				assert os.path.exists(__UpperCAmelCase	)
																				logger.info('''Self-training job completed: iteration: %d, stage: 2.''',      __UpperCAmelCase	)

										snake_case_									=				iteration
										snake_case_									=				data_dir_format(iteration + 1	)

										snake_case_									=				AutoConfig.from_pretrained(os.path.join(__UpperCAmelCase,      '''best-checkpoint'''	)	)
										snake_case_									=				config.idalabel
										snake_case_									=				os.path.join(__UpperCAmelCase,      '''eval_results_best-checkpoint.json'''	)
										snake_case_									=				os.path.join(__UpperCAmelCase,      '''test_results_best-checkpoint.json'''	)
										assert os.path.exists(__UpperCAmelCase	)

										with open(__UpperCAmelCase,      '''r'''	) as f:
															snake_case_									=				float(json.load(__UpperCAmelCase	)[args.eval_metric]	)
										snake_case_									=				os.path.join(__UpperCAmelCase,      '''infer_output_best-checkpoint.csv'''	)
										assert os.path.exists(__UpperCAmelCase	)
										# Loading the dataset from local csv or json files.
										snake_case_									=				load_dataset(args.data_file_extension,      data_files={'''data''': data_files['''infer''']}	)['''data''']
										snake_case_									=				load_dataset('''csv''',      data_files={'''data''': infer_output_file}	)['''data''']

										if accelerator.is_main_process:
															os.makedirs(__UpperCAmelCase,      exist_ok=__UpperCAmelCase	)
															shutil.copy(__UpperCAmelCase,      os.path.join(__UpperCAmelCase,      F"eval_results_iter-{iteration}.json"	)	)
															if os.path.exists(__UpperCAmelCase	):
																				shutil.copy(__UpperCAmelCase,      os.path.join(__UpperCAmelCase,      F"test_results_iter-{iteration}.json"	)	)
															create_pseudo_labeled_data(__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)
										accelerator.wait_for_everyone()

										snake_case_									=				os.path.join(__UpperCAmelCase,      F"train_pseudo.{args.data_file_extension}"	)

										if args.evaluation_strategy != IntervalStrategy.NO.value:
															snake_case_									=				eval_result

															if best_iteration is None:
																				snake_case_									=				new_iteration
																				snake_case_									=				new_eval_result
															else:
																				if new_eval_result - best_eval_result > args.early_stopping_threshold:
																									snake_case_									=				new_iteration
																									snake_case_									=				new_eval_result
																									snake_case_									=				0
																				else:
																									if new_eval_result == best_eval_result:
																														snake_case_									=				new_iteration
																														snake_case_									=				new_eval_result
																									early_stopping_patience_counter += 1

																				if early_stopping_patience_counter >= args.early_stopping_patience:
																									snake_case_									=				True

										progress_bar.update(1	)

										if should_training_stop:
															break

					if best_iteration is not None:
										# Save the best iteration
										logger.info('''Best iteration: %d''',      __UpperCAmelCase	)
										logger.info('''Best evaluation result: %s = %f''',      args.eval_metric,      __UpperCAmelCase	)
										accelerator.wait_for_everyone()
										if accelerator.is_main_process:
															shutil.copy(
															    os.path.join(__UpperCAmelCase,      F"eval_results_iter-{iteration}.json"	),      os.path.join(__UpperCAmelCase,      '''eval_results_best-iteration.json'''	),      )
					else:
										# Assume that the last iteration is the best
										logger.info('''Best iteration: %d''',      args.max_selftrain_iterations - 1	)
										logger.info('''Best evaluation result: %s = %f''',      args.eval_metric,      __UpperCAmelCase	)
										accelerator.wait_for_everyone()
										if accelerator.is_main_process:
															shutil.copy(
															    os.path.join(__UpperCAmelCase,      F"eval_results_iter-{args.max_selftrain_iterations - 1}.json"	),      os.path.join(__UpperCAmelCase,      '''eval_results_best-iteration.json'''	),      )







'''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


a  :				Dict									=  logging.get_logger(__name__)

a  :				List[str]									=  {
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		"marian"
				snake_case_      =		["past_key_values"]
				snake_case_      =		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


				def __init__(		self    :					List[Any]       ,				lowercase_    :					Optional[Any]=5_8101       ,				lowercase_    :					Dict=None       ,				lowercase_    :					List[str]=1024       ,				lowercase_    :					Optional[Any]=12       ,				lowercase_    :					int=4096       ,				lowercase_    :					Any=16       ,				lowercase_    :					Optional[int]=12       ,				lowercase_    :					str=4096       ,				lowercase_    :					Union[str, Any]=16       ,				lowercase_    :					Dict=0.0       ,				lowercase_    :					Union[str, Any]=0.0       ,				lowercase_    :					Optional[Any]=True       ,				lowercase_    :					Union[str, Any]=True       ,				lowercase_    :					int="gelu"       ,				lowercase_    :					Dict=1024       ,				lowercase_    :					int=0.1       ,				lowercase_    :					Tuple=0.0       ,				lowercase_    :					Tuple=0.0       ,				lowercase_    :					Tuple=0.02       ,				lowercase_    :					int=5_8100       ,				lowercase_    :					Optional[Any]=False       ,				lowercase_    :					Any=5_8100       ,				lowercase_    :					Optional[int]=0       ,				lowercase_    :					Dict=0       ,				lowercase_    :					List[str]=True       ,				**lowercase_    :					Any       ,				):
									snake_case_									=				vocab_size
									snake_case_									=				decoder_vocab_size or 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_									=				use_cache
									snake_case_									=				encoder_layers
									snake_case_									=				scale_embedding  # scale factor will be sqrt(d_model) if True
									snake_case_									=				share_encoder_decoder_embeddings
									super().__init__(
									    pad_token_id=lowercase_       ,				eos_token_id=lowercase_       ,				is_encoder_decoder=lowercase_       ,				decoder_start_token_id=lowercase_       ,				forced_eos_token_id=lowercase_       ,				**lowercase_       ,				)







class 						a				(   _lowerCamelCase     ):


				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
				def   A_ (		self    :					Union[str, Any]					):
									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_(lowercase_       ,				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(lowercase_					):
																								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
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
				def   A_ (		self    :					Dict					):
									if self.task in ["default", "seq2seq-lm"]:
														snake_case_									=				super().outputs
									else:
														snake_case_									=				super(lowercase_       ,				self					).outputs
														if self.use_past:
																			snake_case_			,snake_case_									=				self.num_layers
																			for i in range(lowercase_					):
																								snake_case_									=				{0: '''batch''', 2: '''past_sequence + sequence'''}
																								snake_case_									=				{0: '''batch''', 2: '''past_sequence + sequence'''}
									return common_outputs


				def   A_ (		self    :					Dict       ,				lowercase_    :					PreTrainedTokenizer       ,				lowercase_    :					int = -1       ,				lowercase_    :					int = -1       ,				lowercase_    :					bool = False       ,				lowercase_    :					Optional[TensorType] = None       ,				):
									snake_case_									=				self._generate_dummy_inputs_for_encoder_and_decoder(
									    lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_					)

									# Generate decoder inputs
									snake_case_									=				seq_length if not self.use_past else 1
									snake_case_									=				self._generate_dummy_inputs_for_encoder_and_decoder(
									    lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_					)
									snake_case_									=				{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
									snake_case_									=				dict(**lowercase_       ,				**lowercase_					)

									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(lowercase_       ,				lowercase_					)]       ,				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(lowercase_       ,				lowercase_					)
														snake_case_									=				max(lowercase_       ,				lowercase_					) - min_num_layers
														snake_case_									=				'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

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


				def   A_ (		self    :					Union[str, Any]       ,				lowercase_    :					PreTrainedTokenizer       ,				lowercase_    :					int = -1       ,				lowercase_    :					int = -1       ,				lowercase_    :					bool = False       ,				lowercase_    :					Optional[TensorType] = None       ,				):
									snake_case_									=				self._generate_dummy_inputs_for_encoder_and_decoder(
									    lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_					)

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


				def   A_ (		self    :					List[str]       ,				lowercase_    :					PreTrainedTokenizer       ,				lowercase_    :					int = -1       ,				lowercase_    :					int = -1       ,				lowercase_    :					bool = False       ,				lowercase_    :					Optional[TensorType] = 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(
									    lowercase_       ,				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(lowercase_					)
									snake_case_									=				compute_effective_axis_dimension(
									    lowercase_       ,				fixed_dimension=OnnxConfig.default_fixed_sequence       ,				num_token_to_add=lowercase_					)

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


				def   A_ (		self    :					Any       ,				lowercase_    :					PreTrainedTokenizer       ,				lowercase_    :					int = -1       ,				lowercase_    :					int = -1       ,				lowercase_    :					bool = False       ,				lowercase_    :					Optional[TensorType] = None       ,				):
									if self.task in ["default", "seq2seq-lm"]:
														snake_case_									=				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
														    lowercase_       ,				batch_size=lowercase_       ,				seq_length=lowercase_       ,				is_pair=lowercase_       ,				framework=lowercase_					)

									else:
														snake_case_									=				self._generate_dummy_inputs_for_causal_lm(
														    lowercase_       ,				batch_size=lowercase_       ,				seq_length=lowercase_       ,				is_pair=lowercase_       ,				framework=lowercase_					)

									return common_inputs


				def   A_ (		self    :					Dict       ,				lowercase_    :					List[str]       ,				lowercase_    :					List[str]       ,				lowercase_    :					int       ,				lowercase_    :					List[str]					):
									if self.task in ["default", "seq2seq-lm"]:
														snake_case_									=				super()._flatten_past_key_values_(lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_					)
									else:
														snake_case_									=				super(lowercase_       ,				self					)._flatten_past_key_values_(
														    lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_					)


				@property
				def   A_ (		self    :					List[str]					):
									return 1e-4

