


import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer

def 			lowerCAmelCase__					(     a__:						Union[str, Any]     ,    a__:						Optional[Any]     ,    a__:						Union[str, Any]     ,    a__:						List[str]     ,    a__:						Any=True     ,    a__:						List[Any]="pt"			)    ->						List[str]:


							'''simple docstring'''





							_UpperCAmelCase											=     {'add_prefix_space': True} if isinstance(a__     ,    a__			) and not line.startswith(' '			) else {}
							_UpperCAmelCase											=     padding_side
							return tokenizer(
							    [line]     ,    max_length=a__     ,    padding='max_length' if pad_to_max_length else None     ,    truncation=a__     ,    return_tensors=a__     ,    add_special_tokens=a__     ,    **a__     ,    )

def 			lowerCAmelCase__					(     a__:						Union[str, Any]     ,    a__:						str     ,    a__:						int=None     ,    )    ->						Tuple:


							'''simple docstring'''





							_UpperCAmelCase											=     input_ids.ne(a__			).any(dim=0			)
							if attention_mask is None:
														return input_ids[:, keep_column_mask]
							else:
														return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])

class 						__a (							UpperCAmelCase     ):
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE="train"     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=""     ,       )					->			Tuple:




									"""simple docstring"""





									super().__init__()
									_UpperCAmelCase											=     Path(_SCREAMING_SNAKE_CASE		).joinpath(type_path + '.source'		)
									_UpperCAmelCase											=     Path(_SCREAMING_SNAKE_CASE		).joinpath(type_path + '.target'		)
									_UpperCAmelCase											=     self.get_char_lens(self.src_file		)
									_UpperCAmelCase											=     max_source_length
									_UpperCAmelCase											=     max_target_length
									assert min(self.src_lens		) > 0, f'''found empty line in {self.src_file}'''
									_UpperCAmelCase											=     tokenizer
									_UpperCAmelCase											=     prefix
									if n_obs is not None:
																_UpperCAmelCase											=     self.src_lens[:n_obs]
									_UpperCAmelCase											=     src_lang
									_UpperCAmelCase											=     tgt_lang
		def __len__(     self		)					->			List[Any]:




									"""simple docstring"""





									return len(self.src_lens		)
		def __getitem__(     self     ,       _SCREAMING_SNAKE_CASE		)					->			Dict[str, torch.Tensor]:




									"""simple docstring"""





									_UpperCAmelCase											=     index + 1  # linecache starts at 1
									_UpperCAmelCase											=     self.prefix + linecache.getline(str(self.src_file		)     ,       _SCREAMING_SNAKE_CASE		).rstrip('\n'		)
									_UpperCAmelCase											=     linecache.getline(str(self.tgt_file		)     ,       _SCREAMING_SNAKE_CASE		).rstrip('\n'		)
									assert source_line, f'''empty source line for index {index}'''
									assert tgt_line, f'''empty tgt line for index {index}'''

									# Need to add eos token manually for T5
									if isinstance(self.tokenizer     ,       _SCREAMING_SNAKE_CASE		):
																source_line += self.tokenizer.eos_token
																tgt_line += self.tokenizer.eos_token

									# Pad source and target to the right
									_UpperCAmelCase											=     (
									    self.tokenizer.question_encoder if isinstance(self.tokenizer     ,       _SCREAMING_SNAKE_CASE		) else self.tokenizer
									)
									_UpperCAmelCase											=     self.tokenizer.generator if isinstance(self.tokenizer     ,       _SCREAMING_SNAKE_CASE		) else self.tokenizer

									_UpperCAmelCase											=     encode_line(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       self.max_source_length     ,       'right'		)
									_UpperCAmelCase											=     encode_line(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       self.max_target_length     ,       'right'		)

									_UpperCAmelCase											=     source_inputs['input_ids'].squeeze()
									_UpperCAmelCase											=     target_inputs['input_ids'].squeeze()
									_UpperCAmelCase											=     source_inputs['attention_mask'].squeeze()
									return {
									    "input_ids": source_ids,
									    "attention_mask": src_mask,
									    "decoder_input_ids": target_ids,
									}
		@staticmethod
		def       UpperCAmelCase__							(     _SCREAMING_SNAKE_CASE		)					->			List[str]:




									"""simple docstring"""





									return [len(_SCREAMING_SNAKE_CASE		) for x in Path(_SCREAMING_SNAKE_CASE		).open().readlines()]






		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			Dict[str, torch.Tensor]:




									"""simple docstring"""





									_UpperCAmelCase											=     torch.stack([x['input_ids'] for x in batch]		)
									_UpperCAmelCase											=     torch.stack([x['attention_mask'] for x in batch]		)
									_UpperCAmelCase											=     torch.stack([x['decoder_input_ids'] for x in batch]		)
									_UpperCAmelCase											=     (
									    self.tokenizer.generator.pad_token_id
									    if isinstance(self.tokenizer     ,       _SCREAMING_SNAKE_CASE		)
									    else self.tokenizer.pad_token_id
									)
									_UpperCAmelCase											=     (
									    self.tokenizer.question_encoder.pad_token_id
									    if isinstance(self.tokenizer     ,       _SCREAMING_SNAKE_CASE		)
									    else self.tokenizer.pad_token_id
									)
									_UpperCAmelCase											=     trim_batch(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase						,			_UpperCAmelCase											=     trim_batch(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     {
									    'input_ids': source_ids,
									    'attention_mask': source_mask,
									    'decoder_input_ids': y,
									}
									return batch



lowerCAmelCase__    :int       =				getLogger(__name__)

def 			lowerCAmelCase__					(     a__:						List[List]			)    ->						List[str]:


							'''simple docstring'''





							return list(itertools.chain.from_iterable(a__			)			)

def 			lowerCAmelCase__					(     a__:						str			)    ->						None:


							'''simple docstring'''





							_UpperCAmelCase											=     get_git_info()
							save_json(a__     ,    os.path.join(a__     ,    'git_log.json'			)			)

def 			lowerCAmelCase__					(     a__:						Optional[Any]     ,    a__:						Optional[Any]     ,    a__:						Dict=4     ,    **a__:						Optional[Any]			)    ->						Dict:


							'''simple docstring'''





							with open(a__     ,    'w'			) as f:
														json.dump(a__     ,    a__     ,    indent=a__     ,    **a__			)

def 			lowerCAmelCase__					(     a__:						str			)    ->						Any:


							'''simple docstring'''





							with open(a__			) as f:
														return json.load(a__			)

def 			lowerCAmelCase__					(     )    ->						Any:


							'''simple docstring'''





							_UpperCAmelCase											=     git.Repo(search_parent_directories=a__			)
							_UpperCAmelCase											=     {
							    'repo_id': str(a__			),
							    'repo_sha': str(repo.head.object.hexsha			),
							    'repo_branch': str(repo.active_branch			),
							    'hostname': str(socket.gethostname()			),
							}
							return repo_infos

def 			lowerCAmelCase__					(     a__:						Callable     ,    a__:						Iterable			)    ->						List:


							'''simple docstring'''





							return list(map(a__     ,    a__			)			)

def 			lowerCAmelCase__					(     a__:						Optional[Any]     ,    a__:						List[Any]			)    ->						Optional[int]:


							'''simple docstring'''





							with open(a__     ,    'wb'			) as f:
														return pickle.dump(a__     ,    a__			)

def 			lowerCAmelCase__					(     a__:						Tuple			)    ->						Any:


							'''simple docstring'''





							def remove_articles(a__:						Tuple			):
														return re.sub(R'\b(a|an|the)\b'     ,    ' '     ,    a__			)

							def white_space_fix(a__:						List[str]			):
														return " ".join(text.split()			)

							def remove_punc(a__:						Tuple			):
														_UpperCAmelCase											=     set(string.punctuation			)
														return "".join(ch for ch in text if ch not in exclude			)

							def lower(a__:						Tuple			):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(a__			)			)			)			)

def 			lowerCAmelCase__					(     a__:						List[str]     ,    a__:						Optional[int]			)    ->						int:


							'''simple docstring'''





							_UpperCAmelCase											=     normalize_answer(a__			).split()
							_UpperCAmelCase											=     normalize_answer(a__			).split()
							_UpperCAmelCase											=     Counter(a__			) & Counter(a__			)
							_UpperCAmelCase											=     sum(common.values()			)
							if num_same == 0:
														return 0
							_UpperCAmelCase											=     1.0 * num_same / len(a__			)
							_UpperCAmelCase											=     1.0 * num_same / len(a__			)
							_UpperCAmelCase											=     (2 * precision * recall) / (precision + recall)
							return fa

def 			lowerCAmelCase__					(     a__:						str     ,    a__:						Tuple			)    ->						Tuple:


							'''simple docstring'''





							return normalize_answer(a__			) == normalize_answer(a__			)

def 			lowerCAmelCase__					(     a__:						List[str]     ,    a__:						List[str]			)    ->						Dict:


							'''simple docstring'''





							assert len(a__			) == len(a__			)
							_UpperCAmelCase											=     0
							for hypo, pred in zip(a__     ,    a__			):
														em += exact_match_score(a__     ,    a__			)
							if len(a__			) > 0:
														em /= len(a__			)
							return {"em": em}

def 			lowerCAmelCase__					(     a__:						int			)    ->						List[str]:


							'''simple docstring'''





							return model_prefix.startswith('rag'			)







def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						List[str]     ,    a__:						Any			)    ->						Tuple:


							'''simple docstring'''





							_UpperCAmelCase											=     {p: p for p in extra_params}
							# T5 models don't have `dropout` param, they have `dropout_rate` instead
							_UpperCAmelCase											=     'dropout_rate'
							for p in extra_params:
														if getattr(a__     ,    a__     ,    a__			):
																					if not hasattr(a__     ,    a__			) and not hasattr(a__     ,    equivalent_param[p]			):
																												logger.info('config doesn\'t have a `{}` attribute'.format(a__			)			)
																												delattr(a__     ,    a__			)
																												continue
																					_UpperCAmelCase											=     p if hasattr(a__     ,    a__			) else equivalent_param[p]
																					setattr(a__     ,    a__     ,    getattr(a__     ,    a__			)			)
																					delattr(a__     ,    a__			)
							return hparams, config




from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


lowerCAmelCase__    :List[Any]       =				logging.get_logger(__name__)

lowerCAmelCase__    :int       =				{
    '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}

class 						__a (							UpperCAmelCase     ):
		_a      :    Dict           =      'blenderbot-small'
		_a      :    Tuple           =      ['past_key_values']
		_a      :    List[str]           =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=50265     ,       _SCREAMING_SNAKE_CASE=512     ,       _SCREAMING_SNAKE_CASE=8     ,       _SCREAMING_SNAKE_CASE=2048     ,       _SCREAMING_SNAKE_CASE=16     ,       _SCREAMING_SNAKE_CASE=8     ,       _SCREAMING_SNAKE_CASE=2048     ,       _SCREAMING_SNAKE_CASE=16     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE="gelu"     ,       _SCREAMING_SNAKE_CASE=512     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=0.02     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE=0     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE=2     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			Dict:




									"""simple docstring"""





									_UpperCAmelCase											=     vocab_size
									_UpperCAmelCase											=     max_position_embeddings
									_UpperCAmelCase											=     d_model
									_UpperCAmelCase											=     encoder_ffn_dim
									_UpperCAmelCase											=     encoder_layers
									_UpperCAmelCase											=     encoder_attention_heads
									_UpperCAmelCase											=     decoder_ffn_dim
									_UpperCAmelCase											=     decoder_layers
									_UpperCAmelCase											=     decoder_attention_heads
									_UpperCAmelCase											=     dropout
									_UpperCAmelCase											=     attention_dropout
									_UpperCAmelCase											=     activation_dropout
									_UpperCAmelCase											=     activation_function
									_UpperCAmelCase											=     init_std
									_UpperCAmelCase											=     encoder_layerdrop
									_UpperCAmelCase											=     decoder_layerdrop
									_UpperCAmelCase											=     use_cache
									_UpperCAmelCase											=     encoder_layers
									_UpperCAmelCase											=     scale_embedding  # scale factor will be sqrt(d_model) if True

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



class 						__a (							UpperCAmelCase     ):
		@property
		def       UpperCAmelCase__							(     self		)					->			Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





									if self.task in ["default", "seq2seq-lm"]:
																_UpperCAmelCase											=     OrderedDict(
																    [
																        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																    ]		)

																if self.use_past:
																							_UpperCAmelCase											=     {0: 'batch'}
																							_UpperCAmelCase											=     {0: 'batch', 1: 'past_decoder_sequence + sequence'}
																else:
																							_UpperCAmelCase											=     {0: 'batch', 1: 'decoder_sequence'}
																							_UpperCAmelCase											=     {0: 'batch', 1: 'decoder_sequence'}

																if self.use_past:
																							self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE     ,       direction='inputs'		)
									elif self.task == "causal-lm":
																# TODO: figure this case out.
																_UpperCAmelCase											=     OrderedDict(
																    [
																        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																    ]		)
																if self.use_past:
																							_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_layers
																							for i in range(_SCREAMING_SNAKE_CASE		):
																														_UpperCAmelCase											=     {0: 'batch', 2: 'past_sequence + sequence'}
																														_UpperCAmelCase											=     {0: 'batch', 2: 'past_sequence + sequence'}
									else:
																_UpperCAmelCase											=     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		)					->			Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





									if self.task in ["default", "seq2seq-lm"]:
																_UpperCAmelCase											=     super().outputs
									else:
																_UpperCAmelCase											=     super(_SCREAMING_SNAKE_CASE     ,       self		).outputs
																if self.use_past:
																							_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_layers
																							for i in range(_SCREAMING_SNAKE_CASE		):
																														_UpperCAmelCase											=     {0: 'batch', 2: 'past_sequence + sequence'}
																														_UpperCAmelCase											=     {0: 'batch', 2: 'past_sequence + sequence'}
									return common_outputs
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)

									# Generate decoder inputs
									_UpperCAmelCase											=     seq_length if not self.use_past else 1
									_UpperCAmelCase											=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
									_UpperCAmelCase											=     dict(**_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)

									if self.use_past:
																if not is_torch_available():
																							raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'		)
																else:
																							import torch
																_UpperCAmelCase						,			_UpperCAmelCase											=     common_inputs['input_ids'].shape
																_UpperCAmelCase											=     common_inputs['decoder_input_ids'].shape[1]
																_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_attention_heads
																_UpperCAmelCase											=     (
																    batch,
																    num_encoder_attention_heads,
																    encoder_seq_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)
																_UpperCAmelCase											=     decoder_seq_length + 3
																_UpperCAmelCase											=     (
																    batch,
																    num_decoder_attention_heads,
																    decoder_past_length,
																    self._config.hidden_size // num_decoder_attention_heads,
																)

																_UpperCAmelCase											=     torch.cat(
																    [common_inputs['decoder_attention_mask'], torch.ones(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)]     ,       dim=1		)

																_UpperCAmelCase											=     []
																# If the number of encoder and decoder layers are present in the model configuration, both are considered
																_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_layers
																_UpperCAmelCase											=     min(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     max(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		) - min_num_layers
																_UpperCAmelCase											=     'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

																for _ in range(_SCREAMING_SNAKE_CASE		):
																							common_inputs["past_key_values"].append(
																							    (
																							        torch.zeros(_SCREAMING_SNAKE_CASE		),
																							        torch.zeros(_SCREAMING_SNAKE_CASE		),
																							        torch.zeros(_SCREAMING_SNAKE_CASE		),
																							        torch.zeros(_SCREAMING_SNAKE_CASE		),
																							    )		)
																# TODO: test this.
																_UpperCAmelCase											=     encoder_shape if remaining_side_name == 'encoder' else decoder_shape
																for _ in range(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		):
																							common_inputs["past_key_values"].append((torch.zeros(_SCREAMING_SNAKE_CASE		), torch.zeros(_SCREAMING_SNAKE_CASE		))		)
									return common_inputs
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)

									if self.use_past:
																if not is_torch_available():
																							raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'		)
																else:
																							import torch
																_UpperCAmelCase						,			_UpperCAmelCase											=     common_inputs['input_ids'].shape
																# Not using the same length for past_key_values
																_UpperCAmelCase											=     seqlen + 2
																_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_layers
																_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_attention_heads
																_UpperCAmelCase											=     (
																    batch,
																    num_encoder_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)

																_UpperCAmelCase											=     common_inputs['attention_mask'].dtype
																_UpperCAmelCase											=     torch.cat(
																    [common_inputs['attention_mask'], torch.ones(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       dtype=_SCREAMING_SNAKE_CASE		)]     ,       dim=1		)
																_UpperCAmelCase											=     [
																    (torch.zeros(_SCREAMING_SNAKE_CASE		), torch.zeros(_SCREAMING_SNAKE_CASE		)) for _ in range(_SCREAMING_SNAKE_CASE		)
																]
									return common_inputs
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     compute_effective_axis_dimension(
									    _SCREAMING_SNAKE_CASE     ,       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
									_UpperCAmelCase											=     tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     compute_effective_axis_dimension(
									    _SCREAMING_SNAKE_CASE     ,       fixed_dimension=OnnxConfig.default_fixed_sequence     ,       num_token_to_add=_SCREAMING_SNAKE_CASE		)

									# Generate dummy inputs according to compute batch and sequence
									_UpperCAmelCase											=     [' '.join([tokenizer.unk_token]		) * seq_length] * batch_size
									_UpperCAmelCase											=     dict(tokenizer(_SCREAMING_SNAKE_CASE     ,       return_tensors=_SCREAMING_SNAKE_CASE		)		)
									return common_inputs
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									if self.task in ["default", "seq2seq-lm"]:
																_UpperCAmelCase											=     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																    _SCREAMING_SNAKE_CASE     ,       batch_size=_SCREAMING_SNAKE_CASE     ,       seq_length=_SCREAMING_SNAKE_CASE     ,       is_pair=_SCREAMING_SNAKE_CASE     ,       framework=_SCREAMING_SNAKE_CASE		)

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

									return common_inputs






		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			List[str]:




									"""simple docstring"""





									if self.task in ["default", "seq2seq-lm"]:
																_UpperCAmelCase											=     super()._flatten_past_key_values_(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
									else:
																_UpperCAmelCase											=     super(_SCREAMING_SNAKE_CASE     ,       self		)._flatten_past_key_values_(
																    _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
