






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      lowercase__      (     __snake_case    :							Optional[Any]     ,       __snake_case    :							str     ,       __snake_case    :							Dict     ,       __snake_case    :							int     ,       __snake_case    :							str=True     ,       __snake_case    :							Optional[Any]="pt"  ):


					'''simple docstring'''


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




def      lowercase__      (     __snake_case    :							int     ,       __snake_case    :							Tuple     ,       __snake_case    :							str=None     ,       ):


					'''simple docstring'''


					UpperCAmelCase_    :      str            =						input_ids.ne(__snake_case  ).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   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase="train"				,		_UpperCamelCase=None				,		_UpperCamelCase=None				,		_UpperCamelCase=None				,		_UpperCamelCase=""				,		)	->							Any:
							super().__init__()
							UpperCAmelCase_    :      Optional[Any]            =						Path(_UpperCamelCase   ).joinpath(type_path + '.source'   )
							UpperCAmelCase_    :      Dict            =						Path(_UpperCamelCase   ).joinpath(type_path + '.target'   )
							UpperCAmelCase_    :      Optional[Any]            =						self.get_char_lens(self.src_file   )
							UpperCAmelCase_    :      Dict            =						max_source_length
							UpperCAmelCase_    :      List[Any]            =						max_target_length
							assert min(self.src_lens   ) > 0, f"found empty line in {self.src_file}"
							UpperCAmelCase_    :      List[Any]            =						tokenizer
							UpperCAmelCase_    :      Tuple            =						prefix
							if n_obs is not None:
												UpperCAmelCase_    :      int            =						self.src_lens[:n_obs]
							UpperCAmelCase_    :      Optional[int]            =						src_lang
							UpperCAmelCase_    :      List[str]            =						tgt_lang



		def __len__(							self   )	->							Dict:
							return len(self.src_lens   )



		def __getitem__(							self				,		_UpperCamelCase   )	->							Dict[str, torch.Tensor]:
							UpperCAmelCase_    :      Union[str, Any]            =						index + 1  # linecache starts at 1
							UpperCAmelCase_    :      Any            =						self.prefix + linecache.getline(str(self.src_file   )				,		_UpperCamelCase   ).rstrip('\n'   )
							UpperCAmelCase_    :      Optional[int]            =						linecache.getline(str(self.tgt_file   )				,		_UpperCamelCase   ).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				,		_UpperCamelCase   ):
												source_line += self.tokenizer.eos_token
												tgt_line += self.tokenizer.eos_token

							# Pad source and target to the right
							UpperCAmelCase_    :      str            =						(
							    self.tokenizer.question_encoder if isinstance(self.tokenizer				,		_UpperCamelCase   ) else self.tokenizer
							)
							UpperCAmelCase_    :      Optional[Any]            =						self.tokenizer.generator if isinstance(self.tokenizer				,		_UpperCamelCase   ) else self.tokenizer

							UpperCAmelCase_    :      str            =						encode_line(_UpperCamelCase				,		_UpperCamelCase				,		self.max_source_length				,		'right'   )
							UpperCAmelCase_    :      List[Any]            =						encode_line(_UpperCamelCase				,		_UpperCamelCase				,		self.max_target_length				,		'right'   )

							UpperCAmelCase_    :      Optional[Any]            =						source_inputs['input_ids'].squeeze()
							UpperCAmelCase_    :      Any            =						target_inputs['input_ids'].squeeze()
							UpperCAmelCase_    :      int            =						source_inputs['attention_mask'].squeeze()
							return {
							    "input_ids": source_ids,
							    "attention_mask": src_mask,
							    "decoder_input_ids": target_ids,
							}



		@staticmethod
		def 					__UpperCAmelCase			(							_UpperCamelCase   )	->							Optional[int]:
							return [len(_UpperCamelCase   ) for x in Path(_UpperCamelCase   ).open().readlines()]



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Dict[str, torch.Tensor]:
							UpperCAmelCase_    :      List[str]            =						torch.stack([x['input_ids'] for x in batch]   )
							UpperCAmelCase_    :      int            =						torch.stack([x['attention_mask'] for x in batch]   )
							UpperCAmelCase_    :      Tuple            =						torch.stack([x['decoder_input_ids'] for x in batch]   )
							UpperCAmelCase_    :      Tuple            =						(
							    self.tokenizer.generator.pad_token_id
							    if isinstance(self.tokenizer				,		_UpperCamelCase   )
							    else self.tokenizer.pad_token_id
							)
							UpperCAmelCase_    :      str            =						(
							    self.tokenizer.question_encoder.pad_token_id
							    if isinstance(self.tokenizer				,		_UpperCamelCase   )
							    else self.tokenizer.pad_token_id
							)
							UpperCAmelCase_    :      Union[str, Any]            =						trim_batch(_UpperCamelCase				,		_UpperCamelCase   )
							UpperCAmelCase_       ,  UpperCAmelCase_    :      Union[str, Any]            =						trim_batch(_UpperCamelCase				,		_UpperCamelCase				,		attention_mask=_UpperCamelCase   )
							UpperCAmelCase_    :      List[Any]            =						{
							    'input_ids': source_ids,
							    'attention_mask': source_mask,
							    'decoder_input_ids': y,
							}
							return batch





__UpperCAmelCase								=						getLogger(__name__)




def      lowercase__      (     __snake_case    :							List[List]  ):


					'''simple docstring'''


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




def      lowercase__      (     __snake_case    :							str  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Union[str, Any]            =						get_git_info()
					save_json(__snake_case     ,       os.path.join(__snake_case     ,       'git_log.json'  )  )




def      lowercase__      (     __snake_case    :							Optional[int]     ,       __snake_case    :							int     ,       __snake_case    :							List[Any]=4     ,       **__snake_case    :							Optional[Any]  ):


					'''simple docstring'''


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




def      lowercase__      (     __snake_case    :							Optional[Any]  ):


					'''simple docstring'''


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




def      lowercase__      (     ):


					'''simple docstring'''


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




def      lowercase__      (     __snake_case    :							Callable     ,       __snake_case    :							Iterable  ):


					'''simple docstring'''


					return list(map(__snake_case     ,       __snake_case  )  )




def      lowercase__      (     __snake_case    :							Tuple     ,       __snake_case    :							Any  ):


					'''simple docstring'''


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




def      lowercase__      (     __snake_case    :							Tuple  ):


					'''simple docstring'''


					def remove_articles(__snake_case    :							Optional[int]  ):
										return re.sub(R'\b(a|an|the)\b'     ,       ' '     ,       __snake_case  )

					def white_space_fix(__snake_case    :							Any  ):
										return " ".join(text.split()  )

					def remove_punc(__snake_case    :							int  ):
										UpperCAmelCase_    :      Optional[int]            =						set(string.punctuation  )
										return "".join(ch for ch in text if ch not in exclude  )

					def lower(__snake_case    :							int  ):
										return text.lower()

					return white_space_fix(remove_articles(remove_punc(lower(__snake_case  )  )  )  )




def      lowercase__      (     __snake_case    :							Union[str, Any]     ,       __snake_case    :							Optional[Any]  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Any            =						normalize_answer(__snake_case  ).split()
					UpperCAmelCase_    :      str            =						normalize_answer(__snake_case  ).split()
					UpperCAmelCase_    :      Tuple            =						Counter(__snake_case  ) & Counter(__snake_case  )
					UpperCAmelCase_    :      Optional[Any]            =						sum(common.values()  )
					if num_same == 0:
										return 0
					UpperCAmelCase_    :      List[Any]            =						1.0 * num_same / len(__snake_case  )
					UpperCAmelCase_    :      Tuple            =						1.0 * num_same / len(__snake_case  )
					UpperCAmelCase_    :      List[Any]            =						(2 * precision * recall) / (precision + recall)
					return fa




def      lowercase__      (     __snake_case    :							Optional[Any]     ,       __snake_case    :							str  ):


					'''simple docstring'''


					return normalize_answer(__snake_case  ) == normalize_answer(__snake_case  )




def      lowercase__      (     __snake_case    :							List[str]     ,       __snake_case    :							List[str]  ):


					'''simple docstring'''


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




def      lowercase__      (     __snake_case    :							List[Any]  ):


					'''simple docstring'''


					return model_prefix.startswith('rag'  )






def      lowercase__      (     __snake_case    :							Union[str, Any]     ,       __snake_case    :							List[str]     ,       __snake_case    :							Tuple  ):


					'''simple docstring'''


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










'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__    					=      {
    '''configuration_distilbert''': [
        '''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DistilBertConfig''',
        '''DistilBertOnnxConfig''',
    ],
    '''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      ['''DistilBertTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''DistilBertForMaskedLM''',
      '''DistilBertForMultipleChoice''',
      '''DistilBertForQuestionAnswering''',
      '''DistilBertForSequenceClassification''',
      '''DistilBertForTokenClassification''',
      '''DistilBertModel''',
      '''DistilBertPreTrainedModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFDistilBertForMaskedLM''',
      '''TFDistilBertForMultipleChoice''',
      '''TFDistilBertForQuestionAnswering''',
      '''TFDistilBertForSequenceClassification''',
      '''TFDistilBertForTokenClassification''',
      '''TFDistilBertMainLayer''',
      '''TFDistilBertModel''',
      '''TFDistilBertPreTrainedModel''',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''FlaxDistilBertForMaskedLM''',
      '''FlaxDistilBertForMultipleChoice''',
      '''FlaxDistilBertForQuestionAnswering''',
      '''FlaxDistilBertForSequenceClassification''',
      '''FlaxDistilBertForTokenClassification''',
      '''FlaxDistilBertModel''',
      '''FlaxDistilBertPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_distilbert import (
      DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DistilBertConfig,
      DistilBertOnnxConfig,
  )
  from .tokenization_distilbert import DistilBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_distilbert_fast import DistilBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_distilbert import (
        DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DistilBertForMaskedLM,
        DistilBertForMultipleChoice,
        DistilBertForQuestionAnswering,
        DistilBertForSequenceClassification,
        DistilBertForTokenClassification,
        DistilBertModel,
        DistilBertPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_distilbert import (
        TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDistilBertForMaskedLM,
        TFDistilBertForMultipleChoice,
        TFDistilBertForQuestionAnswering,
        TFDistilBertForSequenceClassification,
        TFDistilBertForTokenClassification,
        TFDistilBertMainLayer,
        TFDistilBertModel,
        TFDistilBertPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_distilbert import (
        FlaxDistilBertForMaskedLM,
        FlaxDistilBertForMultipleChoice,
        FlaxDistilBertForQuestionAnswering,
        FlaxDistilBertForSequenceClassification,
        FlaxDistilBertForTokenClassification,
        FlaxDistilBertModel,
        FlaxDistilBertPreTrainedModel,
    )

else:
  import sys

  UpperCamelCase__    					=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
