
'''simple docstring'''


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  __snake_case     (    _UpperCAmelCase					:       int,      _UpperCAmelCase					:       Dict,      _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       int,      _UpperCAmelCase					:       int=True,      _UpperCAmelCase					:       Tuple="pt"):
   UpperCamelCase       =  {'''add_prefix_space''': True} if isinstance(snake_case__,      snake_case__) and not line.startswith(''' ''') else {}
   UpperCamelCase       =  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  __snake_case     (    _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       int,      _UpperCAmelCase					:       Optional[Any]=None,      ):
   UpperCamelCase       =  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 						lowercase__	(  __lowerCAmelCase   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__="train" ,   lowerCamelCase__=None ,   lowerCamelCase__=None ,   lowerCamelCase__=None ,   lowerCamelCase__="" ,   ):

      '''simple docstring'''







      super().__init__()
      UpperCamelCase       =  Path(lowerCAmelCase_   ).joinpath(type_path + '''.source'''   )
      UpperCamelCase       =  Path(lowerCAmelCase_   ).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   ):

      '''simple docstring'''







      return len(self.src_lens   )







   def __getitem__(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  index + 1  # linecache starts at 1
      UpperCamelCase       =  self.prefix + linecache.getline(str(self.src_file   ) ,   lowerCAmelCase_   ).rstrip('''\n'''   )
      UpperCamelCase       =  linecache.getline(str(self.tgt_file   ) ,   lowerCAmelCase_   ).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 ,   lowerCAmelCase_   ):
         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 ,   lowerCAmelCase_   ) else self.tokenizer
      )
      UpperCamelCase       =  self.tokenizer.generator if isinstance(self.tokenizer ,   lowerCAmelCase_   ) else self.tokenizer

      UpperCamelCase       =  encode_line(lowerCAmelCase_ ,   lowerCAmelCase_ ,   self.max_source_length ,   '''right'''   )
      UpperCamelCase       =  encode_line(lowerCAmelCase_ ,   lowerCAmelCase_ ,   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				(			lowerCamelCase__   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''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 ,   lowerCAmelCase_   )
          else self.tokenizer.pad_token_id
      )
      UpperCamelCase       =  (
          self.tokenizer.question_encoder.pad_token_id
          if isinstance(self.tokenizer ,   lowerCAmelCase_   )
          else self.tokenizer.pad_token_id
      )
      UpperCamelCase       =  trim_batch(lowerCAmelCase_ ,   lowerCAmelCase_   )
      UpperCamelCase     , UpperCamelCase       =  trim_batch(lowerCAmelCase_ ,   lowerCAmelCase_ ,   attention_mask=lowerCAmelCase_   )
      UpperCamelCase       =  {
          '''input_ids''': source_ids,
          '''attention_mask''': source_mask,
          '''decoder_input_ids''': y,
      }
      return batch




snake_case_ :  List[str]     =       getLogger(__name__)



def  __snake_case     (    _UpperCAmelCase					:       List[List]):
   return list(itertools.chain.from_iterable(snake_case__))



def  __snake_case     (    _UpperCAmelCase					:       str):
   UpperCamelCase       =  get_git_info()
   save_json(snake_case__,      os.path.join(snake_case__,      '''git_log.json'''))



def  __snake_case     (    _UpperCAmelCase					:       Optional[Any],      _UpperCAmelCase					:       Union[str, Any],      _UpperCAmelCase					:       Dict=4,      **_UpperCAmelCase					:       Union[str, Any]):
   with open(snake_case__,      '''w''') as f:
      json.dump(snake_case__,      snake_case__,      indent=snake_case__,      **snake_case__)



def  __snake_case     (    _UpperCAmelCase					:       List[str]):
   with open(snake_case__) as f:
      return json.load(snake_case__)



def  __snake_case     (    ):
   UpperCamelCase       =  git.Repo(search_parent_directories=snake_case__)
   UpperCamelCase       =  {
       '''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  __snake_case     (    _UpperCAmelCase					:       Callable,      _UpperCAmelCase					:       Iterable):
   return list(map(snake_case__,      snake_case__))



def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       List[Any]):
   with open(snake_case__,      '''wb''') as f:
      return pickle.dump(snake_case__,      snake_case__)



def  __snake_case     (    _UpperCAmelCase					:       int):

   def remove_articles(_UpperCAmelCase					:       List[str]):
      return re.sub(R'''\b(a|an|the)\b''',      ''' ''',      snake_case__)

   def white_space_fix(_UpperCAmelCase					:       List[Any]):
      return " ".join(text.split())

   def remove_punc(_UpperCAmelCase					:       List[Any]):
      UpperCamelCase       =  set(string.punctuation)
      return "".join(ch for ch in text if ch not in exclude)

   def lower(_UpperCAmelCase					:       Optional[Any]):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(snake_case__))))



def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       Dict):
   UpperCamelCase       =  normalize_answer(snake_case__).split()
   UpperCamelCase       =  normalize_answer(snake_case__).split()
   UpperCamelCase       =  Counter(snake_case__) & Counter(snake_case__)
   UpperCamelCase       =  sum(common.values())
   if num_same == 0:
      return 0
   UpperCamelCase       =  1.0 * num_same / len(snake_case__)
   UpperCamelCase       =  1.0 * num_same / len(snake_case__)
   UpperCamelCase       =  (2 * precision * recall) / (precision + recall)
   return fa



def  __snake_case     (    _UpperCAmelCase					:       Union[str, Any],      _UpperCAmelCase					:       Optional[Any]):
   return normalize_answer(snake_case__) == normalize_answer(snake_case__)



def  __snake_case     (    _UpperCAmelCase					:       List[str],      _UpperCAmelCase					:       List[str]):
   assert len(snake_case__) == len(snake_case__)
   UpperCamelCase       =  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  __snake_case     (    _UpperCAmelCase					:       List[Any]):
   return model_prefix.startswith('''rag''')



def  __snake_case     (    _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       Optional[Any]):
   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(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       =  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_tf_available, is_torch_available


snake_case_ :  List[Any]     =					{
    'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
    'configuration_data2vec_text': [
        'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecTextConfig',
        'Data2VecTextOnnxConfig',
    ],
    'configuration_data2vec_vision': [
        'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecVisionConfig',
        'Data2VecVisionOnnxConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_ :  Union[str, Any]     =					[
       'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Data2VecAudioForAudioFrameClassification',
       'Data2VecAudioForCTC',
       'Data2VecAudioForSequenceClassification',
       'Data2VecAudioForXVector',
       'Data2VecAudioModel',
       'Data2VecAudioPreTrainedModel',
   ]
   snake_case_ :  str     =					[
       'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Data2VecTextForCausalLM',
       'Data2VecTextForMaskedLM',
       'Data2VecTextForMultipleChoice',
       'Data2VecTextForQuestionAnswering',
       'Data2VecTextForSequenceClassification',
       'Data2VecTextForTokenClassification',
       'Data2VecTextModel',
       'Data2VecTextPreTrainedModel',
   ]
   snake_case_ :  Optional[int]     =					[
       'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Data2VecVisionForImageClassification',
       'Data2VecVisionForMaskedImageModeling',
       'Data2VecVisionForSemanticSegmentation',
       'Data2VecVisionModel',
       'Data2VecVisionPreTrainedModel',
   ]

if is_tf_available():
   snake_case_ :  List[str]     =					[
       'TFData2VecVisionForImageClassification',
       'TFData2VecVisionForSemanticSegmentation',
       'TFData2VecVisionModel',
       'TFData2VecVisionPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
   from .configuration_dataavec_text import (
       DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DataaVecTextConfig,
       DataaVecTextOnnxConfig,
   )
   from .configuration_dataavec_vision import (
       DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DataaVecVisionConfig,
       DataaVecVisionOnnxConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_dataavec_audio import (
          DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
          DataaVecAudioForAudioFrameClassification,
          DataaVecAudioForCTC,
          DataaVecAudioForSequenceClassification,
          DataaVecAudioForXVector,
          DataaVecAudioModel,
          DataaVecAudioPreTrainedModel,
      )
      from .modeling_dataavec_text import (
          DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
          DataaVecTextForCausalLM,
          DataaVecTextForMaskedLM,
          DataaVecTextForMultipleChoice,
          DataaVecTextForQuestionAnswering,
          DataaVecTextForSequenceClassification,
          DataaVecTextForTokenClassification,
          DataaVecTextModel,
          DataaVecTextPreTrainedModel,
      )
      from .modeling_dataavec_vision import (
          DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
          DataaVecVisionForImageClassification,
          DataaVecVisionForMaskedImageModeling,
          DataaVecVisionForSemanticSegmentation,
          DataaVecVisionModel,
          DataaVecVisionPreTrainedModel,
      )
   if is_tf_available():
      from .modeling_tf_dataavec_vision import (
          TFDataaVecVisionForImageClassification,
          TFDataaVecVisionForSemanticSegmentation,
          TFDataaVecVisionModel,
          TFDataaVecVisionPreTrainedModel,
      )

else:
   import sys

   snake_case_ :  int     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





