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





def  SCREAMING_SNAKE_CASE							(  snake_case,   snake_case,   snake_case=None,   ):
     __snake_case				=				input_ids.ne(_UpperCamelCase).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       ):




    """simple docstring"""




    def __init__(		self				:  Any						,   A_				:  Optional[Any]						,   A_				:  Any						,   A_				:  Optional[int]						,   A_				:  Optional[Any]						,   A_				:  Dict="train"						,   A_				:  List[Any]=None						,   A_				:  Tuple=None						,   A_				:  Optional[int]=None						,   A_				:  Optional[int]=""						,   )     ->							Optional[int]:
         super().__init__()
         __snake_case				=				Path(A_     ).joinpath(type_path + '''.source'''     )
         __snake_case				=				Path(A_     ).joinpath(type_path + '''.target'''     )
         __snake_case				=				self.get_char_lens(self.src_file     )
         __snake_case				=				max_source_length
         __snake_case				=				max_target_length
         assert min(self.src_lens     ) > 0, f"found empty line in {self.src_file}"
         __snake_case				=				tokenizer
         __snake_case				=				prefix
         if n_obs is not None:
              __snake_case				=				self.src_lens[:n_obs]
         __snake_case				=				src_lang
         __snake_case				=				tgt_lang
    def __len__(		self				:  Dict     )     ->							Optional[int]:
         return len(self.src_lens     )
    def __getitem__(		self				:  str						,   A_				:  Union[str, Any]     )     ->							List[str]:
         __snake_case				=				index + 1  # linecache starts at 1
         __snake_case				=				self.prefix + linecache.getline(str(self.src_file     )						,   A_     ).rstrip('''\n'''     )
         __snake_case				=				linecache.getline(str(self.tgt_file     )						,   A_     ).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						,   A_     ):
              source_line += self.tokenizer.eos_token
              tgt_line += self.tokenizer.eos_token

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

         __snake_case				=				encode_line(A_						,   A_						,   self.max_source_length						,   '''right'''     )
         __snake_case				=				encode_line(A_						,   A_						,   self.max_target_length						,   '''right'''     )

         __snake_case				=				source_inputs["input_ids"].squeeze()
         __snake_case				=				target_inputs["input_ids"].squeeze()
         __snake_case				=				source_inputs["attention_mask"].squeeze()
         return {
             "input_ids": source_ids,
             "attention_mask": src_mask,
             "decoder_input_ids": target_ids,
         }
    @staticmethod
    def 			lowercase       (		A_				:  Dict     )     ->							int:
         return [len(A_     ) for x in Path(A_     ).open().readlines()]







    def 			lowercase       (		self				:  str						,   A_				:  Any     )     ->							Optional[int]:
         __snake_case				=				torch.stack([x['''input_ids'''] for x in batch]     )
         __snake_case				=				torch.stack([x['''attention_mask'''] for x in batch]     )
         __snake_case				=				torch.stack([x['''decoder_input_ids'''] for x in batch]     )
         __snake_case				=				(
             self.tokenizer.generator.pad_token_id
             if isinstance(self.tokenizer						,   A_     )
             else self.tokenizer.pad_token_id
         )
         __snake_case				=				(
             self.tokenizer.question_encoder.pad_token_id
             if isinstance(self.tokenizer						,   A_     )
             else self.tokenizer.pad_token_id
         )
         __snake_case				=				trim_batch(A_						,   A_     )
         __snake_case				=				trim_batch(A_						,   A_						,   attention_mask=A_     )
         __snake_case				=				{
             "input_ids": source_ids,
             "attention_mask": source_mask,
             "decoder_input_ids": y,
         }
         return batch






__lowercase		:		List[Any]								=				getLogger(__name__)





def  SCREAMING_SNAKE_CASE							(  snake_case):
     return list(itertools.chain.from_iterable(_UpperCamelCase))





def  SCREAMING_SNAKE_CASE							(  snake_case):
     __snake_case				=				get_git_info()
     save_json(_UpperCamelCase,   os.path.join(_UpperCamelCase,   '''git_log.json'''))





def  SCREAMING_SNAKE_CASE							(  snake_case,   snake_case,   snake_case=4,   **snake_case):




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





def  SCREAMING_SNAKE_CASE							(  snake_case):




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





def  SCREAMING_SNAKE_CASE							(  ):
     __snake_case				=				git.Repo(search_parent_directories=_UpperCamelCase)
     __snake_case				=				{
         "repo_id": str(_UpperCamelCase),
         "repo_sha": str(repo.head.object.hexsha),
         "repo_branch": str(repo.active_branch),
         "hostname": str(socket.gethostname()),
     }
     return repo_infos





def  SCREAMING_SNAKE_CASE							(  snake_case,   snake_case):
     return list(map(_UpperCamelCase,   _UpperCamelCase))





def  SCREAMING_SNAKE_CASE							(  snake_case,   snake_case):




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





def  SCREAMING_SNAKE_CASE							(  snake_case):




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

     def white_space_fix(snake_case):
          return " ".join(text.split())

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

     def lower(snake_case):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(_UpperCamelCase))))





def  SCREAMING_SNAKE_CASE							(  snake_case,   snake_case):
     __snake_case				=				normalize_answer(_UpperCamelCase).split()
     __snake_case				=				normalize_answer(_UpperCamelCase).split()
     __snake_case				=				Counter(_UpperCamelCase) & Counter(_UpperCamelCase)
     __snake_case				=				sum(common.values())
     if num_same == 0:
          return 0
     __snake_case				=				1.0 * num_same / len(_UpperCamelCase)
     __snake_case				=				1.0 * num_same / len(_UpperCamelCase)
     __snake_case				=				(2 * precision * recall) / (precision + recall)
     return fa





def  SCREAMING_SNAKE_CASE							(  snake_case,   snake_case):
     return normalize_answer(_UpperCamelCase) == normalize_answer(_UpperCamelCase)





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





def  SCREAMING_SNAKE_CASE							(  snake_case):
     return model_prefix.startswith('''rag''')





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







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

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


UpperCamelCase__			=      {
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      ['LayoutLMv3TokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      [
     'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
     'LayoutLMv3ForQuestionAnswering',
     'LayoutLMv3ForSequenceClassification',
     'LayoutLMv3ForTokenClassification',
     'LayoutLMv3Model',
     'LayoutLMv3PreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      [
     'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFLayoutLMv3ForQuestionAnswering',
     'TFLayoutLMv3ForSequenceClassification',
     'TFLayoutLMv3ForTokenClassification',
     'TFLayoutLMv3Model',
     'TFLayoutLMv3PreTrainedModel',
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      ['LayoutLMv3FeatureExtractor']
 UpperCamelCase__			=      ['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
 from .configuration_layoutlmva import (
     LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
     LayoutLMvaConfig,
     LayoutLMvaOnnxConfig,
 )
 from .processing_layoutlmva import LayoutLMvaProcessor
 from .tokenization_layoutlmva import LayoutLMvaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlmva import (
      LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMvaForQuestionAnswering,
      LayoutLMvaForSequenceClassification,
      LayoutLMvaForTokenClassification,
      LayoutLMvaModel,
      LayoutLMvaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_layoutlmva import (
      TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLayoutLMvaForQuestionAnswering,
      TFLayoutLMvaForSequenceClassification,
      TFLayoutLMvaForTokenClassification,
      TFLayoutLMvaModel,
      TFLayoutLMvaPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
  from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
 import sys

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




