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 	__A		(      _A				,					_A				,					_A				,					_A				,					_A=True				,					_A="pt"       ):



 """simple docstring"""


 __a							=	{"add_prefix_space": True} if isinstance(_A				,					_A       ) and not line.startswith(" "       ) else {}
 __a							=	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 	__A		(      _A				,					_A				,					_A=None				,					):



 """simple docstring"""


 __a							=	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_			(							a_			):

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

  def __len__(		self			:   Optional[int]							):
   return len(self.src_lens							)

  def __getitem__(		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   List[str]							):
   __a							=	index + 1  # linecache starts at 1
   __a							=	self.prefix + linecache.getline(str(self.src_file							)  ,				__SCREAMING_SNAKE_CASE							).rstrip("\n"							)
   __a							=	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
   __a							=	(
       self.tokenizer.question_encoder if isinstance(self.tokenizer  ,				__SCREAMING_SNAKE_CASE							) else self.tokenizer
   )
   __a							=	self.tokenizer.generator if isinstance(self.tokenizer  ,				__SCREAMING_SNAKE_CASE							) else self.tokenizer

   __a							=	encode_line(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				self.max_source_length  ,				"right"							)
   __a							=	encode_line(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				self.max_target_length  ,				"right"							)

   __a							=	source_inputs["input_ids"].squeeze()
   __a							=	target_inputs["input_ids"].squeeze()
   __a							=	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[Any]							):
   return [len(__SCREAMING_SNAKE_CASE							) for x in Path(__SCREAMING_SNAKE_CASE							).open().readlines()]

  def 		_UpperCAmelCase (		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   List[Any]							):
   __a							=	torch.stack([x["input_ids"] for x in batch]							)
   __a							=	torch.stack([x["attention_mask"] for x in batch]							)
   __a							=	torch.stack([x["decoder_input_ids"] for x in batch]							)
   __a							=	(
       self.tokenizer.generator.pad_token_id
       if isinstance(self.tokenizer  ,				__SCREAMING_SNAKE_CASE							)
       else self.tokenizer.pad_token_id
   )
   __a							=	(
       self.tokenizer.question_encoder.pad_token_id
       if isinstance(self.tokenizer  ,				__SCREAMING_SNAKE_CASE							)
       else self.tokenizer.pad_token_id
   )
   __a							=	trim_batch(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
   __a							,  __a							=	trim_batch(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				attention_mask=__SCREAMING_SNAKE_CASE							)
   __a							=	{
       "input_ids": source_ids,
       "attention_mask": source_mask,
       "decoder_input_ids": y,
   }
   return batch
SCREAMING_SNAKE_CASE			:						List[Any]				  =		getLogger(__name__)



def 	__A		(      _A       ):



 """simple docstring"""


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



def 	__A		(      _A       ):



 """simple docstring"""


 __a							=	get_git_info()
 save_json(_A				,					os.path.join(_A				,					"git_log.json"       )       )



def 	__A		(      _A				,					_A				,					_A=4				,					**_A       ):



 """simple docstring"""


 with open(_A				,					"w"       ) as f:
  json.dump(_A				,					_A				,					indent=_A				,					**_A       )



def 	__A		(      _A       ):



 """simple docstring"""


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



def 	__A		(      ):



 """simple docstring"""


 __a							=	git.Repo(search_parent_directories=_A       )
 __a							=	{
     "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 	__A		(      _A				,					_A       ):



 """simple docstring"""


 return list(map(_A				,					_A       )       )



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 with open(_A				,					"wb"       ) as f:
  return pickle.dump(_A				,					_A       )



def 	__A		(      _A       ):



 """simple docstring"""


 def remove_articles(_A       ):
  return re.sub(r"\b(a|an|the)\b"				,					" "				,					_A       )

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

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

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

 return white_space_fix(remove_articles(remove_punc(lower(_A       )       )       )       )



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


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



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 return normalize_answer(_A       ) == normalize_answer(_A       )



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 assert len(_A       ) == len(_A       )
 __a							=	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 	__A		(      _A       ):



 """simple docstring"""


 return model_prefix.startswith("rag"       )






def 	__A		(      _A				,					_A				,					_A       ):



 """simple docstring"""


 __a							=	{p: p for p in extra_params}
 # T5 models don't have `dropout` param, they have `dropout_rate` instead
 __a							=	"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
   __a							=	p if hasattr(_A				,					_A       ) else equivalent_param[p]
   setattr(_A				,					_A				,					getattr(_A				,					_A       )       )
   delattr(_A				,					_A       )
 return hparams, config



from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE			:						Optional[int]				  =		{
    """configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
    """feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
    """processing_mctct""": ["""MCTCTProcessor"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		[
      """MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MCTCTForCTC""",
      """MCTCTModel""",
      """MCTCTPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
  from .feature_extraction_mctct import MCTCTFeatureExtractor
  from .processing_mctct import MCTCTProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
  import sys

  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


