






'''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 	_a(					UpperCamelCase__			:  Optional[int],		UpperCamelCase__			:  Optional[Any],		UpperCamelCase__			:  List[str],		UpperCamelCase__			:  Dict,		UpperCamelCase__			:  Dict=True,		UpperCamelCase__			:  str="pt"							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Tuple													={'''add_prefix_space''': True} if isinstance(UpperCamelCase__,		UpperCamelCase__							) and not line.startswith(''' '''							) else {}
      SCREAMING_SNAKE_CASE__       :			Any													=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 	_a(					UpperCamelCase__			:  Dict,		UpperCamelCase__			:  List[Any],		UpperCamelCase__			:  Optional[int]=None,		):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			int													=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        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




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




 def __len__( self		:							Any )		->  Optional[Any]:
       return len(self.src_lens )




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

       # Pad source and target to the right
       SCREAMING_SNAKE_CASE__       :			Tuple													=(
           self.tokenizer.question_encoder if isinstance(self.tokenizer   , __lowercase ) else self.tokenizer
       )
       SCREAMING_SNAKE_CASE__       :			Tuple													=self.tokenizer.generator if isinstance(self.tokenizer   , __lowercase ) else self.tokenizer

       SCREAMING_SNAKE_CASE__       :			Any													=encode_line(__lowercase   , __lowercase   , self.max_source_length   , '''right''' )
       SCREAMING_SNAKE_CASE__       :			Dict													=encode_line(__lowercase   , __lowercase   , self.max_target_length   , '''right''' )

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




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




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


a_					=	getLogger(__name__)





def 	_a(					UpperCamelCase__			:  List[List]							):




      '''simple docstring'''






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





def 	_a(					UpperCamelCase__			:  str							):




      '''simple docstring'''






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





def 	_a(					UpperCamelCase__			:  Tuple,		UpperCamelCase__			:  int,		UpperCamelCase__			:  List[Any]=4,		**UpperCamelCase__			:  Tuple							):




      '''simple docstring'''






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





def 	_a(					UpperCamelCase__			:  Optional[int]							):




      '''simple docstring'''






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





def 	_a(					):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Dict													=git.Repo(search_parent_directories=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													={
          '''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 	_a(					UpperCamelCase__			:  Callable,		UpperCamelCase__			:  Iterable							):




      '''simple docstring'''






      return list(map(UpperCamelCase__,		UpperCamelCase__							)							)





def 	_a(					UpperCamelCase__			:  List[Any],		UpperCamelCase__			:  str							):




      '''simple docstring'''






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





def 	_a(					UpperCamelCase__			:  int							):




      '''simple docstring'''






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

      def white_space_fix(UpperCamelCase__			:  Tuple							):
            return " ".join(text.split()							)

      def remove_punc(UpperCamelCase__			:  Union[str, Any]							):
            SCREAMING_SNAKE_CASE__       :			List[Any]													=set(string.punctuation							)
            return "".join(ch for ch in text if ch not in exclude							)

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

      return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__							)							)							)							)





def 	_a(					UpperCamelCase__			:  List[Any],		UpperCamelCase__			:  List[str]							):




      '''simple docstring'''






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





def 	_a(					UpperCamelCase__			:  str,		UpperCamelCase__			:  str							):




      '''simple docstring'''






      return normalize_answer(UpperCamelCase__							) == normalize_answer(UpperCamelCase__							)





def 	_a(					UpperCamelCase__			:  List[str],		UpperCamelCase__			:  List[str]							):




      '''simple docstring'''






      assert len(UpperCamelCase__							) == len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=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 	_a(					UpperCamelCase__			:  Dict							):




      '''simple docstring'''






      return model_prefix.startswith('''rag'''							)







def 	_a(					UpperCamelCase__			:  Optional[Any],		UpperCamelCase__			:  Optional[int],		UpperCamelCase__			:  Tuple							):




      '''simple docstring'''






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







'''simple docstring'''
from __future__ import annotations





def 	_a(					UpperCamelCase__			:  List[str],		UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  int,		UpperCamelCase__			:  List[str]							):  # noqa: E741




      '''simple docstring'''






      while r - l > 1:
            SCREAMING_SNAKE_CASE__       :			List[str]													=(l + r) // 2
            if v[m] >= key:
                  SCREAMING_SNAKE_CASE__       :			Dict													=m
            else:
                  SCREAMING_SNAKE_CASE__       :			Any													=m  # noqa: E741
      return r





def 	_a(					UpperCamelCase__			:  list[int]							):




      '''simple docstring'''






      if len(UpperCamelCase__							) == 0:
            return 0

      SCREAMING_SNAKE_CASE__       :			List[Any]													=[0] * len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Dict													=1

      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=v[0]

      for i in range(1,		len(UpperCamelCase__							)							):
            if v[i] < tail[0]:
                  SCREAMING_SNAKE_CASE__       :			List[Any]													=v[i]
            elif v[i] > tail[length - 1]:
                  SCREAMING_SNAKE_CASE__       :			int													=v[i]
                  length += 1
            else:
                  SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=v[i]

      return length


if __name__ == "__main__":
  import doctest

  doctest.testmod()