




'''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       (      a_	:							int				,					a_	:							Any				,					a_	:							int				,					a_	:							List[str]				,					a_	:							Optional[Any]=True				,					a_	:							Optional[Any]="pt"			):
  __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 						SCREAMING_SNAKE_CASE       (      a_	:							int				,					a_	:							Optional[int]				,					a_	:							Tuple=None				,					):
  __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    __lowercase						(  __magic_name__ ):







   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase="train"      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=""      ,							)				->    Union[str, Any]:
     super().__init__()
     __a			=					Path(UpperCamelCase      ).joinpath(type_path + '.source'      )
     __a			=					Path(UpperCamelCase      ).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      )				->    str:
     return len(self.src_lens      )







   def __getitem__(	self      ,							UpperCamelCase      )				->    Dict[str, torch.Tensor]:
     __a			=					index + 1  # linecache starts at 1
     __a			=					self.prefix + linecache.getline(str(self.src_file      )      ,							UpperCamelCase      ).rstrip('\n'      )
     __a			=					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
     __a			=					(
         self.tokenizer.question_encoder if isinstance(self.tokenizer      ,							UpperCamelCase      ) else self.tokenizer
     )
     __a			=					self.tokenizer.generator if isinstance(self.tokenizer      ,							UpperCamelCase      ) else self.tokenizer

     __a			=					encode_line(UpperCamelCase      ,							UpperCamelCase      ,							self.max_source_length      ,							'right'      )
     __a			=					encode_line(UpperCamelCase      ,							UpperCamelCase      ,							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__			(	UpperCamelCase      )				->    Optional[Any]:
     return [len(UpperCamelCase      ) for x in Path(UpperCamelCase      ).open().readlines()]







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Dict[str, torch.Tensor]:
     __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      ,							UpperCamelCase      )
         else self.tokenizer.pad_token_id
     )
     __a			=					(
         self.tokenizer.question_encoder.pad_token_id
         if isinstance(self.tokenizer      ,							UpperCamelCase      )
         else self.tokenizer.pad_token_id
     )
     __a			=					trim_batch(UpperCamelCase      ,							UpperCamelCase      )
     __a	,       __a			=					trim_batch(UpperCamelCase      ,							UpperCamelCase      ,							attention_mask=UpperCamelCase      )
     __a			=					{
         'input_ids': source_ids,
         'attention_mask': source_mask,
         'decoder_input_ids': y,
     }
     return batch

UpperCAmelCase_ 		=      getLogger(__name__)


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[List]			):
  return list(itertools.chain.from_iterable(a_			)			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  __a			=					get_git_info()
  save_json(a_				,					os.path.join(a_				,					'git_log.json'			)			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple				,					a_	:							Union[str, Any]				,					a_	:							Tuple=4				,					**a_	:							Tuple			):
  with open(a_				,					'w'			) as f:
    json.dump(a_				,					a_				,					indent=a_				,					**a_			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple			):
  with open(a_			) as f:
    return json.load(a_			)


def 						SCREAMING_SNAKE_CASE       (      ):
  __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 						SCREAMING_SNAKE_CASE       (      a_	:							Callable				,					a_	:							Iterable			):
  return list(map(a_				,					a_			)			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							str				,					a_	:							Optional[Any]			):
  with open(a_				,					'wb'			) as f:
    return pickle.dump(a_				,					a_			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[Any]			):

  def remove_articles(a_	:							Union[str, Any]			):
    return re.sub(R'\b(a|an|the)\b'				,					' '				,					a_			)

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

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

  def lower(a_	:							Optional[int]			):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(a_			)			)			)			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple				,					a_	:							List[Any]			):
  __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 						SCREAMING_SNAKE_CASE       (      a_	:							Dict				,					a_	:							Optional[Any]			):
  return normalize_answer(a_			) == normalize_answer(a_			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]				,					a_	:							List[str]			):
  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 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  return model_prefix.startswith('rag'			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							Dict				,					a_	:							Optional[int]				,					a_	:							Any			):
  __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









'''simple docstring'''


def 						SCREAMING_SNAKE_CASE       (      a_	:							int				,					a_	:							int			):
  return int((input_a, input_a).count(0			) != 0			)


def 						SCREAMING_SNAKE_CASE       (      ):
  assert nand_gate(0				,					0			) == 1
  assert nand_gate(0				,					1			) == 1
  assert nand_gate(1				,					0			) == 1
  assert nand_gate(1				,					1			) == 0


if __name__ == "__main__":
  print(nand_gate(0, 0))
  print(nand_gate(0, 1))
  print(nand_gate(1, 0))
  print(nand_gate(1, 1))



