



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	:Dict	,   _snake_case	:Union[str, Any]	,   _snake_case	:List[str]	,   _snake_case	:Union[str, Any]	,   _snake_case	:List[Any]=True	,   _snake_case	:Optional[Any]="pt"				)  ->     Optional[Any]:
   _A      =       {'''add_prefix_space''': True} if isinstance(_snake_case	,   _snake_case				) and not line.startswith(''' '''				) else {}
   _A      =       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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Union[str, Any]	,   _snake_case	:Optional[Any]	,   _snake_case	:List[Any]=None	,   )  ->     List[str]:
   _A      =       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 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					Union[str, Any]			,						__lowerCAmelCase					:					Optional[int]			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					Union[str, Any]			,						__lowerCAmelCase					:					Dict			,						__lowerCAmelCase					:					Dict="train"			,						__lowerCAmelCase					:					int=None			,						__lowerCAmelCase					:					Dict=None			,						__lowerCAmelCase					:					List[str]=None			,						__lowerCAmelCase					:					Any=""			,						)   ->	Optional[Any]:
    super().__init__()
    _A      =       Path(__lowerCAmelCase      ).joinpath(type_path + '''.source'''      )
    _A      =       Path(__lowerCAmelCase      ).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					:					Union[str, Any]      )   ->	List[Any]:
    return len(self.src_lens      )

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

    _A      =       encode_line(__lowerCAmelCase			,						__lowerCAmelCase			,						self.max_source_length			,						'''right'''      )
    _A      =       encode_line(__lowerCAmelCase			,						__lowerCAmelCase			,						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     snake_case_      (    __lowerCAmelCase					:					int      )   ->	List[Any]:
    return [len(__lowerCAmelCase      ) for x in Path(__lowerCAmelCase      ).open().readlines()]

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					Dict      )   ->	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			,						__lowerCAmelCase      )
        else self.tokenizer.pad_token_id
    )
    _A      =       (
        self.tokenizer.question_encoder.pad_token_id
        if isinstance(self.tokenizer			,						__lowerCAmelCase      )
        else self.tokenizer.pad_token_id
    )
    _A      =       trim_batch(__lowerCAmelCase			,						__lowerCAmelCase      )
    _A  ,    _A      =       trim_batch(__lowerCAmelCase			,						__lowerCAmelCase			,						attention_mask=__lowerCAmelCase      )
    _A      =       {
        '''input_ids''': source_ids,
        '''attention_mask''': source_mask,
        '''decoder_input_ids''': y,
    }
    return batch
UpperCAmelCase_		 =					getLogger(__name__)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[List]				)  ->     List[str]:
   return list(itertools.chain.from_iterable(_snake_case				)				)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str				)  ->     None:
   _A      =       get_git_info()
   save_json(_snake_case	,   os.path.join(_snake_case	,   '''git_log.json'''				)				)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[Any]	,   _snake_case	:Dict	,   _snake_case	:Any=4	,   **_snake_case	:Optional[Any]				)  ->     str:
   with open(_snake_case	,   '''w'''				) as f:
      json.dump(_snake_case	,   _snake_case	,   indent=_snake_case	,   **_snake_case				)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[str]				)  ->     List[str]:
   with open(_snake_case				) as f:
      return json.load(_snake_case				)

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Optional[Any]:
   _A      =       git.Repo(search_parent_directories=_snake_case				)
   _A      =       {
       '''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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Callable	,   _snake_case	:Iterable				)  ->     List:
   return list(map(_snake_case	,   _snake_case				)				)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[Any]	,   _snake_case	:int				)  ->     Optional[Any]:
   with open(_snake_case	,   '''wb'''				) as f:
      return pickle.dump(_snake_case	,   _snake_case				)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Union[str, Any]				)  ->     Any:

   def remove_articles(_snake_case	:Optional[int]				):
      return re.sub(r'''\b(a|an|the)\b'''	,   ''' '''	,   _snake_case				)

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

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

   def lower(_snake_case	:Any				):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(_snake_case				)				)				)				)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[Any]	,   _snake_case	:Optional[int]				)  ->     Optional[int]:
   _A      =       normalize_answer(_snake_case				).split()
   _A      =       normalize_answer(_snake_case				).split()
   _A      =       Counter(_snake_case				) & Counter(_snake_case				)
   _A      =       sum(common.values()				)
   if num_same == 0:
      return 0
   _A      =       1.0 * num_same / len(_snake_case				)
   _A      =       1.0 * num_same / len(_snake_case				)
   _A      =       (2 * precision * recall) / (precision + recall)
   return fa

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Tuple	,   _snake_case	:Optional[int]				)  ->     int:
   return normalize_answer(_snake_case				) == normalize_answer(_snake_case				)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[str]	,   _snake_case	:List[str]				)  ->     Dict:
   assert len(_snake_case				) == len(_snake_case				)
   _A      =       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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str				)  ->     str:
   return model_prefix.startswith('''rag'''				)


def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[Any]	,   _snake_case	:List[Any]	,   _snake_case	:Union[str, Any]				)  ->     Union[str, 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(_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
         _A      =       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








def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     int:
   if not isinstance(_snake_case	,   _snake_case				):
      raise ValueError('''Input must be an integer'''				)
   if input_num <= 0:
      raise ValueError('''Input must be positive'''				)
   return sum(
       divisor for divisor in range(1	,   input_num // 2 + 1				) if input_num % divisor == 0				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



