

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





def    A_  ( snake_case      ,       snake_case      ,       snake_case=None      ,       ):
 SCREAMING_SNAKE_CASE:Union[str, Any]						      =					input_ids.ne(lowerCAmelCase__ ).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 							_snake_case (       __SCREAMING_SNAKE_CASE						):
   def __init__( self			:      str     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      Any     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      List[str]="train"     ,SCREAMING_SNAKE_CASE__			:      Dict=None     ,SCREAMING_SNAKE_CASE__			:      Tuple=None     ,SCREAMING_SNAKE_CASE__			:      str=None     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=""     ,):
    super().__init__()
    SCREAMING_SNAKE_CASE:str						      =					Path(_a							).joinpath(type_path + ".source"							)
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					Path(_a							).joinpath(type_path + ".target"							)
    SCREAMING_SNAKE_CASE:List[Any]						      =					self.get_char_lens(self.src_file							)
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					max_source_length
    SCREAMING_SNAKE_CASE:List[str]						      =					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[str]						      =					prefix
    if n_obs is not None:
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.src_lens[:n_obs]
    SCREAMING_SNAKE_CASE:Any						      =					src_lang
    SCREAMING_SNAKE_CASE:Optional[int]						      =					tgt_lang
   def __len__( self			:      Optional[Any]							):
    return len(self.src_lens							)
   def __getitem__( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      Dict							):
    SCREAMING_SNAKE_CASE:Any						      =					index + 1  # linecache starts at 1
    SCREAMING_SNAKE_CASE:Any						      =					self.prefix + linecache.getline(str(self.src_file							)     ,_a							).rstrip("\n"							)
    SCREAMING_SNAKE_CASE:int						      =					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
    SCREAMING_SNAKE_CASE:List[Any]						      =					(
        self.tokenizer.question_encoder if isinstance(self.tokenizer     ,_a							) else self.tokenizer
    )
    SCREAMING_SNAKE_CASE:Optional[int]						      =					self.tokenizer.generator if isinstance(self.tokenizer     ,_a							) else self.tokenizer

    SCREAMING_SNAKE_CASE:Optional[int]						      =					encode_line(_a     ,_a     ,self.max_source_length     ,"right"							)
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					encode_line(_a     ,_a     ,self.max_target_length     ,"right"							)

    SCREAMING_SNAKE_CASE:Optional[int]						      =					source_inputs["input_ids"].squeeze()
    SCREAMING_SNAKE_CASE:Dict						      =					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     __UpperCamelCase						( SCREAMING_SNAKE_CASE__			:      Tuple							):
    return [len(_a							) for x in Path(_a							).open().readlines()]







   def     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      int							):
    SCREAMING_SNAKE_CASE:Dict						      =					torch.stack([x["input_ids"] for x in batch]							)
    SCREAMING_SNAKE_CASE:Any						      =					torch.stack([x["attention_mask"] for x in batch]							)
    SCREAMING_SNAKE_CASE:List[str]						      =					torch.stack([x["decoder_input_ids"] for x in batch]							)
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					(
        self.tokenizer.generator.pad_token_id
        if isinstance(self.tokenizer     ,_a							)
        else self.tokenizer.pad_token_id
    )
    SCREAMING_SNAKE_CASE:Dict						      =					(
        self.tokenizer.question_encoder.pad_token_id
        if isinstance(self.tokenizer     ,_a							)
        else self.tokenizer.pad_token_id
    )
    SCREAMING_SNAKE_CASE:str						      =					trim_batch(_a     ,_a							)
    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					trim_batch(_a     ,_a     ,attention_mask=_a							)
    SCREAMING_SNAKE_CASE:Tuple						      =					{
        "input_ids": source_ids,
        "attention_mask": source_mask,
        "decoder_input_ids": y,
    }
    return batch



A_       						=							getLogger(__name__)





def    A_  ( snake_case ):
 return list(itertools.chain.from_iterable(lowerCAmelCase__ ) )





def    A_  ( snake_case ):
 SCREAMING_SNAKE_CASE:Tuple						      =					get_git_info()
 save_json(lowerCAmelCase__      ,       os.path.join(lowerCAmelCase__      ,       "git_log.json" ) )





def    A_  ( snake_case      ,       snake_case      ,       snake_case=4      ,       **snake_case ):
 with open(lowerCAmelCase__      ,       "w" ) as f:
  json.dump(lowerCAmelCase__      ,       lowerCAmelCase__      ,       indent=lowerCAmelCase__      ,       **lowerCAmelCase__ )





def    A_  ( snake_case ):
 with open(lowerCAmelCase__ ) as f:
  return json.load(lowerCAmelCase__ )





def    A_  ( ):
 SCREAMING_SNAKE_CASE:Tuple						      =					git.Repo(search_parent_directories=lowerCAmelCase__ )
 SCREAMING_SNAKE_CASE:Optional[int]						      =					{
     "repo_id": str(lowerCAmelCase__ ),
     "repo_sha": str(repo.head.object.hexsha ),
     "repo_branch": str(repo.active_branch ),
     "hostname": str(socket.gethostname() ),
 }
 return repo_infos





def    A_  ( snake_case      ,       snake_case ):
 return list(map(lowerCAmelCase__      ,       lowerCAmelCase__ ) )





def    A_  ( snake_case      ,       snake_case ):
 with open(lowerCAmelCase__      ,       "wb" ) as f:
  return pickle.dump(lowerCAmelCase__      ,       lowerCAmelCase__ )





def    A_  ( snake_case ):

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

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

 def remove_punc(snake_case ):
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					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(lowerCAmelCase__ ) ) ) )





def    A_  ( snake_case      ,       snake_case ):
 SCREAMING_SNAKE_CASE:Any						      =					normalize_answer(lowerCAmelCase__ ).split()
 SCREAMING_SNAKE_CASE:int						      =					normalize_answer(lowerCAmelCase__ ).split()
 SCREAMING_SNAKE_CASE:Union[str, Any]						      =					Counter(lowerCAmelCase__ ) & Counter(lowerCAmelCase__ )
 SCREAMING_SNAKE_CASE:Union[str, Any]						      =					sum(common.values() )
 if num_same == 0:
  return 0
 SCREAMING_SNAKE_CASE:Dict						      =					1.0 * num_same / len(lowerCAmelCase__ )
 SCREAMING_SNAKE_CASE:List[str]						      =					1.0 * num_same / len(lowerCAmelCase__ )
 SCREAMING_SNAKE_CASE:List[Any]						      =					(2 * precision * recall) / (precision + recall)
 return fa





def    A_  ( snake_case      ,       snake_case ):
 return normalize_answer(lowerCAmelCase__ ) == normalize_answer(lowerCAmelCase__ )





def    A_  ( snake_case      ,       snake_case ):
 assert len(lowerCAmelCase__ ) == len(lowerCAmelCase__ )
 SCREAMING_SNAKE_CASE:List[Any]						      =					0
 for hypo, pred in zip(lowerCAmelCase__      ,       lowerCAmelCase__ ):
  em += exact_match_score(lowerCAmelCase__      ,       lowerCAmelCase__ )
 if len(lowerCAmelCase__ ) > 0:
  em /= len(lowerCAmelCase__ )
 return {"em": em}





def    A_  ( snake_case ):
 return model_prefix.startswith("rag" )





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









'''simple docstring'''

def    A_  ( snake_case = 100 ):
  SCREAMING_SNAKE_CASE:Dict						      =					0
  SCREAMING_SNAKE_CASE:Optional[int]						      =					0
  for i in range(1      ,       n + 1 ):
    sum_of_squares += i**2
    sum_of_ints += i
  return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
    print(f'''{solution() = }''')






