


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


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A="train"  ,       __A=None  ,       __A=None  ,       __A=None  ,       __A=""  ,       ):
        super().__init__()
        __a					=  Path(__A    ).joinpath(type_path + """.source"""    )
        __a					=  Path(__A    ).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    ):
        return len(self.src_lens    )


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

        __a					=  encode_line(__A  ,       __A  ,       self.max_source_length  ,       """right"""    )
        __a					=  encode_line(__A  ,       __A  ,       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_  (	__A    ):
        return [len(__A    ) for x in Path(__A    ).open().readlines()]


     def 	snake_case_  (	self  ,       __A    ):
        __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  ,       __A    )
            else self.tokenizer.pad_token_id
        )
        __a					=  (
            self.tokenizer.question_encoder.pad_token_id
            if isinstance(self.tokenizer  ,       __A    )
            else self.tokenizer.pad_token_id
        )
        __a					=  trim_batch(__A  ,       __A    )
        __a	,    __a					=  trim_batch(__A  ,       __A  ,       attention_mask=__A    )
        __a					=  {
            """input_ids""": source_ids,
            """attention_mask""": source_mask,
            """decoder_input_ids""": y,
        }
        return batch







SCREAMING_SNAKE_CASE							    =							getLogger(__name__)




def  a   (lowerCAmelCase__   ):
   return list(itertools.chain.from_iterable(lowerCAmelCase__   )   )




def  a   (lowerCAmelCase__   ):
   __a					=  get_git_info()
   save_json(lowerCAmelCase__		,				os.path.join(lowerCAmelCase__		,				"""git_log.json"""   )   )




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




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




def  a   ():
   __a					=  git.Repo(search_parent_directories=lowerCAmelCase__   )
   __a					=  {
       """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   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   return list(map(lowerCAmelCase__		,				lowerCAmelCase__   )   )




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




def  a   (lowerCAmelCase__   ):

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

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

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

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

   return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase__   )   )   )   )




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




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   return normalize_answer(lowerCAmelCase__   ) == normalize_answer(lowerCAmelCase__   )




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   assert len(lowerCAmelCase__   ) == len(lowerCAmelCase__   )
   __a					=  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   (lowerCAmelCase__   ):
   return model_prefix.startswith("""rag"""   )




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __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(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
         __a					=  p if hasattr(lowerCAmelCase__		,				lowerCAmelCase__   ) else equivalent_param[p]
         setattr(lowerCAmelCase__		,				lowerCAmelCase__		,				getattr(lowerCAmelCase__		,				lowerCAmelCase__   )   )
         delattr(lowerCAmelCase__		,				lowerCAmelCase__   )
   return hparams, config







import math




def  a   (lowerCAmelCase__   ):

   if 1 < number < 4:
      # 2 and 3 are primes
      return True
   elif number < 2 or number % 2 == 0 or number % 3 == 0:
      # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
      return False

   # All primes number are in format of 6k +/- 1
   for i in range(5		,				int(math.sqrt(lowerCAmelCase__   ) + 1   )		,				6   ):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True




def  a   (lowerCAmelCase__ = 10_001   ):

   try:
      __a					=  int(lowerCAmelCase__   )
   except (TypeError, ValueError):
      raise TypeError("""Parameter nth must be int or castable to int."""   ) from None
   if nth <= 0:
      raise ValueError("""Parameter nth must be greater than or equal to one."""   )
   __a					=  []
   __a					=  2
   while len(lowerCAmelCase__   ) < nth:
      if is_prime(lowerCAmelCase__   ):
         primes.append(lowerCAmelCase__   )
         num += 1
      else:
         num += 1
   return primes[len(lowerCAmelCase__   ) - 1]


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



