






'''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 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=True	,		__UpperCamelCase="pt"					)->				Tuple:
      UpperCamelCase       						=      {"""add_prefix_space""": True} if isinstance(__UpperCamelCase	,		__UpperCamelCase					) and not line.startswith(""" """					) else {}
      UpperCamelCase       						=      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 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None	,		)->				str:
      UpperCamelCase       						=      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        a_		(    a__       ):

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE="train"    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=""    ,	)						->   str:



        """simple docstring"""






        super().__init__()
        UpperCamelCase       						=      Path(lowerCamelCase_   ).joinpath(type_path + """.source"""   )
        UpperCamelCase       						=      Path(lowerCamelCase_   ).joinpath(type_path + """.target"""   )
        UpperCamelCase       						=      self.get_char_lens(self.src_file   )
        UpperCamelCase       						=      max_source_length
        UpperCamelCase       						=      max_target_length
        assert min(self.src_lens   ) > 0, F"found empty line in {self.src_file}"
        UpperCamelCase       						=      tokenizer
        UpperCamelCase       						=      prefix
        if n_obs is not None:
              UpperCamelCase       						=      self.src_lens[:n_obs]
        UpperCamelCase       						=      src_lang
        UpperCamelCase       						=      tgt_lang

  def __len__(   self   )						->   Tuple:



        """simple docstring"""






        return len(self.src_lens   )

  def __getitem__(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Dict[str, torch.Tensor]:



        """simple docstring"""






        UpperCamelCase       						=      index + 1  # linecache starts at 1
        UpperCamelCase       						=      self.prefix + linecache.getline(str(self.src_file   )    ,	lowerCamelCase_   ).rstrip("""\n"""   )
        UpperCamelCase       						=      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
        UpperCamelCase       						=      (
            self.tokenizer.question_encoder if isinstance(self.tokenizer    ,	lowerCamelCase_   ) else self.tokenizer
        )
        UpperCamelCase       						=      self.tokenizer.generator if isinstance(self.tokenizer    ,	lowerCamelCase_   ) else self.tokenizer

        UpperCamelCase       						=      encode_line(lowerCamelCase_    ,	lowerCamelCase_    ,	self.max_source_length    ,	"""right"""   )
        UpperCamelCase       						=      encode_line(lowerCamelCase_    ,	lowerCamelCase_    ,	self.max_target_length    ,	"""right"""   )

        UpperCamelCase       						=      source_inputs["""input_ids"""].squeeze()
        UpperCamelCase       						=      target_inputs["""input_ids"""].squeeze()
        UpperCamelCase       						=      source_inputs["""attention_mask"""].squeeze()
        return {
            "input_ids": source_ids,
            "attention_mask": src_mask,
            "decoder_input_ids": target_ids,
        }

  @staticmethod
  def 						A__		(   _SCREAMING_SNAKE_CASE   )						->   Dict:



        """simple docstring"""






        return [len(lowerCamelCase_   ) for x in Path(lowerCamelCase_   ).open().readlines()]


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Dict[str, torch.Tensor]:



        """simple docstring"""






        UpperCamelCase       						=      torch.stack([x["""input_ids"""] for x in batch]   )
        UpperCamelCase       						=      torch.stack([x["""attention_mask"""] for x in batch]   )
        UpperCamelCase       						=      torch.stack([x["""decoder_input_ids"""] for x in batch]   )
        UpperCamelCase       						=      (
            self.tokenizer.generator.pad_token_id
            if isinstance(self.tokenizer    ,	lowerCamelCase_   )
            else self.tokenizer.pad_token_id
        )
        UpperCamelCase       						=      (
            self.tokenizer.question_encoder.pad_token_id
            if isinstance(self.tokenizer    ,	lowerCamelCase_   )
            else self.tokenizer.pad_token_id
        )
        UpperCamelCase       						=      trim_batch(lowerCamelCase_    ,	lowerCamelCase_   )
        UpperCamelCase						,UpperCamelCase       						=      trim_batch(lowerCamelCase_    ,	lowerCamelCase_    ,	attention_mask=lowerCamelCase_   )
        UpperCamelCase       						=      {
            """input_ids""": source_ids,
            """attention_mask""": source_mask,
            """decoder_input_ids""": y,
        }
        return batch


SCREAMING_SNAKE_CASE__											=     getLogger(__name__)







def 					lowercase__				(   __UpperCamelCase					)->				Tuple:
      return list(itertools.chain.from_iterable(__UpperCamelCase					)					)







def 					lowercase__				(   __UpperCamelCase					)->				None:
      UpperCamelCase       						=      get_git_info()
      save_json(__UpperCamelCase	,		os.path.join(__UpperCamelCase	,		"""git_log.json"""					)					)







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=4	,		**__UpperCamelCase					)->				Optional[int]:
      with open(__UpperCamelCase	,		"""w"""					) as f:
            json.dump(__UpperCamelCase	,		__UpperCamelCase	,		indent=__UpperCamelCase	,		**__UpperCamelCase					)







def 					lowercase__				(   __UpperCamelCase					)->				Optional[int]:
      with open(__UpperCamelCase					) as f:
            return json.load(__UpperCamelCase					)







def 					lowercase__				(   )->				str:
      UpperCamelCase       						=      git.Repo(search_parent_directories=__UpperCamelCase					)
      UpperCamelCase       						=      {
          """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 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				List:
      return list(map(__UpperCamelCase	,		__UpperCamelCase					)					)







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				str:
      with open(__UpperCamelCase	,		"""wb"""					) as f:
            return pickle.dump(__UpperCamelCase	,		__UpperCamelCase					)







def 					lowercase__				(   __UpperCamelCase					)->				int:

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

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

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

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

      return white_space_fix(remove_articles(remove_punc(lower(__UpperCamelCase					)					)					)					)







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				str:
      UpperCamelCase       						=      normalize_answer(__UpperCamelCase					).split()
      UpperCamelCase       						=      normalize_answer(__UpperCamelCase					).split()
      UpperCamelCase       						=      Counter(__UpperCamelCase					) & Counter(__UpperCamelCase					)
      UpperCamelCase       						=      sum(common.values()					)
      if num_same == 0:
            return 0
      UpperCamelCase       						=      1.0 * num_same / len(__UpperCamelCase					)
      UpperCamelCase       						=      1.0 * num_same / len(__UpperCamelCase					)
      UpperCamelCase       						=      (2 * precision * recall) / (precision + recall)
      return fa







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				str:
      return normalize_answer(__UpperCamelCase					) == normalize_answer(__UpperCamelCase					)







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				Dict:
      assert len(__UpperCamelCase					) == len(__UpperCamelCase					)
      UpperCamelCase       						=      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 					lowercase__				(   __UpperCamelCase					)->				Union[str, Any]:
      return model_prefix.startswith("""rag"""					)







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				Optional[Any]:
      UpperCamelCase       						=      {p: p for p in extra_params}
      # T5 models don't have `dropout` param, they have `dropout_rate` instead
      UpperCamelCase       						=      """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
                  UpperCamelCase       						=      p if hasattr(__UpperCamelCase	,		__UpperCamelCase					) else equivalent_param[p]
                  setattr(__UpperCamelCase	,		__UpperCamelCase	,		getattr(__UpperCamelCase	,		__UpperCamelCase					)					)
                  delattr(__UpperCamelCase	,		__UpperCamelCase					)
      return hparams, config







'''simple docstring'''

SCREAMING_SNAKE_CASE__											=     8.31_44_62  # Unit - J mol-1 K-1







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				float:
      if moles < 0 or kelvin < 0 or volume < 0:
            raise ValueError("""Invalid inputs. Enter positive value."""					)
      return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				float:
      if moles < 0 or kelvin < 0 or pressure < 0:
            raise ValueError("""Invalid inputs. Enter positive value."""					)
      return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
     from doctest import testmod

     testmod()
