

"""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					(	_a	:						Optional[Any]     ,   _a	:						Optional[int]     ,   _a	:						Union[str, Any]     ,   _a	:						List[Any]     ,   _a	:						Any=True     ,   _a	:						int="pt"				):



      """simple docstring"""






      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   _A					(	_a	:						Tuple     ,   _a	:						Any     ,   _a	:						str=None     ,   ):



      """simple docstring"""






      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 lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    def __init__( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_="train" ,lowerCamelCase_=None ,lowerCamelCase_=None ,lowerCamelCase_=None ,lowerCamelCase_="" ,)    ->      Tuple:
          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    )    ->      Optional[int]:
          return len(self.src_lens    )




    def __getitem__( self ,lowerCamelCase_    )    ->      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 UpperCamelCase__		( lowerCamelCase_    )    ->      Any:
          return [len(lowerCamelCase_    ) for x in Path(lowerCamelCase_    ).open().readlines()]




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      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   _A					(	_a	:						List[List]				):



      """simple docstring"""






      return list(itertools.chain.from_iterable(_a				)				)



def   _A					(	_a	:						str				):



      """simple docstring"""






      A				  =		get_git_info()
      save_json(_a     ,   os.path.join(_a     ,   """git_log.json"""				)				)



def   _A					(	_a	:						Dict     ,   _a	:						List[Any]     ,   _a	:						Any=4     ,   **_a	:						Union[str, Any]				):



      """simple docstring"""






      with open(_a     ,   """w"""				) as f:
            json.dump(_a     ,   _a     ,   indent=_a     ,   **_a				)



def   _A					(	_a	:						Tuple				):



      """simple docstring"""






      with open(_a				) as f:
            return json.load(_a				)



def   _A					(	):



      """simple docstring"""






      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   _A					(	_a	:						Callable     ,   _a	:						Iterable				):



      """simple docstring"""






      return list(map(_a     ,   _a				)				)



def   _A					(	_a	:						Optional[Any]     ,   _a	:						int				):



      """simple docstring"""






      with open(_a     ,   """wb"""				) as f:
            return pickle.dump(_a     ,   _a				)



def   _A					(	_a	:						Tuple				):



      """simple docstring"""






      def remove_articles(_a	:						Optional[Any]				):
            return re.sub(r"""\b(a|an|the)\b"""     ,   """ """     ,   _a				)

      def white_space_fix(_a	:						Union[str, Any]				):
            return " ".join(text.split()				)

      def remove_punc(_a	:						int				):
            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   _A					(	_a	:						List[Any]     ,   _a	:						str				):



      """simple docstring"""






      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   _A					(	_a	:						Any     ,   _a	:						List[Any]				):



      """simple docstring"""






      return normalize_answer(_a				) == normalize_answer(_a				)



def   _A					(	_a	:						List[str]     ,   _a	:						List[str]				):



      """simple docstring"""






      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   _A					(	_a	:						str				):



      """simple docstring"""






      return model_prefix.startswith("""rag"""				)





def   _A					(	_a	:						Dict     ,   _a	:						Union[str, Any]     ,   _a	:						List[Any]				):



      """simple docstring"""






      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"""


from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate





@dataclass(frozen=SCREAMING_SNAKE_CASE    )
class lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    _lowerCamelCase					=   field(default='''summarization'''     ,   metadata={'''include_in_asdict_even_if_is_default''': True}    )
    _lowerCamelCase					=   Features({'''text''': Value('''string'''    )}    )
    _lowerCamelCase					=   Features({'''summary''': Value('''string'''    )}    )
    _lowerCamelCase					=   "text"
    _lowerCamelCase					=   "summary"




    @property
    def UpperCamelCase__		( self    )    ->      Dict[str, str]:
          return {self.text_column: "text", self.summary_column: "summary"}




