






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  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase=True ,							__lowerCamelCase="pt"       ) ->			Tuple:

     '''simple docstring'''
     UpperCAmelCase__      :  Union[str, Any]				      =			{"""add_prefix_space""": True} if isinstance(__lowerCamelCase ,							__lowerCamelCase       ) and not line.startswith(""" """       ) else {}
     UpperCAmelCase__      :  Dict				      =			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  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase=None ,							) ->			Optional[int]:

     '''simple docstring'''
     UpperCAmelCase__      :  str				      =			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_  (		__lowerCamelCase		):




     def __init__(				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase="train"	,						_lowerCAmelCase=None	,						_lowerCAmelCase=None	,						_lowerCAmelCase=None	,						_lowerCAmelCase=""	,						):
          super().__init__()
          UpperCAmelCase__      :  Any				      =			Path(_lowerCAmelCase		).joinpath(type_path + """.source"""		)
          UpperCAmelCase__      :  List[Any]				      =			Path(_lowerCAmelCase		).joinpath(type_path + """.target"""		)
          UpperCAmelCase__      :  int				      =			self.get_char_lens(self.src_file		)
          UpperCAmelCase__      :  Union[str, Any]				      =			max_source_length
          UpperCAmelCase__      :  Optional[int]				      =			max_target_length
          assert min(self.src_lens		) > 0, f"found empty line in {self.src_file}"
          UpperCAmelCase__      :  Optional[Any]				      =			tokenizer
          UpperCAmelCase__      :  Optional[int]				      =			prefix
          if n_obs is not None:
               UpperCAmelCase__      :  str				      =			self.src_lens[:n_obs]
          UpperCAmelCase__      :  Union[str, Any]				      =			src_lang
          UpperCAmelCase__      :  int				      =			tgt_lang




     def __len__(				self		):
          return len(self.src_lens		)




     def __getitem__(				self	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  Optional[int]				      =			index + 1  # linecache starts at 1
          UpperCAmelCase__      :  List[Any]				      =			self.prefix + linecache.getline(str(self.src_file		)	,						_lowerCAmelCase		).rstrip("""\n"""		)
          UpperCAmelCase__      :  Union[str, Any]				      =			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__      :  Optional[int]				      =			(
              self.tokenizer.question_encoder if isinstance(self.tokenizer	,						_lowerCAmelCase		) else self.tokenizer
          )
          UpperCAmelCase__      :  Optional[Any]				      =			self.tokenizer.generator if isinstance(self.tokenizer	,						_lowerCAmelCase		) else self.tokenizer

          UpperCAmelCase__      :  Optional[Any]				      =			encode_line(_lowerCAmelCase	,						_lowerCAmelCase	,						self.max_source_length	,						"""right"""		)
          UpperCAmelCase__      :  Any				      =			encode_line(_lowerCAmelCase	,						_lowerCAmelCase	,						self.max_target_length	,						"""right"""		)

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




     @staticmethod
     def   __UpperCAmelCase  (				_lowerCAmelCase		):
          return [len(_lowerCAmelCase		) for x in Path(_lowerCAmelCase		).open().readlines()]




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  Union[str, Any]				      =			torch.stack([x["""input_ids"""] for x in batch]		)
          UpperCAmelCase__      :  Tuple				      =			torch.stack([x["""attention_mask"""] for x in batch]		)
          UpperCAmelCase__      :  str				      =			torch.stack([x["""decoder_input_ids"""] for x in batch]		)
          UpperCAmelCase__      :  Union[str, Any]				      =			(
              self.tokenizer.generator.pad_token_id
              if isinstance(self.tokenizer	,						_lowerCAmelCase		)
              else self.tokenizer.pad_token_id
          )
          UpperCAmelCase__      :  List[Any]				      =			(
              self.tokenizer.question_encoder.pad_token_id
              if isinstance(self.tokenizer	,						_lowerCAmelCase		)
              else self.tokenizer.pad_token_id
          )
          UpperCAmelCase__      :  str				      =			trim_batch(_lowerCAmelCase	,						_lowerCAmelCase		)
          UpperCAmelCase__      ,		UpperCAmelCase__      :  Optional[Any]				      =			trim_batch(_lowerCAmelCase	,						_lowerCAmelCase	,						attention_mask=_lowerCAmelCase		)
          UpperCAmelCase__      :  Union[str, Any]				      =			{
              """input_ids""": source_ids,
              """attention_mask""": source_mask,
              """decoder_input_ids""": y,
          }
          return batch



SCREAMING_SNAKE_CASE__		:							Optional[int] 				=				getLogger(__name__)





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			Optional[Any]:

     '''simple docstring'''
     return list(itertools.chain.from_iterable(__lowerCamelCase       )       )





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			None:

     '''simple docstring'''
     UpperCAmelCase__      :  List[Any]				      =			get_git_info()
     save_json(__lowerCamelCase ,							os.path.join(__lowerCamelCase ,							"""git_log.json"""       )       )





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase=4 ,							**__lowerCamelCase       ) ->			Tuple:

     '''simple docstring'''
     with open(__lowerCamelCase ,							"""w"""       ) as f:
          json.dump(__lowerCamelCase ,							__lowerCamelCase ,							indent=__lowerCamelCase ,							**__lowerCamelCase       )





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			int:

     '''simple docstring'''
     with open(__lowerCamelCase       ) as f:
          return json.load(__lowerCamelCase       )





def  _lowerCamelCase						(						) ->			str:

     '''simple docstring'''
     UpperCAmelCase__      :  Dict				      =			git.Repo(search_parent_directories=__lowerCamelCase       )
     UpperCAmelCase__      :  Tuple				      =			{
         """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  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase       ) ->			List:

     '''simple docstring'''
     return list(map(__lowerCamelCase ,							__lowerCamelCase       )       )





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase       ) ->			List[str]:

     '''simple docstring'''
     with open(__lowerCamelCase ,							"""wb"""       ) as f:
          return pickle.dump(__lowerCamelCase ,							__lowerCamelCase       )





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			List[Any]:

     '''simple docstring'''

     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       ):
          UpperCAmelCase__      :  int				      =			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  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase       ) ->			Dict:

     '''simple docstring'''
     UpperCAmelCase__      :  Optional[int]				      =			normalize_answer(__lowerCamelCase       ).split()
     UpperCAmelCase__      :  int				      =			normalize_answer(__lowerCamelCase       ).split()
     UpperCAmelCase__      :  List[Any]				      =			Counter(__lowerCamelCase       ) & Counter(__lowerCamelCase       )
     UpperCAmelCase__      :  Dict				      =			sum(common.values()       )
     if num_same == 0:
          return 0
     UpperCAmelCase__      :  Optional[Any]				      =			1.0 * num_same / len(__lowerCamelCase       )
     UpperCAmelCase__      :  List[Any]				      =			1.0 * num_same / len(__lowerCamelCase       )
     UpperCAmelCase__      :  str				      =			(2 * precision * recall) / (precision + recall)
     return fa





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase       ) ->			List[Any]:

     '''simple docstring'''
     return normalize_answer(__lowerCamelCase       ) == normalize_answer(__lowerCamelCase       )





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase       ) ->			Dict:

     '''simple docstring'''
     assert len(__lowerCamelCase       ) == len(__lowerCamelCase       )
     UpperCAmelCase__      :  Optional[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  _lowerCamelCase						(						__lowerCamelCase       ) ->			Optional[Any]:

     '''simple docstring'''
     return model_prefix.startswith("""rag"""       )







def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase       ) ->			Optional[Any]:

     '''simple docstring'''
     UpperCAmelCase__      :  List[Any]				      =			{p: p for p in extra_params}
     # T5 models don't have `dropout` param, they have `dropout_rate` instead
     UpperCAmelCase__      :  Any				      =			"""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
               UpperCAmelCase__      :  str				      =			p if hasattr(__lowerCamelCase ,							__lowerCamelCase       ) else equivalent_param[p]
               setattr(__lowerCamelCase ,							__lowerCamelCase ,							getattr(__lowerCamelCase ,							__lowerCamelCase       )       )
               delattr(__lowerCamelCase ,							__lowerCamelCase       )
     return hparams, config








"""simple docstring"""
import warnings

from ..trainer import Trainer
from ..utils import logging


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_=None    ,				**snake_case_				)       ->  Optional[Any]:
													warnings.warn(
													    """`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
													    """instead."""    ,				snake_case_    ,				)
													super().__init__(args=snake_case_    ,				**snake_case_				)
