





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"     )						-> int:
 UpperCamelCase	:					Any       	=   {"add_prefix_space": True} if isinstance(_lowerCAmelCase							,   _lowerCAmelCase     ) and not line.startswith(" "     ) else {}
 UpperCamelCase	:					List[str]       	=   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							,   )						-> str:
 UpperCamelCase	:					List[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 					A__   (       __snake_case			):





      def __init__( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_="train"       ,							A_=None       ,							A_=None       ,							A_=None       ,							A_=""       ,							):
       '''simple docstring'''




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





      def __len__( self   ):
       '''simple docstring'''




       return len(self.src_lens   )





      def __getitem__( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   index + 1  # linecache starts at 1
       UpperCamelCase	:					Dict       	=   self.prefix + linecache.getline(str(self.src_file   )       ,							A_   ).rstrip("\n"   )
       UpperCamelCase	:					Dict       	=   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
       UpperCamelCase	:					str       	=   (
           self.tokenizer.question_encoder if isinstance(self.tokenizer       ,							A_   ) else self.tokenizer
       )
       UpperCamelCase	:					Any       	=   self.tokenizer.generator if isinstance(self.tokenizer       ,							A_   ) else self.tokenizer

       UpperCamelCase	:					Dict       	=   encode_line(A_       ,							A_       ,							self.max_source_length       ,							"right"   )
       UpperCamelCase	:					Optional[int]       	=   encode_line(A_       ,							A_       ,							self.max_target_length       ,							"right"   )

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





      @staticmethod
      def        __UpperCamelCase( A_   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




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


__lowerCamelCase       :     List[Any]   =     getLogger(__name__)



def 		A_				(     _lowerCAmelCase     )						-> List[Any]:
 return list(itertools.chain.from_iterable(_lowerCAmelCase     )     )



def 		A_				(     _lowerCAmelCase     )						-> None:
 UpperCamelCase	:					Optional[int]       	=   get_git_info()
 save_json(_lowerCAmelCase							,   os.path.join(_lowerCAmelCase							,   "git_log.json"     )     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase=4							,   **_lowerCAmelCase     )						-> List[str]:
 with open(_lowerCAmelCase							,   "w"     ) as f:
  json.dump(_lowerCAmelCase							,   _lowerCAmelCase							,   indent=_lowerCAmelCase							,   **_lowerCAmelCase     )



def 		A_				(     _lowerCAmelCase     )						-> Any:
 with open(_lowerCAmelCase     ) as f:
  return json.load(_lowerCAmelCase     )



def 		A_				(     )						-> Any:
 UpperCamelCase	:					List[Any]       	=   git.Repo(search_parent_directories=_lowerCAmelCase     )
 UpperCamelCase	:					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_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> List:
 return list(map(_lowerCAmelCase							,   _lowerCAmelCase     )     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> List[Any]:
 with open(_lowerCAmelCase							,   "wb"     ) as f:
  return pickle.dump(_lowerCAmelCase							,   _lowerCAmelCase     )



def 		A_				(     _lowerCAmelCase     )						-> Union[str, Any]:

 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	:					Any       	=   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     )						-> List[Any]:
 UpperCamelCase	:					Dict       	=   normalize_answer(_lowerCAmelCase     ).split()
 UpperCamelCase	:					Union[str, Any]       	=   normalize_answer(_lowerCAmelCase     ).split()
 UpperCamelCase	:					str       	=   Counter(_lowerCAmelCase     ) & Counter(_lowerCAmelCase     )
 UpperCamelCase	:					Union[str, Any]       	=   sum(common.values()     )
 if num_same == 0:
  return 0
 UpperCamelCase	:					Tuple       	=   1.0 * num_same / len(_lowerCAmelCase     )
 UpperCamelCase	:					Optional[int]       	=   1.0 * num_same / len(_lowerCAmelCase     )
 UpperCamelCase	:					Any       	=   (2 * precision * recall) / (precision + recall)
 return fa



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> Union[str, Any]:
 return normalize_answer(_lowerCAmelCase     ) == normalize_answer(_lowerCAmelCase     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> Dict:
 assert len(_lowerCAmelCase     ) == len(_lowerCAmelCase     )
 UpperCamelCase	:					Union[str, 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_				(     _lowerCAmelCase     )						-> str:
 return model_prefix.startswith("rag"     )



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








from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       """"""
				lowercase__											=       """hf-legacy"""  # "hf://"" is reserved for hffs




				def __init__(     self						:    List[str],  lowerCamelCase						:    Optional[DatasetInfo] = None,  lowerCamelCase						:    Optional[str] = None,  **lowerCamelCase						:    str,  ):

								'''simple docstring'''
								super().__init__(self,  **lowerCamelCase  )
								lowercase__    =			repo_info
								lowercase__    =			token
								lowercase__    =			None




				def 				lowercase__   (     self						:    Optional[Any]  ):

								'''simple docstring'''
								if self.dir_cache is None:
												lowercase__    =			{}
												for hf_file in self.repo_info.siblings:
																# TODO(QL): add sizes
																lowercase__    =			{
																    '''name''': hf_file.rfilename,
																    '''size''': None,
																    '''type''': '''file''',
																}
																self.dir_cache.update(
																    {
																        str(lowerCamelCase  ): {'''name''': str(lowerCamelCase  ), '''size''': None, '''type''': '''directory'''}
																        for d in list(PurePosixPath(hf_file.rfilename  ).parents  )[:-1]
																    }  )




				def 				lowercase__   (     self						:    Any,  lowerCamelCase						:    str,  lowerCamelCase						:    str = "rb",  **lowerCamelCase						:    Any,  ):

								'''simple docstring'''
								if not isinstance(self.repo_info,  lowerCamelCase  ):
												raise NotImplementedError(F"""Open is only implemented for dataset repositories, but got {self.repo_info}"""  )
								lowercase__    =			hf_hub_url(self.repo_info.id,  lowerCamelCase,  revision=self.repo_info.sha  )
								return fsspec.open(
								    lowerCamelCase,  mode=lowerCamelCase,  headers=get_authentication_headers_for_url(lowerCamelCase,  use_auth_token=self.token  ),  client_kwargs={'''trust_env''': True},  ).open()




				def 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    Any,  **lowerCamelCase						:    int  ):

								'''simple docstring'''
								self._get_dirs()
								lowercase__    =			self._strip_protocol(lowerCamelCase  )
								if path in self.dir_cache:
												return self.dir_cache[path]
								else:
												raise FileNotFoundError(lowerCamelCase  )




				def 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    List[str],  lowerCamelCase						:    Optional[int]=False,  **lowerCamelCase						:    str  ):

								'''simple docstring'''
								self._get_dirs()
								lowercase__    =			PurePosixPath(path.strip('''/'''  )  )
								lowercase__    =			{}
								for p, f in self.dir_cache.items():
												lowercase__    =			PurePosixPath(p.strip('''/'''  )  )
												lowercase__    =			p.parent
												if root == path:
																lowercase__    =			f
								lowercase__    =			list(paths.values()  )
								if detail:
												return out
								else:
												return sorted(f['''name'''] for f in out  )




