





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__   (							snake_case_     :  List[str]	,				snake_case_     :  Tuple	,				snake_case_     :  List[str]	,				snake_case_     :  Any	,				snake_case_     :  str=True	,				snake_case_     :  int="pt"	)				->    List[str]:
   __snake_case								=       {'''add_prefix_space''': True} if isinstance(snake_case_	,				snake_case_	) and not line.startswith(''' '''	) else {}
   __snake_case								=       padding_side
   return tokenizer(
       [line]	,				max_length=snake_case_	,				padding='''max_length''' if pad_to_max_length else None	,				truncation=snake_case_	,				return_tensors=snake_case_	,				add_special_tokens=snake_case_	,				**snake_case_	,				)






def lowerCamelCase__   (							snake_case_     :  Any	,				snake_case_     :  int	,				snake_case_     :  List[str]=None	,				)				->    Optional[Any]:
   __snake_case								=       input_ids.ne(snake_case_	).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 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


      def __init__(self	:				Dict    ,      a__	:				Optional[Any]    ,      a__	:				List[Any]    ,      a__	:				List[str]    ,      a__	:				Optional[int]    ,      a__	:				Optional[Any]="train"    ,      a__	:				Optional[int]=None    ,      a__	:				Any=None    ,      a__	:				Any=None    ,      a__	:				int=""    ,      ):





         """simple docstring"""



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


      def __len__(self	:				Any				):





         """simple docstring"""



         return len(self.src_lens				)


      def __getitem__(self	:				str    ,      a__	:				Any				):





         """simple docstring"""



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

         __snake_case								=       encode_line(a__    ,      a__    ,      self.max_source_length    ,      '''right'''				)
         __snake_case								=       encode_line(a__    ,      a__    ,      self.max_target_length    ,      '''right'''				)

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


      @staticmethod
      def        a			(a__	:				Optional[Any]				):





         """simple docstring"""



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


      def        a			(self	:				List[str]    ,      a__	:				List[str]				):





         """simple docstring"""



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






snake_case_           =      getLogger(__name__)






def lowerCamelCase__   (							snake_case_     :  List[List]	)				->    Tuple:
   return list(itertools.chain.from_iterable(snake_case_	)	)






def lowerCamelCase__   (							snake_case_     :  str	)				->    None:
   __snake_case								=       get_git_info()
   save_json(snake_case_	,				os.path.join(snake_case_	,				'''git_log.json'''	)	)






def lowerCamelCase__   (							snake_case_     :  int	,				snake_case_     :  Dict	,				snake_case_     :  Dict=4	,				**snake_case_     :  Any	)				->    Tuple:
   with open(snake_case_	,				'''w'''	) as f:
      json.dump(snake_case_	,				snake_case_	,				indent=snake_case_	,				**snake_case_	)






def lowerCamelCase__   (							snake_case_     :  Optional[int]	)				->    Optional[Any]:
   with open(snake_case_	) as f:
      return json.load(snake_case_	)






def lowerCamelCase__   (							)				->    List[Any]:
   __snake_case								=       git.Repo(search_parent_directories=snake_case_	)
   __snake_case								=       {
       '''repo_id''': str(snake_case_	),
       '''repo_sha''': str(repo.head.object.hexsha	),
       '''repo_branch''': str(repo.active_branch	),
       '''hostname''': str(socket.gethostname()	),
   }
   return repo_infos






def lowerCamelCase__   (							snake_case_     :  Callable	,				snake_case_     :  Iterable	)				->    List:
   return list(map(snake_case_	,				snake_case_	)	)






def lowerCamelCase__   (							snake_case_     :  Any	,				snake_case_     :  int	)				->    int:
   with open(snake_case_	,				'''wb'''	) as f:
      return pickle.dump(snake_case_	,				snake_case_	)






def lowerCamelCase__   (							snake_case_     :  List[Any]	)				->    int:

   def remove_articles(snake_case_     :  Optional[int]	):
      return re.sub(R'''\b(a|an|the)\b'''	,				''' '''	,				snake_case_	)

   def white_space_fix(snake_case_     :  List[Any]	):
      return " ".join(text.split()	)

   def remove_punc(snake_case_     :  Optional[int]	):
      __snake_case								=       set(string.punctuation	)
      return "".join(ch for ch in text if ch not in exclude	)

   def lower(snake_case_     :  str	):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(snake_case_	)	)	)	)






def lowerCamelCase__   (							snake_case_     :  List[str]	,				snake_case_     :  List[str]	)				->    List[str]:
   __snake_case								=       normalize_answer(snake_case_	).split()
   __snake_case								=       normalize_answer(snake_case_	).split()
   __snake_case								=       Counter(snake_case_	) & Counter(snake_case_	)
   __snake_case								=       sum(common.values()	)
   if num_same == 0:
      return 0
   __snake_case								=       1.0 * num_same / len(snake_case_	)
   __snake_case								=       1.0 * num_same / len(snake_case_	)
   __snake_case								=       (2 * precision * recall) / (precision + recall)
   return fa






def lowerCamelCase__   (							snake_case_     :  int	,				snake_case_     :  Union[str, Any]	)				->    Any:
   return normalize_answer(snake_case_	) == normalize_answer(snake_case_	)






def lowerCamelCase__   (							snake_case_     :  List[str]	,				snake_case_     :  List[str]	)				->    Dict:
   assert len(snake_case_	) == len(snake_case_	)
   __snake_case								=       0
   for hypo, pred in zip(snake_case_	,				snake_case_	):
      em += exact_match_score(snake_case_	,				snake_case_	)
   if len(snake_case_	) > 0:
      em /= len(snake_case_	)
   return {"em": em}






def lowerCamelCase__   (							snake_case_     :  List[str]	)				->    Optional[int]:
   return model_prefix.startswith('''rag'''	)







def lowerCamelCase__   (							snake_case_     :  Union[str, Any]	,				snake_case_     :  Dict	,				snake_case_     :  List[Any]	)				->    Any:
   __snake_case								=       {p: p for p in extra_params}
   # T5 models don't have `dropout` param, they have `dropout_rate` instead
   __snake_case								=       '''dropout_rate'''
   for p in extra_params:
      if getattr(snake_case_	,				snake_case_	,				snake_case_	):
         if not hasattr(snake_case_	,				snake_case_	) and not hasattr(snake_case_	,				equivalent_param[p]	):
            logger.info('''config doesn\'t have a `{}` attribute'''.format(snake_case_	)	)
            delattr(snake_case_	,				snake_case_	)
            continue
         __snake_case								=       p if hasattr(snake_case_	,				snake_case_	) else equivalent_param[p]
         setattr(snake_case_	,				snake_case_	,				getattr(snake_case_	,				snake_case_	)	)
         delattr(snake_case_	,				snake_case_	)
   return hparams, config










import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


snake_case_           =      logging.get_logger(__name__)

snake_case_           =      {'tokenizer_file': 'tokenizer.json'}

snake_case_           =      {
    'tokenizer_file': {
        'bigscience/tokenizer': 'https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json',
        'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json',
        'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json',
        'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json',
        'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json',
        'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json',
        'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json',
    },
}

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    List[str]							=   VOCAB_FILES_NAMES
      A_						:    Optional[Any]							=   PRETRAINED_VOCAB_FILES_MAP
      A_						:    str							=   ['input_ids', 'attention_mask']
      A_						:    Optional[Any]							=   None


      def __init__(self	:				Optional[int]    ,      a__	:				int=None    ,      a__	:				str=None    ,      a__	:				Any=None    ,      a__	:				List[Any]="<unk>"    ,      a__	:				List[Any]="<s>"    ,      a__	:				Optional[int]="</s>"    ,      a__	:				List[str]="<pad>"    ,      a__	:				Union[str, Any]=False    ,      a__	:				str=False    ,      **a__	:				Optional[Any]    ,      ):





         """simple docstring"""



         super().__init__(
             a__    ,      a__    ,      tokenizer_file=a__    ,      unk_token=a__    ,      bos_token=a__    ,      eos_token=a__    ,      pad_token=a__    ,      add_prefix_space=a__    ,      clean_up_tokenization_spaces=a__    ,      **a__    ,      )
         __snake_case								=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
         if pre_tok_state.get('''add_prefix_space'''    ,      a__				) != add_prefix_space:
            __snake_case								=       getattr(a__    ,      pre_tok_state.pop('''type'''				)				)
            __snake_case								=       add_prefix_space
            __snake_case								=       pre_tok_class(**a__				)

         __snake_case								=       add_prefix_space


      def        a			(self	:				int    ,      *a__	:				Tuple    ,      **a__	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       kwargs.get('''is_split_into_words'''    ,      a__				)
         if not (self.add_prefix_space or not is_split_into_words):
            raise Exception(
                f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                ''' pretokenized inputs.'''				)

         return super()._batch_encode_plus(*a__    ,      **a__				)


      def        a			(self	:				List[str]    ,      *a__	:				List[str]    ,      **a__	:				List[str]				):





         """simple docstring"""



         __snake_case								=       kwargs.get('''is_split_into_words'''    ,      a__				)

         if not (self.add_prefix_space or not is_split_into_words):
            raise Exception(
                f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                ''' pretokenized inputs.'''				)

         return super()._encode_plus(*a__    ,      **a__				)


      def        a			(self	:				List[Any]    ,      a__	:				str    ,      a__	:				Optional[str] = None				):





         """simple docstring"""



         __snake_case								=       self._tokenizer.model.save(a__    ,      name=a__				)
         return tuple(a__				)


      def        a			(self	:				Tuple    ,      a__	:				"Conversation"				):





         """simple docstring"""



         __snake_case								=       []
         for is_user, text in conversation.iter_texts():
            input_ids.extend(self.encode(a__    ,      add_special_tokens=a__				) + [self.eos_token_id]				)

         if len(a__				) > self.model_max_length:
            __snake_case								=       input_ids[-self.model_max_length :]
         return input_ids

