





'''simple docstring'''


import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{"vocab_file": "spiece.model"}

_SCREAMING_SNAKE_CASE									=				{
    "vocab_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
    }
}

class  lowerCAmelCase_		(		__magic_name__		):




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase="<s>"      ,  _lowerCAmelCase="</s>"      ,  _lowerCAmelCase="<unk>"      ,  _lowerCAmelCase="<sep>"      ,  _lowerCAmelCase="<pad>"      ,  _lowerCAmelCase="<cls>"      ,  _lowerCAmelCase="<mask>"      ,  _lowerCAmelCase=["<eop>", "<eod>"]      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				None:
     _lowerCAmelCase						      =   AddedToken(_lowerCAmelCase      ,  lstrip=_lowerCAmelCase      ,  rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase      ,  _lowerCAmelCase		) else mask_token

     _lowerCAmelCase						      =   {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         do_lower_case=_lowerCAmelCase      ,  remove_space=_lowerCAmelCase      ,  keep_accents=_lowerCAmelCase      ,  bos_token=_lowerCAmelCase      ,  eos_token=_lowerCAmelCase      ,  unk_token=_lowerCAmelCase      ,  sep_token=_lowerCAmelCase      ,  pad_token=_lowerCAmelCase      ,  cls_token=_lowerCAmelCase      ,  mask_token=_lowerCAmelCase      ,  additional_special_tokens=_lowerCAmelCase      ,  sp_model_kwargs=self.sp_model_kwargs      ,  **_lowerCAmelCase      ,  )

     _lowerCAmelCase						      =   3

     _lowerCAmelCase						      =   do_lower_case
     _lowerCAmelCase						      =   remove_space
     _lowerCAmelCase						      =   keep_accents
     _lowerCAmelCase						      =   vocab_file

     _lowerCAmelCase						      =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
     self.sp_model.Load(_lowerCAmelCase		)

     try:
         import jieba
     except ModuleNotFoundError as error:
         raise error.__class__(
             "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
             "See https://pypi.org/project/jieba/ for installation."		)
     _lowerCAmelCase						      =   jieba
     _lowerCAmelCase						      =   str.maketrans(" \n"      ,  "\u2582\u2583"		)




 @property
 # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
 def 				_snake_case (						self		)     ->				Optional[int]:
     return len(self.sp_model		)




 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   {self.convert_ids_to_tokens(_lowerCAmelCase		): i for i in range(self.vocab_size		)}
     vocab.update(self.added_tokens_encoder		)
     return vocab




 def __getstate__(						self		)     ->				Tuple:
     _lowerCAmelCase						      =   self.__dict__.copy()
     _lowerCAmelCase						      =   None
     return state




 def __setstate__(						self      ,  _lowerCAmelCase		)     ->				Dict:
     _lowerCAmelCase						      =   d

     # for backward compatibility
     if not hasattr(self      ,  "sp_model_kwargs"		):
         _lowerCAmelCase						      =   {}

     _lowerCAmelCase						      =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
     self.sp_model.Load(self.vocab_file		)




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				str:
     if self.remove_space:
         _lowerCAmelCase						      =   " ".join(inputs.strip().split()		)
     else:
         _lowerCAmelCase						      =   inputs
     _lowerCAmelCase						      =   outputs.replace("``"      ,  "\""		).replace("''"      ,  "\""		)

     if not self.keep_accents:
         _lowerCAmelCase						      =   unicodedata.normalize("NFKD"      ,  _lowerCAmelCase		)
         _lowerCAmelCase						      =   "".join([c for c in outputs if not unicodedata.combining(_lowerCAmelCase		)]		)
     if self.do_lower_case:
         _lowerCAmelCase						      =   outputs.lower()

     return outputs




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				List[str]:
     _lowerCAmelCase						      =   self.preprocess_text(_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.sp_model.encode(_lowerCAmelCase      ,  out_type=_lowerCAmelCase		)
     _lowerCAmelCase						      =   []
     for piece in pieces:
         if len(_lowerCAmelCase		) > 1 and piece[-1] == str(","		) and piece[-2].isdigit():
             _lowerCAmelCase						      =   self.sp_model.EncodeAsPieces(piece[:-1].replace(_lowerCAmelCase      ,  ""		)		)
             if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                 if len(cur_pieces[0]		) == 1:
                     _lowerCAmelCase						      =   cur_pieces[1:]
                 else:
                     _lowerCAmelCase						      =   cur_pieces[0][1:]
             cur_pieces.append(piece[-1]		)
             new_pieces.extend(_lowerCAmelCase		)
         else:
             new_pieces.append(_lowerCAmelCase		)

     return new_pieces




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				str:
     return self.sp_model.PieceToId(_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     return self.sp_model.IdToPiece(_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   "".join(_lowerCAmelCase		).replace(_lowerCAmelCase      ,  " "		).strip()
     return out_string




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None		)     ->				List[int]:
     _lowerCAmelCase						      =   [self.sep_token_id]
     _lowerCAmelCase						      =   [self.cls_token_id]
     if token_ids_a is None:
         return token_ids_a + sep + cls
     return token_ids_a + sep + token_ids_a + sep + cls




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = False		)     ->				List[int]:

     if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=_lowerCAmelCase      ,  token_ids_a=_lowerCAmelCase      ,  already_has_special_tokens=_lowerCAmelCase		)

     if token_ids_a is not None:
         return ([0] * len(_lowerCAmelCase		)) + [1] + ([0] * len(_lowerCAmelCase		)) + [1, 1]
     return ([0] * len(_lowerCAmelCase		)) + [1, 1]




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None		)     ->				List[int]:
     _lowerCAmelCase						      =   [self.sep_token_id]
     _lowerCAmelCase						      =   [2]

     if token_ids_a is None:
         return len(token_ids_a + sep		) * [0] + cls_segment_id
     return len(token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1] + cls_segment_id




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None		)     ->				Tuple[str]:
     if not os.path.isdir(_lowerCAmelCase		):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
         return
     _lowerCAmelCase						      =   os.path.join(
         _lowerCAmelCase      ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

     if os.path.abspath(self.vocab_file		) != os.path.abspath(_lowerCAmelCase		) and os.path.isfile(self.vocab_file		):
         copyfile(self.vocab_file      ,  _lowerCAmelCase		)
     elif not os.path.isfile(self.vocab_file		):
         with open(_lowerCAmelCase      ,  "wb"		) as fi:
             _lowerCAmelCase						      =   self.sp_model.serialized_model_proto()
             fi.write(_lowerCAmelCase		)

     return (out_vocab_file,)





 def 				_snake_case (						self      ,  *_lowerCAmelCase      ,  **_lowerCAmelCase		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   super()._decode(*_lowerCAmelCase      ,  **_lowerCAmelCase		)
     _lowerCAmelCase						      =   text.replace(" "      ,  ""		).replace("\u2582"      ,  " "		).replace("\u2583"      ,  "\n"		)
     return text









'''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__							(							lowercase   :	Tuple,   lowercase   :	List[str],   lowercase   :	Optional[int],   lowercase   :	List[str],   lowercase   :	List[str]=True,   lowercase   :	str="pt" )						->						Union[str, Any]:





     """simple docstring"""

     _UpperCamelCase							       =			{'''add_prefix_space''': True} if isinstance(lowercase,   lowercase ) and not line.startswith(''' ''' ) else {}
     _UpperCamelCase							       =			padding_side
     return tokenizer(
         [line],   max_length=lowercase,   padding='''max_length''' if pad_to_max_length else None,   truncation=lowercase,   return_tensors=lowercase,   add_special_tokens=lowercase,   **lowercase,   )







def    a__							(							lowercase   :	str,   lowercase   :	int,   lowercase   :	Tuple=None,   )						->						Union[str, Any]:





     """simple docstring"""

     _UpperCamelCase							       =			input_ids.ne(lowercase ).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	(       __magic_name__					):






       """simple docstring"""





       def __init__(				self  :						Optional[int]			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						str			,					lowerCAmelCase__  :						Tuple			,					lowerCAmelCase__  :						str="train"			,					lowerCAmelCase__  :						List[str]=None			,					lowerCAmelCase__  :						Dict=None			,					lowerCAmelCase__  :						Optional[Any]=None			,					lowerCAmelCase__  :						Dict=""			,					)       ->	Any:

            '''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  :						Optional[Any]	)       ->	List[Any]:

            '''simple docstring'''




            return len(self.src_lens	)





       def __getitem__(				self  :						Optional[Any]			,					lowerCAmelCase__  :						Any	)       ->	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 snake_case__		(				lowerCAmelCase__  :						List[Any]	)       ->	Optional[int]:

            '''simple docstring'''




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







       def snake_case__		(				self  :						Dict			,					lowerCAmelCase__  :						List[str]	)       ->	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

lowercase__		:     Union[str, Any]			=							getLogger(__name__)







def    a__							(							lowercase   :	List[List] )						->						Optional[int]:





     """simple docstring"""

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







def    a__							(							lowercase   :	str )						->						None:





     """simple docstring"""

     _UpperCamelCase							       =			get_git_info()
     save_json(lowercase,   os.path.join(lowercase,   '''git_log.json''' ) )







def    a__							(							lowercase   :	Optional[int],   lowercase   :	List[str],   lowercase   :	str=4,   **lowercase   :	Optional[Any] )						->						Any:





     """simple docstring"""

     with open(lowercase,   '''w''' ) as f:
          json.dump(lowercase,   lowercase,   indent=lowercase,   **lowercase )







def    a__							(							lowercase   :	Union[str, Any] )						->						Optional[int]:





     """simple docstring"""

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







def    a__							(							)						->						List[Any]:





     """simple docstring"""

     _UpperCamelCase							       =			git.Repo(search_parent_directories=lowercase )
     _UpperCamelCase							       =			{
         '''repo_id''': str(lowercase ),
         '''repo_sha''': str(repo.head.object.hexsha ),
         '''repo_branch''': str(repo.active_branch ),
         '''hostname''': str(socket.gethostname() ),
     }
     return repo_infos







def    a__							(							lowercase   :	Callable,   lowercase   :	Iterable )						->						List:





     """simple docstring"""

     return list(map(lowercase,   lowercase ) )







def    a__							(							lowercase   :	List[Any],   lowercase   :	Dict )						->						Optional[int]:





     """simple docstring"""

     with open(lowercase,   '''wb''' ) as f:
          return pickle.dump(lowercase,   lowercase )







def    a__							(							lowercase   :	Tuple )						->						Tuple:





     """simple docstring"""

     def remove_articles(lowercase   :	Tuple ):
          return re.sub(r'''\b(a|an|the)\b''',   ''' ''',   lowercase )

     def white_space_fix(lowercase   :	Tuple ):
          return " ".join(text.split() )

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

     def lower(lowercase   :	Tuple ):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(lowercase ) ) ) )







def    a__							(							lowercase   :	Tuple,   lowercase   :	Union[str, Any] )						->						Dict:





     """simple docstring"""

     _UpperCamelCase							       =			normalize_answer(lowercase ).split()
     _UpperCamelCase							       =			normalize_answer(lowercase ).split()
     _UpperCamelCase							       =			Counter(lowercase ) & Counter(lowercase )
     _UpperCamelCase							       =			sum(common.values() )
     if num_same == 0:
          return 0
     _UpperCamelCase							       =			1.0 * num_same / len(lowercase )
     _UpperCamelCase							       =			1.0 * num_same / len(lowercase )
     _UpperCamelCase							       =			(2 * precision * recall) / (precision + recall)
     return fa







def    a__							(							lowercase   :	Tuple,   lowercase   :	Any )						->						List[str]:





     """simple docstring"""

     return normalize_answer(lowercase ) == normalize_answer(lowercase )







def    a__							(							lowercase   :	List[str],   lowercase   :	List[str] )						->						Dict:





     """simple docstring"""

     assert len(lowercase ) == len(lowercase )
     _UpperCamelCase							       =			0
     for hypo, pred in zip(lowercase,   lowercase ):
          em += exact_match_score(lowercase,   lowercase )
     if len(lowercase ) > 0:
          em /= len(lowercase )
     return {"em": em}







def    a__							(							lowercase   :	Tuple )						->						Union[str, Any]:





     """simple docstring"""

     return model_prefix.startswith('''rag''' )







def    a__							(							lowercase   :	int,   lowercase   :	List[Any],   lowercase   :	List[Any] )						->						Optional[int]:





     """simple docstring"""

     _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(lowercase,   lowercase,   lowercase ):
               if not hasattr(lowercase,   lowercase ) and not hasattr(lowercase,   equivalent_param[p] ):
                    logger.info('''config doesn\'t have a `{}` attribute'''.format(lowercase ) )
                    delattr(lowercase,   lowercase )
                    continue
               _UpperCamelCase							       =			p if hasattr(lowercase,   lowercase ) else equivalent_param[p]
               setattr(lowercase,   lowercase,   getattr(lowercase,   lowercase ) )
               delattr(lowercase,   lowercase )
     return hparams, config



