






"""simple docstring"""







import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter







class 					lowerCamelCase__					(       TensorFormatter[Mapping, np.ndarray, Mapping] ):






    def __init__( self	,A=None	,**A					):
          super().__init__(features=lowercase__					)
          UpperCAmelCase							=       np_array_kwargs






    def 				_UpperCamelCase							( self	,A					):







          if isinstance(lowercase__	,lowercase__					):
                if column and all(
                    isinstance(lowercase__	,np.ndarray					) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column					):
                      return np.stack(lowercase__					)
                else:
                      # don't use np.array(column, dtype=object)
                      # since it fails in certain cases
                      # see https://stackoverflow.com/q/51005699
                      UpperCAmelCase							=       np.empty(len(lowercase__					)	,dtype=lowercase__					)
                      UpperCAmelCase							=       column
                      return out
          return column






    def 				_UpperCamelCase							( self	,A					):







          if isinstance(lowercase__	,(str, bytes, type(lowercase__					))					):
                return value
          elif isinstance(lowercase__	,(np.character, np.ndarray)					) and np.issubdtype(value.dtype	,np.character					):
                return value
          elif isinstance(lowercase__	,np.number					):
                return value

          UpperCAmelCase							=       {}

          if isinstance(lowercase__	,np.ndarray					) and np.issubdtype(value.dtype	,np.integer					):
                UpperCAmelCase							=       {"""dtype""": np.intaa}
          elif isinstance(lowercase__	,np.ndarray					) and np.issubdtype(value.dtype	,np.floating					):
                UpperCAmelCase							=       {"""dtype""": np.floataa}
          elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                import PIL.Image

                if isinstance(lowercase__	,PIL.Image.Image					):
                      return np.asarray(lowercase__	,**self.np_array_kwargs					)

          return np.asarray(lowercase__	,**{**default_dtype, **self.np_array_kwargs}					)






    def 				_UpperCamelCase							( self	,A					):







          if config.TORCH_AVAILABLE and "torch" in sys.modules:
                import torch

                if isinstance(lowercase__	,torch.Tensor					):
                      return self._tensorize(data_struct.detach().cpu().numpy()[()]					)
          if hasattr(lowercase__	,"""__array__"""					) and not isinstance(lowercase__	,(np.ndarray, np.character, np.number)					):
                UpperCAmelCase							=       data_struct.__array__()
          # support for nested types like struct of list of struct
          if isinstance(lowercase__	,np.ndarray					):
                if data_struct.dtype == object:
                      return self._consolidate([self.recursive_tensorize(lowercase__					) for substruct in data_struct]					)
          if isinstance(lowercase__	,(list, tuple)					):
                return self._consolidate([self.recursive_tensorize(lowercase__					) for substruct in data_struct]					)
          return self._tensorize(lowercase__					)






    def 				_UpperCamelCase							( self	,A					):
          return map_nested(self._recursive_tensorize	,lowercase__	,map_list=lowercase__					)






    def 				_UpperCamelCase							( self	,A					):
          UpperCAmelCase							=       self.numpy_arrow_extractor().extract_row(lowercase__					)
          UpperCAmelCase							=       self.python_features_decoder.decode_row(lowercase__					)
          return self.recursive_tensorize(lowercase__					)






    def 				_UpperCamelCase							( self	,A					):
          UpperCAmelCase							=       self.numpy_arrow_extractor().extract_column(lowercase__					)
          UpperCAmelCase							=       self.python_features_decoder.decode_column(lowercase__	,pa_table.column_names[0]					)
          UpperCAmelCase							=       self.recursive_tensorize(lowercase__					)
          UpperCAmelCase							=       self._consolidate(lowercase__					)
          return column






    def 				_UpperCamelCase							( self	,A					):
          UpperCAmelCase							=       self.numpy_arrow_extractor().extract_batch(lowercase__					)
          UpperCAmelCase							=       self.python_features_decoder.decode_batch(lowercase__					)
          UpperCAmelCase							=       self.recursive_tensorize(lowercase__					)
          for column_name in batch:
                UpperCAmelCase							=       self._consolidate(batch[column_name]					)
          return batch





"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


snake_case    =		logging.get_logger(__name__)

snake_case    =		{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

snake_case    =		{
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json',
    },
}




class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				def __init__(				self		,	lowercase__=None		,	lowercase__=None		,	lowercase__=False		,	lowercase__=True		,	lowercase__=False		,	lowercase__="<s>"		,	lowercase__="</s>"		,	lowercase__="<unk>"		,	lowercase__="<sep>"		,	lowercase__="<pad>"		,	lowercase__="<cls>"		,	lowercase__="<mask>"		,	lowercase__=["<eop>", "<eod>"]		,	**lowercase__		,	)    ->    int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	AddedToken(lowercase__		,	lstrip=lowercase__		,	rstrip=lowercase__			) if isinstance(lowercase__		,	lowercase__			) else mask_token

									super().__init__(
									    vocab_file=lowercase__		,	tokenizer_file=lowercase__		,	do_lower_case=lowercase__		,	remove_space=lowercase__		,	keep_accents=lowercase__		,	bos_token=lowercase__		,	eos_token=lowercase__		,	unk_token=lowercase__		,	sep_token=lowercase__		,	pad_token=lowercase__		,	cls_token=lowercase__		,	mask_token=lowercase__		,	additional_special_tokens=lowercase__		,	**lowercase__		,	)

									SCREAMING_SNAKE_CASE				=	3
									SCREAMING_SNAKE_CASE				=	do_lower_case
									SCREAMING_SNAKE_CASE				=	remove_space
									SCREAMING_SNAKE_CASE				=	keep_accents
									SCREAMING_SNAKE_CASE				=	vocab_file
									SCREAMING_SNAKE_CASE				=	False if not self.vocab_file else True

									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.'			)
									SCREAMING_SNAKE_CASE				=	jieba
									SCREAMING_SNAKE_CASE				=	str.maketrans(' \n'		,	'\u2582\u2583'			)


				def 							A					(				self		,	lowercase__		,	lowercase__ = None			)    ->    List[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE				=	[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 							A					(				self		,	lowercase__		,	lowercase__ = None			)    ->    List[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE				=	[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 							A					(				self		,	lowercase__		,	lowercase__ = None			)    ->    Tuple[str]:
									"""simple docstring"""







									if not self.can_save_slow_tokenizer:
														raise ValueError(
														    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
														    'tokenizer.'			)

									if not os.path.isdir(lowercase__			):
														logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
														return
									SCREAMING_SNAKE_CASE				=	os.path.join(
									    lowercase__		,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

									if os.path.abspath(self.vocab_file			) != os.path.abspath(lowercase__			):
														copyfile(self.vocab_file		,	lowercase__			)

									return (out_vocab_file,)


				def 							A					(				self		,	lowercase__		,	*lowercase__		,	**lowercase__			)    ->    Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	[
									    ' '.join([x.translate(self.translator			) for x in self.jieba.cut(lowercase__		,	cut_all=lowercase__			)]			)
									    for text in batch_text_or_text_pairs
									]
									return super()._batch_encode_plus(lowercase__		,	*lowercase__		,	**lowercase__			)




				def 							A					(				self		,	*lowercase__		,	**lowercase__			)    ->    Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	super()._decode(*lowercase__		,	**lowercase__			)
									SCREAMING_SNAKE_CASE				=	text.replace(' '		,	''			).replace('\u2582'		,	' '			).replace('\u2583'		,	'\n'			)
									return text
