






# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

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


if TYPE_CHECKING:
       import tensorflow as tf
class    a   (			TensorFormatter[Mapping, 'tf.Tensor', Mapping]       ):



      """simple docstring"""


      def __init__(				self :      Any		,				__lowercase :      Optional[Any]=None		,				**__lowercase :      str )							->				str:
        super().__init__(features=__lowercase )
        __UpperCAmelCase							:     Dict									=  tf_tensor_kwargs
        import tensorflow as tf  # noqa: import tf at initialization
      def      UpperCAmelCase				(				self :      str		,				__lowercase :      Dict )							->				Any:
        import tensorflow as tf

        if isinstance(__lowercase		,				__lowercase ) and column:
          if all(
              isinstance(__lowercase		,				tf.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
            return tf.stack(__lowercase )
          elif all(
              isinstance(__lowercase		,				(tf.Tensor, tf.RaggedTensor) ) and x.ndim == 1 and x.dtype == column[0].dtype
              for x in column ):
            # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
            return tf.ragged.stack(__lowercase )

        return column
      def      UpperCAmelCase				(				self :      Optional[int]		,				__lowercase :      str )							->				str:
        import tensorflow as tf

        if value is None:
          return value

        __UpperCAmelCase							:     int									=  {}

        if isinstance(__lowercase		,				(np.number, np.ndarray) ) and np.issubdtype(value.dtype		,				np.integer ):
          __UpperCAmelCase							:     int									=  {"""dtype""": tf.intaa}
        elif isinstance(__lowercase		,				(np.number, np.ndarray) ) and np.issubdtype(value.dtype		,				np.floating ):
          __UpperCAmelCase							:     Union[str, Any]									=  {"""dtype""": tf.floataa}
        elif config.PIL_AVAILABLE and "PIL" in sys.modules:
          import PIL.Image

          if isinstance(__lowercase		,				PIL.Image.Image ):
            __UpperCAmelCase							:     List[Any]									=  np.asarray(__lowercase )

        return tf.convert_to_tensor(__lowercase		,				**{**default_dtype, **self.tf_tensor_kwargs} )
      def      UpperCAmelCase				(				self :      List[str]		,				__lowercase :      int )							->				Dict:
        import tensorflow as tf

        # support for torch, tf, jax etc.
        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		,				tf.Tensor ):
          __UpperCAmelCase							:     Union[str, Any]									=  data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(__lowercase		,				np.ndarray ):
          if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
            return self._consolidate([self.recursive_tensorize(__lowercase ) for substruct in data_struct] )
        elif isinstance(__lowercase		,				(list, tuple) ):
          return self._consolidate([self.recursive_tensorize(__lowercase ) for substruct in data_struct] )
        return self._tensorize(__lowercase )
      def      UpperCAmelCase				(				self :      Tuple		,				__lowercase :      dict )							->				List[Any]:
        return map_nested(self._recursive_tensorize		,				__lowercase		,				map_list=__lowercase )
      def      UpperCAmelCase				(				self :      int		,				__lowercase :      pa.Table )							->				Mapping:
        __UpperCAmelCase							:     int									=  self.numpy_arrow_extractor().extract_row(__lowercase )
        __UpperCAmelCase							:     str									=  self.python_features_decoder.decode_row(__lowercase )
        return self.recursive_tensorize(__lowercase )
      def      UpperCAmelCase				(				self :      Optional[int]		,				__lowercase :      pa.Table )							->				"tf.Tensor":
        __UpperCAmelCase							:     int									=  self.numpy_arrow_extractor().extract_column(__lowercase )
        __UpperCAmelCase							:     Union[str, Any]									=  self.python_features_decoder.decode_column(__lowercase		,				pa_table.column_names[0] )
        __UpperCAmelCase							:     Dict									=  self.recursive_tensorize(__lowercase )
        __UpperCAmelCase							:     Optional[Any]									=  self._consolidate(__lowercase )
        return column




      def      UpperCAmelCase				(				self :      List[Any]		,				__lowercase :      pa.Table )							->				Mapping:
        __UpperCAmelCase							:     Union[str, Any]									=  self.numpy_arrow_extractor().extract_batch(__lowercase )
        __UpperCAmelCase							:     int									=  self.python_features_decoder.decode_batch(__lowercase )
        __UpperCAmelCase							:     Dict									=  self.recursive_tensorize(__lowercase )
        for column_name in batch:
          __UpperCAmelCase							:     Any									=  self._consolidate(batch[column_name] )
        return batch










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

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


a		:	Any      					=			logging.get_logger(__name__)

a		:	Dict      					=			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a		:	Optional[int]      					=			{
    "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    a   (			lowercase__       ):



      """simple docstring"""


      def __init__(				self :      List[Any]		,				__lowercase :      Union[str, Any]=None		,				__lowercase :      Tuple=None		,				__lowercase :      Optional[int]=False		,				__lowercase :      Any=True		,				__lowercase :      List[str]=False		,				__lowercase :      Union[str, Any]="<s>"		,				__lowercase :      List[str]="</s>"		,				__lowercase :      Dict="<unk>"		,				__lowercase :      List[str]="<sep>"		,				__lowercase :      int="<pad>"		,				__lowercase :      Any="<cls>"		,				__lowercase :      int="<mask>"		,				__lowercase :      Tuple=["<eop>", "<eod>"]		,				**__lowercase :      Union[str, Any]		,				)							->				Optional[Any]:
        __UpperCAmelCase							:     Any									=  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		,				)

        __UpperCAmelCase							:     Optional[int]									=  3
        __UpperCAmelCase							:     Dict									=  do_lower_case
        __UpperCAmelCase							:     Union[str, Any]									=  remove_space
        __UpperCAmelCase							:     Tuple									=  keep_accents
        __UpperCAmelCase							:     int									=  vocab_file
        __UpperCAmelCase							:     Dict									=  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.""" )
        __UpperCAmelCase							:     Dict									=  jieba
        __UpperCAmelCase							:     str									=  str.maketrans(""" \n"""		,				"""\u2582\u2583""" )
      def      UpperCAmelCase				(				self :      Tuple		,				__lowercase :      List[int]		,				__lowercase :      Optional[List[int]] = None )							->				List[int]:
        __UpperCAmelCase							:     List[str]									=  [self.sep_token_id]
        __UpperCAmelCase							:     List[str]									=  [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      UpperCAmelCase				(				self :      Union[str, Any]		,				__lowercase :      List[int]		,				__lowercase :      Optional[List[int]] = None )							->				List[int]:
        __UpperCAmelCase							:     Dict									=  [self.sep_token_id]
        __UpperCAmelCase							:     List[str]									=  [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      UpperCAmelCase				(				self :      Union[str, Any]		,				__lowercase :      str		,				__lowercase :      Optional[str] = None )							->				Tuple[str]:
        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
        __UpperCAmelCase							:     List[str]									=  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      UpperCAmelCase				(				self :      Optional[Any]		,				__lowercase :      Any		,				*__lowercase :      Dict		,				**__lowercase :      int )							->				Optional[Any]:
        __UpperCAmelCase							:     List[str]									=  [
            """ """.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      UpperCAmelCase				(				self :      int		,				*__lowercase :      List[Any]		,				**__lowercase :      Tuple )							->				List[str]:
        __UpperCAmelCase							:     List[str]									=  super()._decode(*__lowercase		,				**__lowercase )
        __UpperCAmelCase							:     List[Any]									=  text.replace(""" """		,				"""""" ).replace("""\u2582"""		,				""" """ ).replace("""\u2583"""		,				"""\n""" )
        return text


