




'''simple docstring'''


from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
 from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore







class     SCREAMING_SNAKE_CASE						(	UpperCAmelCase__							):
      __magic_name__             =				c_float






      @property
      def        lowercase_	(   self      :     Tuple					):


           '''simple docstring'''

           return sizeof(self.value					)



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				str   ,						UpperCamelCase__						:				int   ,						UpperCamelCase__						:				List[Any] = 0							):




     """simple docstring"""


     if dimension <= 0:
          a_    :       int    	=     fixed_dimension

     dimension -= num_token_to_add
     return dimension



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Any   ,						UpperCamelCase__						:				int							):




     """simple docstring"""


     return num_parameters * dtype.size



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				int							):




     """simple docstring"""


     from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

     try:
          return AutoProcessor.from_pretrained(UpperCamelCase__							)
     except (ValueError, OSError, KeyError):
          a_    :       int    	=     None, None
          try:
               a_    :       Dict    	=     AutoTokenizer.from_pretrained(UpperCamelCase__							)
          except (OSError, KeyError):
               pass
          try:
               a_    :       Optional[int]    	=     AutoFeatureExtractor.from_pretrained(UpperCamelCase__							)
          except (OSError, KeyError):
               pass

          if tokenizer is not None and feature_extractor is not None:
               raise ValueError(
                   F"Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."							)
          elif tokenizer is None and feature_extractor is None:
               return None
          elif tokenizer is not None:
               return tokenizer
          else:
               return feature_extractor






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowerCAmelCase_			:     str							    =     {}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_			:     Optional[int]							    =     ['CpmTokenizer']

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_			:     Union[str, Any]							    =     ['CpmTokenizerFast']


if TYPE_CHECKING:
 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_cpm import CpmTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_cpm_fast import CpmTokenizerFast

else:
 import sys

 lowerCAmelCase_			:     Any							    =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
