

"""simple docstring"""




# Lint as: python3

import hashlib
from typing import Union
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					if isinstance(lowercase__     ,				lowercase__ ):
										# Data already in bytes, returns as it as
										return hash_data
					elif isinstance(lowercase__     ,				lowercase__ ):
										# We keep the data as it as for it ot be later encoded to UTF-8
										# However replace `\\` with `/` for Windows compatibility
										A     =   hash_data.replace("\\"     ,				"/" )
					elif isinstance(lowercase__     ,				lowercase__ ):
										A     =   str(lowercase__ )
					else:
										# If data is not of the required type, raise error
										raise InvalidKeyError(lowercase__ )

					return hash_data.encode("utf-8" )





class      __UpperCamelCase					(  _A		):


		def __init__(self					:						List[Any]	,	__SCREAMING_SNAKE_CASE					:						int):
							A     =   "\nFAILURE TO GENERATE DATASET: Invalid key type detected"
							A     =   F"""\nFound Key {hash_data} of type {type(__SCREAMING_SNAKE_CASE)}"""
							A     =   "\nKeys should be either str, int or bytes type"
							super().__init__(F"""{self.prefix}{self.err_msg}{self.suffix}""")





class      __UpperCamelCase					(  _A		):


		def __init__(self					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						Tuple	,	__SCREAMING_SNAKE_CASE					:						Tuple	,	__SCREAMING_SNAKE_CASE					:						int=""):
							A     =   key
							A     =   duplicate_key_indices
							A     =   fix_msg
							A     =   "Found multiple examples generated with the same key"
							if len(__SCREAMING_SNAKE_CASE) <= 2_0:
												A     =   F"""\nThe examples at index {", ".join(__SCREAMING_SNAKE_CASE)} have the key {key}"""
							else:
												A     =   F"""\nThe examples at index {", ".join(duplicate_key_indices[:2_0])}... ({len(__SCREAMING_SNAKE_CASE) - 2_0} more) have the key {key}"""
							A     =   "\n" + fix_msg if fix_msg else ""
							super().__init__(F"""{self.prefix}{self.err_msg}{self.suffix}""")





class      __UpperCamelCase					:


		def __init__(self					:						Tuple	,	__SCREAMING_SNAKE_CASE					:						str):
							A     =   hashlib.mda(_as_bytes(__SCREAMING_SNAKE_CASE))


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]	,	__SCREAMING_SNAKE_CASE					:						Union[str, int, bytes]):
							A     =   self._split_mda.copy()
							A     =   _as_bytes(__SCREAMING_SNAKE_CASE)
							mda.update(__SCREAMING_SNAKE_CASE)
							# Convert to integer with hexadecimal conversion
							return int(mda.hexdigest()	,	1_6)













'''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 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    Dict				       =					c_float




      @property
      def 							lowerCamelCase     (					self						):
             return sizeof(self.value						)






def  lowerCAmelCase__		(					a_  :     int			,						a_  :     int			,						a_  :     int = 0			)   -> int:
       # < 0 is possible if using a dynamic axis
       if dimension <= 0:
              UpperCAmelCase__	:   List[Any]												=	fixed_dimension

       dimension -= num_token_to_add
       return dimension






def  lowerCAmelCase__		(					a_  :     int			,						a_  :     ParameterFormat			)   -> int:
       return num_parameters * dtype.size






def  lowerCAmelCase__		(					a_  :     str			)   -> Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
       from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

       try:
              return AutoProcessor.from_pretrained(a_			)
       except (ValueError, OSError, KeyError):
              UpperCAmelCase__							,							UpperCAmelCase__	:   Dict												=	None, None
              try:
                     UpperCAmelCase__	:   Union[str, Any]												=	AutoTokenizer.from_pretrained(a_			)
              except (OSError, KeyError):
                     pass
              try:
                     UpperCAmelCase__	:   List[str]												=	AutoFeatureExtractor.from_pretrained(a_			)
              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