






# Lint as: python3

import hashlib
from typing import Union

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Union[str, int, bytes]   )						->     bytes:






	"""simple docstring"""



	if isinstance(__magic_name__      ,     __magic_name__   ):
		# Data already in bytes, returns as it as
		return hash_data
	elif isinstance(__magic_name__      ,     __magic_name__   ):
		# We keep the data as it as for it ot be later encoded to UTF-8
		# However replace `\\` with `/` for Windows compatibility
		UpperCamelCase	:str		=      hash_data.replace("""\\"""      ,     """/"""   )
	elif isinstance(__magic_name__      ,     __magic_name__   ):
		UpperCamelCase	:Optional[Any]		=      str(__magic_name__   )
	else:
		# If data is not of the required type, raise error
		raise InvalidKeyError(__magic_name__   )

	return hash_data.encode("""utf-8"""   )

class        _SCREAMING_SNAKE_CASE			(     _a ):





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

class        _SCREAMING_SNAKE_CASE			(     _a ):





	def __init__(						self							:   Optional[int]					,    __lowerCamelCase							:   Any					,    __lowerCamelCase							:   str					,    __lowerCamelCase							:   Any=""      ):
		UpperCamelCase	:Dict		=      key
		UpperCamelCase	:int		=      duplicate_key_indices
		UpperCamelCase	:Tuple		=      fix_msg
		UpperCamelCase	:List[Any]		=      """Found multiple examples generated with the same key"""
		if len(__lowerCamelCase      ) <= 20:
			UpperCamelCase	:List[Any]		=      F"""\nThe examples at index {', '.join(__lowerCamelCase      )} have the key {key}"""
		else:
			UpperCamelCase	:List[str]		=      F"""\nThe examples at index {', '.join(duplicate_key_indices[:20]      )}... ({len(__lowerCamelCase      ) - 20} more) have the key {key}"""
		UpperCamelCase	:Any		=      """\n""" + fix_msg if fix_msg else """"""
		super().__init__(F"""{self.prefix}{self.err_msg}{self.suffix}"""      )

class        _SCREAMING_SNAKE_CASE			:





	def __init__(						self							:   str					,    __lowerCamelCase							:   str      ):
		UpperCamelCase	:Tuple		=      hashlib.mda(_as_bytes(__lowerCamelCase      )      )





	def 		_A					(						self							:   Any					,    __lowerCamelCase							:   Union[str, int, bytes]      ):
		UpperCamelCase	:List[Any]		=      self._split_mda.copy()
		UpperCamelCase	:Any		=      _as_bytes(__lowerCamelCase      )
		mda.update(__lowerCamelCase      )
		# Convert to integer with hexadecimal conversion
		return int(mda.hexdigest()					,    16      )








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase_					:		int           = {
    '''configuration_unispeech_sat''': ['''UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechSatConfig'''],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_					:		Tuple           = [
	    '''UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''UniSpeechSatForAudioFrameClassification''',
	    '''UniSpeechSatForCTC''',
	    '''UniSpeechSatForPreTraining''',
	    '''UniSpeechSatForSequenceClassification''',
	    '''UniSpeechSatForXVector''',
	    '''UniSpeechSatModel''',
	    '''UniSpeechSatPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_unispeech_sat import (
		    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    UniSpeechSatForAudioFrameClassification,
		    UniSpeechSatForCTC,
		    UniSpeechSatForPreTraining,
		    UniSpeechSatForSequenceClassification,
		    UniSpeechSatForXVector,
		    UniSpeechSatModel,
		    UniSpeechSatPreTrainedModel,
		)

else:
	import sys

	UpperCAmelCase_					:		str           = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
