



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'''






			UpperCamelCase     		=			c_float




			@property
			def 					snake_case__	(  self				:					int					):
					'''simple docstring'''





					return sizeof(self.value					)

def 			a		( _UpperCAmelCase				:   int					,  _UpperCAmelCase				:   int					,  _UpperCAmelCase				:   int = 0		):







		'''simple docstring'''





		if dimension <= 0:
				__UpperCAmelCase       :	Any						=      fixed_dimension

		dimension -= num_token_to_add
		return dimension

def 			a		( _UpperCAmelCase				:   int					,  _UpperCAmelCase				:   ParameterFormat		):







		'''simple docstring'''





		return num_parameters * dtype.size

def 			a		( _UpperCAmelCase				:   str		):







		'''simple docstring'''





		from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

		try:
				return AutoProcessor.from_pretrained(_UpperCAmelCase		)
		except (ValueError, OSError, KeyError):
				__UpperCAmelCase				,   __UpperCAmelCase       :	Dict						=      None, None
				try:
						__UpperCAmelCase       :	Tuple						=      AutoTokenizer.from_pretrained(_UpperCAmelCase		)
				except (OSError, KeyError):
						pass
				try:
						__UpperCAmelCase       :	Union[str, Any]						=      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










from typing import TYPE_CHECKING

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


__A       ={}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A       =["BarthezTokenizer"]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A       =["BarthezTokenizerFast"]


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

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_barthez_fast import BarthezTokenizerFast

else:
				import sys

				__A       =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





