
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE			:   Union[str, Any]         =     {
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE			:   Optional[Any]         =     ['CamembertTokenizer']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE			:   Tuple         =     ['CamembertTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE			:   Optional[Any]         =     [
		    'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'CamembertForCausalLM',
		    'CamembertForMaskedLM',
		    'CamembertForMultipleChoice',
		    'CamembertForQuestionAnswering',
		    'CamembertForSequenceClassification',
		    'CamembertForTokenClassification',
		    'CamembertModel',
		    'CamembertPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE			:   Optional[Any]         =     [
		    'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFCamembertForCausalLM',
		    'TFCamembertForMaskedLM',
		    'TFCamembertForMultipleChoice',
		    'TFCamembertForQuestionAnswering',
		    'TFCamembertForSequenceClassification',
		    'TFCamembertForTokenClassification',
		    'TFCamembertModel',
		    'TFCamembertPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_camembert import CamembertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_camembert_fast import CamembertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_camembert import (
				    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CamembertForCausalLM,
				    CamembertForMaskedLM,
				    CamembertForMultipleChoice,
				    CamembertForQuestionAnswering,
				    CamembertForSequenceClassification,
				    CamembertForTokenClassification,
				    CamembertModel,
				    CamembertPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_camembert import (
				    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCamembertForCausalLM,
				    TFCamembertForMaskedLM,
				    TFCamembertForMultipleChoice,
				    TFCamembertForQuestionAnswering,
				    TFCamembertForSequenceClassification,
				    TFCamembertForTokenClassification,
				    TFCamembertModel,
				    TFCamembertPreTrainedModel,
				)

else:
		import sys

		__SCREAMING_SNAKE_CASE			:   Optional[int]         =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


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 		__magic_name__   (						snake_case    ):
		_lowerCAmelCase		=	c_float





		@property
		def       _A							(							self    :	int				):
							return sizeof(self.value				)



def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     int							,  __magic_name__						:     int = 0  ):


					'''simple docstring'''

					if dimension <= 0:
										lowerCAmelCase		:					Tuple  	=     fixed_dimension

					dimension -= num_token_to_add
					return dimension
def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     ParameterFormat  ):


					'''simple docstring'''

					return num_parameters * dtype.size
def     UpperCAmelCase__							(       __magic_name__						:     str  ):


					'''simple docstring'''

					from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

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