



"""simple docstring"""







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 			_a     (   SCREAMING_SNAKE_CASE__):
			__magic_name__    				=    ["""image_processor""", """tokenizer"""]
			__magic_name__    				=    """VideoMAEImageProcessor"""
			__magic_name__    				=    ("""CLIPTokenizer""", """CLIPTokenizerFast""")



			def __init__( self       :							Optional[int]      ,  _lowercase       :							str=None      ,  _lowercase       :							str=None      ,  **_lowercase       :							str		)       ->				Optional[Any]:
									snake_case      : Union[str, Any]							=   None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
															    " instead."      ,  _lowercase      ,  )
															snake_case      : Any							=   kwargs.pop("feature_extractor"		)

									snake_case      : List[str]							=   image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError("You need to specify an `image_processor`."		)
									if tokenizer is None:
															raise ValueError("You need to specify a `tokenizer`."		)

									super().__init__(_lowercase      ,  _lowercase		)
									snake_case      : Any							=   self.image_processor



			def __call__( self       :							List[str]      ,  _lowercase       :							List[Any]=None      ,  _lowercase       :							Tuple=None      ,  _lowercase       :							Any=None      ,  **_lowercase       :							Optional[int]		)       ->				Union[str, Any]:

									if text is None and videos is None:
															raise ValueError("You have to specify either text or videos. Both cannot be none."		)

									if text is not None:
															snake_case      : List[str]							=   self.tokenizer(_lowercase      ,  return_tensors=_lowercase      ,  **_lowercase		)

									if videos is not None:
															snake_case      : Union[str, Any]							=   self.image_processor(_lowercase      ,  return_tensors=_lowercase      ,  **_lowercase		)

									if text is not None and videos is not None:
															snake_case      : Any							=   image_features.pixel_values
															return encoding
									elif text is not None:
															return encoding
									else:
															return BatchEncoding(data=dict(**_lowercase		)      ,  tensor_type=_lowercase		)



			def 							__lowercase				( self       :							int      ,  *_lowercase       :							Tuple      ,  **_lowercase       :							Dict		)       ->				Any:
									return self.tokenizer.batch_decode(*_lowercase      ,  **_lowercase		)



			def 							__lowercase				( self       :							Dict      ,  *_lowercase       :							int      ,  **_lowercase       :							List[str]		)       ->				Optional[Any]:
									return self.tokenizer.decode(*_lowercase      ,  **_lowercase		)



			@property
			def 							__lowercase				( self       :							Any		)       ->				Optional[Any]:
									return ["input_ids", "attention_mask", "position_ids", "pixel_values"]



			@property
			def 							__lowercase				( self       :							Tuple		)       ->				List[str]:
									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,  _lowercase      ,  )
									return self.image_processor_class



			@property
			def 							__lowercase				( self       :							Optional[Any]		)       ->				int:
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,  _lowercase      ,  )
									return self.image_processor








"""simple docstring"""







from typing import TYPE_CHECKING

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


A    =	{
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

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

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    '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:
			A    =	[
			    '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

			A    =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



