





'''simple docstring'''







import warnings

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





class     _UpperCAmelCase							(						lowerCAmelCase__   ):
		a   :   Union[str, Any]     =['image_processor', 'tokenizer']
		a   :   Tuple     ='VideoMAEImageProcessor'
		a   :   Optional[Any]     =('CLIPTokenizer', 'CLIPTokenizerFast')
		def __init__(  self,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,**__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				None
					if "feature_extractor" in kwargs:
								warnings.warn(
								    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
								    """ instead.""",_A,)
								__lowerCAmelCase											=				kwargs.pop("""feature_extractor"""			)

					__lowerCAmelCase											=				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__(_A,_A			)
					__lowerCAmelCase											=				self.image_processor
		def __call__(  self,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,**__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					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:
								__lowerCAmelCase											=				self.tokenizer(_A,return_tensors=_A,**_A			)

					if videos is not None:
								__lowerCAmelCase											=				self.image_processor(_A,return_tensors=_A,**_A			)

					if text is not None and videos is not None:
								__lowerCAmelCase											=				image_features.pixel_values
								return encoding
					elif text is not None:
								return encoding
					else:
								return BatchEncoding(data=dict(**_A			),tensor_type=_A			)
		def   lowerCamelCase__			(  self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					return self.tokenizer.batch_decode(*_A,**_A			)
		def   lowerCamelCase__			(  self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					return self.tokenizer.decode(*_A,**_A			)
		@property
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					return ["input_ids", "attention_mask", "position_ids", "pixel_values"]
		@property
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					warnings.warn(
					    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""",_A,)
					return self.image_processor_class







		@property
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					warnings.warn(
					    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""",_A,)
					return self.image_processor




from typing import TYPE_CHECKING

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


UpperCamelCase__     :					int								=			{
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					str								=			["CamembertTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Any								=			["CamembertTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Union[str, 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:
       UpperCamelCase__     :					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

       UpperCamelCase__     :					List[str]								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


