



'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCamelCase  						=			{
    """configuration_videomae""": ["""VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VideoMAEConfig"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_UpperCamelCase  						=			[
		    """VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """VideoMAEForPreTraining""",
		    """VideoMAEModel""",
		    """VideoMAEPreTrainedModel""",
		    """VideoMAEForVideoClassification""",
		]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_UpperCamelCase  						=			["""VideoMAEFeatureExtractor"""]
		_UpperCamelCase  						=			["""VideoMAEImageProcessor"""]

if TYPE_CHECKING:
		from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_videomae import (
				    VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    VideoMAEForPreTraining,
				    VideoMAEForVideoClassification,
				    VideoMAEModel,
				    VideoMAEPreTrainedModel,
				)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_videomae import VideoMAEFeatureExtractor
				from .image_processing_videomae import VideoMAEImageProcessor

else:
		import sys

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






'''simple docstring'''

import warnings

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




class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				Tuple   							=       ["""image_processor""", """tokenizer"""]
			__magic_name__ :				List[str]   							=       """AutoImageProcessor"""
			__magic_name__ :				List[str]   							=       """AutoTokenizer"""



			def __init__(							self				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):
									snake_case :       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."				,			snake_case_				,			)
															snake_case :       str				=    kwargs.pop("feature_extractor"					)

									snake_case :       Optional[int]				=    image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError("You have to specify an image_processor."					)
									if tokenizer is None:
															raise ValueError("You have to specify a tokenizer."					)

									super().__init__(snake_case_				,			snake_case_					)
									snake_case :       Optional[int]				=    self.image_processor



			def __call__(							self				,			snake_case_=None				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):

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

									if text is not None:
															snake_case :       Optional[int]				=    self.tokenizer(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

									if images is not None:
															snake_case :       List[Any]				=    self.image_processor(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

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



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.batch_decode(*snake_case_				,			**snake_case_					)



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.decode(*snake_case_				,			**snake_case_					)



			@property
			def   lowerCamelCase (							self					):
									snake_case :       List[str]				=    self.tokenizer.model_input_names
									snake_case :       Any				=    self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)



			@property
			def   lowerCamelCase (							self					):
									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,			snake_case_				,			)
									return self.image_processor_class






			@property
			def   lowerCamelCase (							self					):
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,			snake_case_				,			)
									return self.image_processor





