

'''simple docstring'''

import warnings

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







class lowerCamelCase      (						__lowerCAmelCase							):
				snake_case_           =   ['''image_processor''', '''tokenizer''']
				snake_case_           =   '''VideoMAEImageProcessor'''
				snake_case_           =   ('''CLIPTokenizer''', '''CLIPTokenizerFast''')

				def __init__(					self,	lowercase_=None,	lowercase_=None,	**lowercase_   )						->       List[str]:
					snake_case							 =				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							 =				kwargs.pop('feature_extractor'   )

					snake_case							 =				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							 =				self.image_processor

				def __call__(					self,	lowercase_=None,	lowercase_=None,	lowercase_=None,	**lowercase_   )						->       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							 =				self.tokenizer(lowercase_,	return_tensors=lowercase_,	**lowercase_   )

					if videos is not None:
						snake_case							 =				self.image_processor(lowercase_,	return_tensors=lowercase_,	**lowercase_   )

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

				def 				_lowerCamelCase						(					self,	*lowercase_,	**lowercase_   )						->       List[Any]:
					return self.tokenizer.batch_decode(*lowercase_,	**lowercase_   )

				def 				_lowerCamelCase						(					self,	*lowercase_,	**lowercase_   )						->       Dict:
					return self.tokenizer.decode(*lowercase_,	**lowercase_   )

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

				@property
				def 				_lowerCamelCase						(					self   )						->       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 				_lowerCamelCase						(					self   )						->       List[str]:
					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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_						   =			{
    "configuration_led": ["LED_PRETRAINED_CONFIG_ARCHIVE_MAP", "LEDConfig"],
    "tokenization_led": ["LEDTokenizer"],
}

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =			[
						    "LED_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "LEDForConditionalGeneration",
						    "LEDForQuestionAnswering",
						    "LEDForSequenceClassification",
						    "LEDModel",
						    "LEDPreTrainedModel",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =			["TFLEDForConditionalGeneration", "TFLEDModel", "TFLEDPreTrainedModel"]


if TYPE_CHECKING:
						from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
						from .tokenization_led import LEDTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_led_fast import LEDTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_led import (
												    LED_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LEDForConditionalGeneration,
												    LEDForQuestionAnswering,
												    LEDForSequenceClassification,
												    LEDModel,
												    LEDPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
						import sys

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





