


"""simple docstring"""


from typing import TYPE_CHECKING

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


A     :   int			    =			{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   Union[str, Any]			    =			["DPTFeatureExtractor"]
	A     :   str			    =			["DPTImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   Optional[int]			    =			[
	    "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "DPTForDepthEstimation",
	    "DPTForSemanticSegmentation",
	    "DPTModel",
	    "DPTPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_dpt import DPTFeatureExtractor
		from .image_processing_dpt import DPTImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_dpt import (
		    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DPTForDepthEstimation,
		    DPTForSemanticSegmentation,
		    DPTModel,
		    DPTPreTrainedModel,
		)


else:
	import sys

	A     :   int			    =			_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   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Union[str, Any]				      =["""image_processor""", """tokenizer"""]
							__UpperCAmelCase						:    Optional[Any]				      ="""CLIPImageProcessor"""
							__UpperCAmelCase						:    Union[str, Any]				      =("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")

							def __init__(     self    ,   __a=None    ,   __a=None    ,   **__a	):
										__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	)

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

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

										if images is not None:
													__lowerCAmelCase				    =			self.image_processor(__a    ,   return_tensors=__a    ,   **__a	)

										if text is not None and images 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 			snake_case				(     self    ,   *__a    ,   **__a	):
										return self.tokenizer.batch_decode(*__a    ,   **__a	)

							def 			snake_case				(     self    ,   *__a    ,   **__a	):
										return self.tokenizer.decode(*__a    ,   **__a	)



							@property
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.tokenizer.model_input_names
										__lowerCAmelCase				    =			self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)




