






'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase      :      Tuple            =					{
    "configuration_clipseg": [
        "CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPSegConfig",
        "CLIPSegTextConfig",
        "CLIPSegVisionConfig",
    ],
    "processing_clipseg": ["CLIPSegProcessor"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      str            =					[
	    "CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "CLIPSegModel",
	    "CLIPSegPreTrainedModel",
	    "CLIPSegTextModel",
	    "CLIPSegVisionModel",
	    "CLIPSegForImageSegmentation",
	]

if TYPE_CHECKING:
	from .configuration_clipseg import (
	    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    CLIPSegConfig,
	    CLIPSegTextConfig,
	    CLIPSegVisionConfig,
	)
	from .processing_clipseg import CLIPSegProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_clipseg import (
		    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CLIPSegForImageSegmentation,
		    CLIPSegModel,
		    CLIPSegPreTrainedModel,
		    CLIPSegTextModel,
		    CLIPSegVisionModel,
		)

else:
	import sys

	_lowercase      :      Any            =					_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	):
				__magic_name__    :				List[Any]  		=	["image_processor", "tokenizer"]
				__magic_name__    :				Tuple  		=	"ViTImageProcessor"
				__magic_name__    :				int  		=	("CLIPTokenizer", "CLIPTokenizerFast")




				def __init__(		self      :			List[str]				,   lowerCAmelCase      :			Tuple=None				,   lowerCAmelCase      :			List[str]=None				,   **lowerCAmelCase      :			Optional[int]				)->					Tuple:

										"""simple docstring"""




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

										UpperCAmelCase					    =     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__(lowerCAmelCase				,   lowerCAmelCase				)




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

										"""simple docstring"""




										if text is None and visual_prompt is None and images is None:
																raise ValueError('''You have to specify either text, visual prompt or images.'''				)

										if text is not None and visual_prompt is not None:
																raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''				)

										if text is not None:
																UpperCAmelCase					    =     self.tokenizer(lowerCAmelCase				,   return_tensors=lowerCAmelCase				,   **lowerCAmelCase				)

										if visual_prompt is not None:
																UpperCAmelCase					    =     self.image_processor(lowerCAmelCase				,   return_tensors=lowerCAmelCase				,   **lowerCAmelCase				)

										if images is not None:
																UpperCAmelCase					    =     self.image_processor(lowerCAmelCase				,   return_tensors=lowerCAmelCase				,   **lowerCAmelCase				)

										if visual_prompt is not None and images is not None:
																UpperCAmelCase					    =     {
																    '''pixel_values''': image_features.pixel_values,
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										elif text is not None and images is not None:
																UpperCAmelCase					    =     image_features.pixel_values
																return encoding
										elif text is not None:
																return encoding
										elif visual_prompt is not None:
																UpperCAmelCase					    =     {
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										else:
																return BatchEncoding(data=dict(**lowerCAmelCase				)				,   tensor_type=lowerCAmelCase				)




				def 					a__(		self      :			Optional[int]				,   *lowerCAmelCase      :			List[str]				,   **lowerCAmelCase      :			Dict				)->					Tuple:

										"""simple docstring"""




										return self.tokenizer.batch_decode(*lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			List[Any]				,   *lowerCAmelCase      :			str				,   **lowerCAmelCase      :			List[Any]				)->					Optional[Any]:

										"""simple docstring"""




										return self.tokenizer.decode(*lowerCAmelCase				,   **lowerCAmelCase				)




				@property
				def 					a__(		self      :			Any				)->					Optional[int]:

										"""simple docstring"""




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





				@property
				def 					a__(		self      :			str				)->					List[Any]:

										"""simple docstring"""




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





