


'''simple docstring'''



from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
	from PIL import Image

	from ..image_utils import load_image

if is_torch_available():
	import torch

	from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

_lowercase      :      Union[str, Any]            =					logging.get_logger(__name__)






@add_end_docstrings(lowerCAmelCase	)
class 	UpperCamelCase__(       lowerCAmelCase	):




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

										"""simple docstring"""




										super().__init__(*lowerCAmelCase				,   **lowerCAmelCase				)
										requires_backends(self				,   '''vision'''				)
										self.check_model_type(lowerCAmelCase				)




				def __call__(		self      :			List[Any]				,   lowerCAmelCase      :			Union[str, List[str], "Image.Image", List["Image.Image"]]				,   **lowerCAmelCase      :			Optional[int]				)->					Optional[int]:

										"""simple docstring"""




										return super().__call__(lowerCAmelCase				,   **lowerCAmelCase				)




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

										"""simple docstring"""




										return {}, {}, {}




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

										"""simple docstring"""




										UpperCAmelCase					    =     load_image(lowerCAmelCase				)
										UpperCAmelCase					    =     image.size
										UpperCAmelCase					    =     self.image_processor(images=lowerCAmelCase				,   return_tensors=self.framework				)
										return model_inputs




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

										"""simple docstring"""




										UpperCAmelCase					    =     self.model(**lowerCAmelCase				)
										return model_outputs





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

										"""simple docstring"""




										UpperCAmelCase					    =     model_outputs.predicted_depth
										UpperCAmelCase					    =     torch.nn.functional.interpolate(
										    predicted_depth.unsqueeze(1				)				,   size=self.image_size[::-1]				,   mode='''bicubic'''				,   align_corners=lowerCAmelCase				)
										UpperCAmelCase					    =     prediction.squeeze().cpu().numpy()
										UpperCAmelCase					    =     (output * 255 / np.max(lowerCAmelCase				)).astype('''uint8'''				)
										UpperCAmelCase					    =     Image.fromarray(lowerCAmelCase				)
										UpperCAmelCase					    =     {}
										UpperCAmelCase					    =     predicted_depth
										UpperCAmelCase					    =     depth
										return output_dict









'''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





