




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__							:	Optional[Any]											=		logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase__     )
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						def __init__(		self				:	List[str]			,      *__lowercase				:	Optional[int]			,      **__lowercase				:	Optional[int]		):






							"""simple docstring"""






							super().__init__(*__lowercase			,      **__lowercase		)
							requires_backends(self			,      "vision"		)
							self.check_model_type(__lowercase		)






						def __call__(		self				:	List[str]			,      __lowercase				:	Union[str, List[str], "Image.Image", List["Image.Image"]]			,      **__lowercase				:	str		):






							"""simple docstring"""






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






						def 			snake_case__		(		self				:	Optional[int]			,      **__lowercase				:	List[Any]		):






							"""simple docstring"""






							return {}, {}, {}






						def 			snake_case__		(		self				:	str			,      __lowercase				:	Any		):






							"""simple docstring"""






							snake_case_						       =	load_image(__lowercase		)
							snake_case_						       =	image.size
							snake_case_						       =	self.image_processor(images=__lowercase			,      return_tensors=self.framework		)
							return model_inputs






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	Tuple		):






							"""simple docstring"""






							snake_case_						       =	self.model(**__lowercase		)
							return model_outputs






						def 			snake_case__		(		self				:	List[str]			,      __lowercase				:	Optional[int]		):






							"""simple docstring"""






							snake_case_						       =	model_outputs.predicted_depth
							snake_case_						       =	torch.nn.functional.interpolate(
							    predicted_depth.unsqueeze(1		)			,      size=self.image_size[::-1]			,      mode="bicubic"			,      align_corners=__lowercase		)
							snake_case_						       =	prediction.squeeze().cpu().numpy()
							snake_case_						       =	(output * 2_55 / np.max(__lowercase		)).astype("uint8"		)
							snake_case_						       =	Image.fromarray(__lowercase		)
							snake_case_						       =	{}
							snake_case_						       =	predicted_depth
							snake_case_						       =	depth
							return output_dict






from typing import TYPE_CHECKING

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


lowercase__							:	Union[str, Any]											=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Tuple											=		["DPTFeatureExtractor"]
		lowercase__							:	Optional[Any]											=		["DPTImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Dict											=		[
		    "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

		lowercase__							:	List[Any]											=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
