




"""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

a							      =     logging.get_logger(__name__)





@add_end_docstrings(__lowerCAmelCase				)
class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 def __init__(					self			:			Dict       ,				*_UpperCAmelCase			:			List[Any]       ,				**_UpperCAmelCase			:			List[str]						):
       super().__init__(*_UpperCAmelCase       ,				**_UpperCAmelCase						)
       requires_backends(self       ,				'vision'						)
       self.check_model_type(_UpperCAmelCase						)

 def __call__(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			Union[str, List[str], "Image.Image", List["Image.Image"]]       ,				**_UpperCAmelCase			:			int						):
       return super().__call__(_UpperCAmelCase       ,				**_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Optional[Any]       ,				**_UpperCAmelCase			:			List[str]						):
       return {}, {}, {}

 def      lowerCAmelCase_				(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			List[Any]						):
       _A     =  load_image(_UpperCAmelCase						)
       _A     =  image.size
       _A     =  self.image_processor(images=_UpperCAmelCase       ,				return_tensors=self.framework						)
       return model_inputs

 def      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			Optional[Any]						):
       _A     =  self.model(**_UpperCAmelCase						)
       return model_outputs



 def      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			Optional[int]						):
       _A     =  model_outputs.predicted_depth
       _A     =  torch.nn.functional.interpolate(
           predicted_depth.unsqueeze(1						)       ,				size=self.image_size[::-1]       ,				mode='bicubic'       ,				align_corners=_UpperCAmelCase						)
       _A     =  prediction.squeeze().cpu().numpy()
       _A     =  (output * 255 / np.max(_UpperCAmelCase						)).astype('uint8'						)
       _A     =  Image.fromarray(_UpperCAmelCase						)
       _A     =  {}
       _A     =  predicted_depth
       _A     =  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__)
