




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

UpperCamelCase__    			=			logging.get_logger(__name__)
@add_end_docstrings(snake_case__      )
class 			a__    (					snake_case__      ):






						def __init__(   self		,			*_A		,			**_A			):




											"""simple docstring"""



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






						def __call__(   self		,			_A		,			**_A			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self		,			**_A			):




											"""simple docstring"""



											return {}, {}, {}






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       load_image(_A			)
											__lowerCAmelCase				   =       image.size
											__lowerCAmelCase				   =       self.image_processor(images=_A		,			return_tensors=self.framework			)
											return model_inputs






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model(**_A			)
											return model_outputs






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       model_outputs.predicted_depth
											__lowerCAmelCase				   =       torch.nn.functional.interpolate(
											    predicted_depth.unsqueeze(1			)		,			size=self.image_size[::-1]		,			mode="bicubic"		,			align_corners=_A			)
											__lowerCAmelCase				   =       prediction.squeeze().cpu().numpy()
											__lowerCAmelCase				   =       (output * 2_5_5 / np.max(_A			)).astype("uint8"			)
											__lowerCAmelCase				   =       Image.fromarray(_A			)
											__lowerCAmelCase				   =       {}
											__lowerCAmelCase				   =       predicted_depth
											__lowerCAmelCase				   =       depth
											return output_dict











"""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


lowerCamelCase_								=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase_								=		["DPTFeatureExtractor"]
  lowerCamelCase_								=		["DPTImageProcessor"]

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

  lowerCamelCase_								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)