





'''simple docstring'''


from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE	 =							{
    "configuration_mobilenet_v2": [
        "MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV2Config",
        "MobileNetV2OnnxConfig",
    ],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							["MobileNetV2FeatureExtractor"]
       _SCREAMING_SNAKE_CASE	 =							["MobileNetV2ImageProcessor"]


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MobileNetV2ForImageClassification",
           "MobileNetV2ForSemanticSegmentation",
           "MobileNetV2Model",
           "MobileNetV2PreTrainedModel",
           "load_tf_weights_in_mobilenet_v2",
       ]


if TYPE_CHECKING:
       from .configuration_mobilenet_va import (
           MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
           MobileNetVaConfig,
           MobileNetVaOnnxConfig,
       )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
              from .image_processing_mobilenet_va import MobileNetVaImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mobilenet_va import (
                  MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MobileNetVaForImageClassification,
                  MobileNetVaForSemanticSegmentation,
                  MobileNetVaModel,
                  MobileNetVaPreTrainedModel,
                  load_tf_weights_in_mobilenet_va,
              )


else:
       import sys

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









'''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

_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

@add_end_docstrings(__magic_name__		)
class    lowerCAmelCase_		(		__magic_name__		):




 def __init__(	self			,     *_lowerCAmelCase			,     **_lowerCAmelCase		)    ->							List[Any]:
     super().__init__(*_lowerCAmelCase			,     **_lowerCAmelCase		)
     requires_backends(self			,     "vision"		)
     self.check_model_type(_lowerCAmelCase		)




 def __call__(	self			,     _lowerCAmelCase			,     **_lowerCAmelCase		)    ->							str:
     return super().__call__(_lowerCAmelCase			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							Tuple:
     return {}, {}, {}




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      load_image(_lowerCAmelCase		)
     _lowerCAmelCase				=      image.size
     _lowerCAmelCase				=      self.image_processor(images=_lowerCAmelCase			,     return_tensors=self.framework		)
     return model_inputs




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Tuple:
     _lowerCAmelCase				=      self.model(**_lowerCAmelCase		)
     return model_outputs





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


