





'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowercase       						=			{
    'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase       						=			[
       'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ResNetForImageClassification',
       'ResNetModel',
       'ResNetPreTrainedModel',
       'ResNetBackbone',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase       						=			[
       'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFResNetForImageClassification',
       'TFResNetModel',
       'TFResNetPreTrainedModel',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase       						=			[
       'FlaxResNetForImageClassification',
       'FlaxResNetModel',
       'FlaxResNetPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_resnet import (
          RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          ResNetBackbone,
          ResNetForImageClassification,
          ResNetModel,
          ResNetPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_resnet import (
          TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFResNetForImageClassification,
          TFResNetModel,
          TFResNetPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
   import sys

   _lowercase       						=			_LazyModule(__name__, globals()['__file__'], _import_structure)








'''simple docstring'''







import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image
else:
   class 			_lowercase						:







        @staticmethod
        def     UpperCamelCase			( *A__    ,   **A__    ) ->	List[Any]:
         pass

def    __UpperCamelCase	(	a    : Image     ) ->str:
 snake_case				=			hashlib.mda(image.tobytes()     )
 return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 			_lowercase						(       unittest.TestCase					):
     _UpperCAmelCase										=					MODEL_FOR_DEPTH_ESTIMATION_MAPPING







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Union[str, Any]:
      snake_case				=			DepthEstimationPipeline(model=A__    ,   image_processor=A__    )
      return depth_estimator, [
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
      ]







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	List[Any]:
      snake_case				=			depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
      self.assertEqual({'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )}    ,   A__    )
      import datasets

      snake_case				=			datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''    ,   '''image'''    ,   split='''test'''    )
      snake_case				=			depth_estimator(
          [
              Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    ),
              '''http://images.cocodataset.org/val2017/000000039769.jpg''',
              # RGBA
              dataset[0]['''file'''],
              # LA
              dataset[1]['''file'''],
              # L
              dataset[2]['''file'''],
          ]    )
      self.assertEqual(
          [
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
          ]    ,   A__    ,   )







     @require_tf
     @unittest.skip('''Depth estimation is not implemented in TF'''    )
     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      pass







     @slow
     @require_torch
     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			'''Intel/dpt-large'''
      snake_case				=			pipeline('''depth-estimation'''    ,   model=A__    )
      snake_case				=			depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''    )
      snake_case				=			hashimage(outputs['''depth''']    )

      # This seems flaky.
      # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
      self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()    )    ,   2_9.3_0_4    )
      self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()    )    ,   2.6_6_2    )







     @require_torch
     def     UpperCamelCase			( self    ) ->	Any:
      # This is highly irregular to have no small tests.
      self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''    )

