





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__		(	*_lowercase    ,						**_lowercase						):
             pass






def 							_lowerCAmelCase (  _a   :					Image			)	->			str:
     lowerCAmelCase_					:       Optional[int]    =		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    ,						_lowercase    ,						_lowercase    ,						_lowercase						):
       lowerCAmelCase_					:       Optional[int]    =		DepthEstimationPipeline(model=_lowercase    ,						image_processor=_lowercase						)
       return depth_estimator, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase						):
       lowerCAmelCase_					:       Dict    =		depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
       self.assertEqual({"""predicted_depth""": ANY(torch.Tensor						), """depth""": ANY(Image.Image						)}    ,						_lowercase						)
       import datasets

       lowerCAmelCase_					:       Tuple    =		datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""    ,						"""image"""    ,						split="""test"""						)
       lowerCAmelCase_					:       str    =		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						)},
           ]    ,						_lowercase    ,						)







  @require_tf
  @unittest.skip("""Depth estimation is not implemented in TF"""						)
  def 	UpperCAmelCase__		(	self						):
       pass







  @slow
  @require_torch
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Optional[int]    =		"""Intel/dpt-large"""
       lowerCAmelCase_					:       Any    =		pipeline("""depth-estimation"""    ,						model=_lowercase						)
       lowerCAmelCase_					:       List[str]    =		depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""						)
       lowerCAmelCase_					:       Dict    =		hashimage(outputs["""depth"""]						)

       # This seems flaky.
       # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
       self.assertEqual(nested_simplify(outputs["""predicted_depth"""].max().item()						)    ,						29.304						)
       self.assertEqual(nested_simplify(outputs["""predicted_depth"""].min().item()						)    ,						2.662						)







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







from typing import TYPE_CHECKING

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


UpperCAmelCase_					:   Dict 							=						{
    """configuration_poolformer""": [
        """POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """PoolFormerConfig""",
        """PoolFormerOnnxConfig""",
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Union[str, Any] 							=						["""PoolFormerFeatureExtractor"""]
      UpperCAmelCase_					:   str 							=						["""PoolFormerImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   List[str] 							=						[
          """POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """PoolFormerForImageClassification""",
          """PoolFormerModel""",
          """PoolFormerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_poolformer import (
          POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          PoolFormerConfig,
          PoolFormerOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_poolformer import PoolFormerFeatureExtractor
            from .image_processing_poolformer import PoolFormerImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_poolformer import (
                POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                PoolFormerForImageClassification,
                PoolFormerModel,
                PoolFormerPreTrainedModel,
            )


else:
      import sys

      UpperCAmelCase_					:   Optional[Any] 							=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)
