


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  snake_case_     :
											@staticmethod
											def 				lowerCamelCase__(   *__snake_case     :Tuple    ,**__snake_case     :Tuple   )      ->					List[str]:
																		pass






def 							__lowercase     (				__lowerCAmelCase							:       Image     ):
							a__    =  hashlib.mda(image.tobytes()     )
							return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class  snake_case_     (unittest.TestCase   ):
					UpperCAmelCase__      :					List[Any]					   =    MODEL_FOR_DEPTH_ESTIMATION_MAPPING
					def 				lowerCamelCase__(   self     :List[str]    ,__snake_case     :List[str]    ,__snake_case     :str    ,__snake_case     :int   )      ->					List[str]:
												a__    =  DepthEstimationPipeline(model=__snake_case    ,image_processor=__snake_case   )
												return depth_estimator, [
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												]
					def 				lowerCamelCase__(   self     :List[Any]    ,__snake_case     :Union[str, Any]    ,__snake_case     :int   )      ->					str:
												a__    =  depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
												self.assertEqual({'predicted_depth': ANY(torch.Tensor   ), 'depth': ANY(Image.Image   )}    ,__snake_case   )
												import datasets

												a__    =  datasets.load_dataset('hf-internal-testing/fixtures_image_utils'    ,'image'    ,split='test'   )
												a__    =  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   )},
												    ]    ,__snake_case    ,)
					@require_tf
					@unittest.skip('Depth estimation is not implemented in TF'   )
					def 				lowerCamelCase__(   self     :List[Any]   )      ->					List[Any]:
												pass
					@slow
					@require_torch
					def 				lowerCamelCase__(   self     :Dict   )      ->					Dict:
												a__    =  'Intel/dpt-large'
												a__    =  pipeline('depth-estimation'    ,model=__snake_case   )
												a__    =  depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'   )
												a__    =  hashimage(outputs['depth']   )

												# This seems flaky.
												# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
												self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()   )    ,29.3_04   )
												self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()   )    ,2.6_62   )



					@require_torch
					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					Optional[int]:
												# 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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case							: Optional[Any]      =   {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Optional[int]      =   [
						    '''FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''FocalNetForImageClassification''',
						    '''FocalNetForMaskedImageModeling''',
						    '''FocalNetBackbone''',
						    '''FocalNetModel''',
						    '''FocalNetPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_focalnet import (
												    FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FocalNetBackbone,
												    FocalNetForImageClassification,
												    FocalNetForMaskedImageModeling,
												    FocalNetModel,
												    FocalNetPreTrainedModel,
												)

else:
						import sys

						snake_case							: Union[str, Any]      =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




