






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       __SCREAMING_SNAKE_CASE   :






											@staticmethod
											def  UpperCamelCase__ (		*_UpperCamelCase       ,  **_UpperCamelCase				):






																	"""simple docstring"""
																	pass







def 		_UpperCamelCase				(      UpperCamelCase_    :   Image						)					->				str:

						"""simple docstring"""



						lowerCAmelCase__        =  hashlib.mda(image.tobytes()						)
						return m.hexdigest()


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       __SCREAMING_SNAKE_CASE   (				unittest.TestCase):
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   MODEL_FOR_DEPTH_ESTIMATION_MAPPING






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  DepthEstimationPipeline(model=_UpperCamelCase       ,  image_processor=_UpperCamelCase				)
												return depth_estimator, [
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												]






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
												self.assertEqual({'predicted_depth': ANY(torch.Tensor				), 'depth': ANY(Image.Image				)}       ,  _UpperCamelCase				)
												import datasets

												lowerCAmelCase__        =  datasets.load_dataset('hf-internal-testing/fixtures_image_utils'       ,  'image'       ,  split='test'				)
												lowerCAmelCase__        =  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				)},
												    ]       ,  _UpperCamelCase       ,  )






						@require_tf
						@unittest.skip('Depth estimation is not implemented in TF'				)
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												pass






						@slow
						@require_torch
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  'Intel/dpt-large'
												lowerCAmelCase__        =  pipeline('depth-estimation'       ,  model=_UpperCamelCase				)
												lowerCAmelCase__        =  depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'				)
												lowerCAmelCase__        =  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  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# 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      :      Dict             =       {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      str             =       [
					    """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      :      List[Any]             =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





