

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__     (					*_A						,		**_A):
             pass


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Image       ):





    """simple docstring"""


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





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       __snake_case  (    unittest.TestCase       ):
  __lowerCAmelCase     :	str						 =  MODEL_FOR_DEPTH_ESTIMATION_MAPPING

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A):
      SCREAMING_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 						lowerCAmelCase__     (					self						,		_A						,		_A):
      SCREAMING_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

      SCREAMING_SNAKE_CASE_									=						datasets.load_dataset('hf-internal-testing/fixtures_image_utils'						,		'image'						,		split='test')
      SCREAMING_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 						lowerCAmelCase__     (					self):
      pass

  @slow
  @require_torch
  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						'Intel/dpt-large'
      SCREAMING_SNAKE_CASE_									=						pipeline('depth-estimation'						,		model=_A)
      SCREAMING_SNAKE_CASE_									=						depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg')
      SCREAMING_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 						lowerCAmelCase__     (					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')





import pytest

import datasets


# Import fixture modules as plugins
UpperCamelCase__  :      Union[str, Any]			=    ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		Dict       ):





    """simple docstring"""


    for item in items:
        if any(marker in item.keywords for marker in ['integration', 'unit']       ):
            continue
        item.add_marker(pytest.mark.unit       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[int]       ):





    """simple docstring"""


    config.addinivalue_line('markers' ,   'torchaudio_latest: mark test to run with torchaudio>=0.12'       )


@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE       )
def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		str       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						tmp_path_factory.getbasetemp() / 'cache'
    SCREAMING_SNAKE_CASE_									=						test_hf_cache_home / 'datasets'
    SCREAMING_SNAKE_CASE_									=						test_hf_cache_home / 'metrics'
    SCREAMING_SNAKE_CASE_									=						test_hf_cache_home / 'modules'
    monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE' ,   str(_SCREAMING_SNAKE_CASE       )       )
    monkeypatch.setattr('datasets.config.HF_METRICS_CACHE' ,   str(_SCREAMING_SNAKE_CASE       )       )
    monkeypatch.setattr('datasets.config.HF_MODULES_CACHE' ,   str(_SCREAMING_SNAKE_CASE       )       )
    SCREAMING_SNAKE_CASE_									=						test_hf_datasets_cache / 'downloads'
    monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH' ,   str(_SCREAMING_SNAKE_CASE       )       )
    SCREAMING_SNAKE_CASE_									=						test_hf_datasets_cache / 'downloads' / 'extracted'
    monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH' ,   str(_SCREAMING_SNAKE_CASE       )       )


@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE ,   scope='session'       )
def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    datasets.disable_progress_bar()


@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE       )
def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Any       ):





    """simple docstring"""


    monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS' ,   _SCREAMING_SNAKE_CASE       )





@pytest.fixture
def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Tuple       ):





    """simple docstring"""


    monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING' ,   _SCREAMING_SNAKE_CASE       )


