




'''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      A       :







											@staticmethod
											def   __lowerCAmelCase     (    *lowerCAmelCase_			:    Tuple				,     **lowerCAmelCase_			:    Union[str, Any]   )     ->				Union[str, Any]:


																		"""simple docstring"""


																		pass
def 						snake_case_       (UpperCamelCase			:    Image ):





							'''simple docstring'''


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

@is_pipeline_test
@require_vision
@require_timm
@require_torch
class      A       (						unittest.TestCase       ):
							lowercase_							     =							MODEL_FOR_DEPTH_ESTIMATION_MAPPING







							def   __lowerCAmelCase     (    self			:    Any				,     lowerCAmelCase_			:    Optional[int]				,     lowerCAmelCase_			:    Optional[Any]				,     lowerCAmelCase_			:    Optional[Any]   )     ->				Dict:


														"""simple docstring"""


														_a      	=							DepthEstimationPipeline(model=lowerCAmelCase_				,     image_processor=lowerCAmelCase_   )
														return depth_estimator, [
														    "./tests/fixtures/tests_samples/COCO/000000039769.png",
														    "./tests/fixtures/tests_samples/COCO/000000039769.png",
														]







							def   __lowerCAmelCase     (    self			:    Dict				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    int   )     ->				Dict:


														"""simple docstring"""


														_a      	=							depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
														self.assertEqual({'''predicted_depth''': ANY(torch.Tensor   ), '''depth''': ANY(Image.Image   )}				,     lowerCAmelCase_   )
														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   )},
														    ]				,     lowerCAmelCase_				,     )







							@require_tf
							@unittest.skip('''Depth estimation is not implemented in TF'''   )
							def   __lowerCAmelCase     (    self			:    int   )     ->				int:


														"""simple docstring"""


														pass







							@slow
							@require_torch
							def   __lowerCAmelCase     (    self			:    int   )     ->				int:


														"""simple docstring"""


														_a      	=							'''Intel/dpt-large'''
														_a      	=							pipeline('''depth-estimation'''				,     model=lowerCAmelCase_   )
														_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()   )				,     2_9.3_0_4   )
														self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()   )				,     2.6_6_2   )







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


														"""simple docstring"""


														self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''   )







'''simple docstring'''


import requests
def 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    str ):





							'''simple docstring'''


							_a      	=							{'''Content-Type''': '''application/json'''}
							_a      	=							requests.post(UpperCamelCase					,		json={'''text''': message_body}					,		headers=UpperCamelCase )
							if response.status_code != 200:
														_a      	=							(
														    '''Request to slack returned an error '''
														    f'{response.status_code}, the response is:\n{response.text}'
														)
														raise ValueError(UpperCamelCase )


if __name__ == "__main__":
				# Set the slack url to the one provided by Slack when you create the webhook at
				# https://my.slack.com/services/new/incoming-webhook/
				send_slack_message('<YOUR MESSAGE BODY>', '<SLACK CHANNEL URL>')

