
'''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     __snake_case		:
										@staticmethod
										def     UpperCAmelCase_							(     *A,    **A      ):

														"""simple docstring"""


														pass





def    UpperCAmelCase							(  UpperCAmelCase__							:							Image):
				lowerCamelCase					:			Optional[Any]									= hashlib.mda(image.tobytes())
				return m.hexdigest()




@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     __snake_case		(   unittest.TestCase):
					_lowerCAmelCase			     =	MODEL_FOR_DEPTH_ESTIMATION_MAPPING
					def     UpperCAmelCase_							(     self,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= DepthEstimationPipeline(model=A_,    image_processor=A_      )
									return depth_estimator, [
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]
					def     UpperCAmelCase_							(     self,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
									self.assertEqual({'predicted_depth': ANY(torch.Tensor      ), 'depth': ANY(Image.Image      )},    A_      )
									import datasets

									lowerCamelCase					:			int									= datasets.load_dataset('hf-internal-testing/fixtures_image_utils',    'image',    split='test'      )
									lowerCamelCase					:			Union[str, Any]									= 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     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					@slow
					@require_torch
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= 'Intel/dpt-large'
									lowerCamelCase					:			Union[str, Any]									= pipeline('depth-estimation',    model=A_      )
									lowerCamelCase					:			int									= 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      ):

									"""simple docstring"""


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


'''simple docstring'''






import requests
from bsa import BeautifulSoup





def    UpperCAmelCase							(  UpperCAmelCase__							:							str = "AAPL"):
				lowerCamelCase					:			List[Any]									= F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
				lowerCamelCase					:			Dict									= BeautifulSoup(requests.get(UpperCAmelCase__).text       ,							'html.parser')
				lowerCamelCase					:			Dict									= 'My(6px) Pos(r) smartphone_Mt(6px)'
				return soup.find('div'       ,							class_=class_).find('span').text


if __name__ == "__main__":
					for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
										print(f"""Current {symbol:<4} stock price is {stock_price(symbol):>8}""")
