






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  _lowercase   :
             @staticmethod
             def 						lowercase__					(     *a				,			**a   ):
              pass







def 			A__							(      _a  :					Image	):





 '''simple docstring'''







 snake_case__  :    List[str]							       =hashlib.mda(image.tobytes()	)
 return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class  _lowercase   (   unittest.TestCase						):
      _a							:						Optional[int]										=							MODEL_FOR_DEPTH_ESTIMATION_MAPPING
      def 						lowercase__					(     self				,			a				,			a				,			a   ):
       snake_case__  :    Optional[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 						lowercase__					(     self				,			a				,			a   ):
       snake_case__  :    Any							       =depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
       self.assertEqual({"""predicted_depth""": ANY(torch.Tensor   ), """depth""": ANY(Image.Image   )}				,			a   )
       import datasets

       snake_case__  :    Tuple							       =datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""				,			"""image"""				,			split="""test"""   )
       snake_case__  :    Dict							       =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 						lowercase__					(     self   ):
       pass
      @slow
      @require_torch
      def 						lowercase__					(     self   ):
       snake_case__  :    int							       ="""Intel/dpt-large"""
       snake_case__  :    int							       =pipeline("""depth-estimation"""				,			model=a   )
       snake_case__  :    List[str]							       =depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""   )
       snake_case__  :    Any							       =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 						lowercase__					(     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"""   )













from collections import namedtuple

import requests
from lxml import html  # type: ignore

__lowerCamelCase	:		Any										=   namedtuple("""covid_data""", """cases deaths recovered""")







def 			A__							(      _a  :					str = "https://www.worldometers.info/coronavirus/"	):





 '''simple docstring'''







 snake_case__  :    Any							       ="""//div[@class = \"maincounter-number\"]/span/text()"""
 return covid_data(*html.fromstring(requests.get(_a	).content	).xpath(_a	)	)


__lowerCamelCase	:		str										=   """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""

print(fmt.format(*covid_stats()))





