
'''simple docstring'''






import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
 from PIL import Image
else:

 class 			UpperCAmelCase__    :



     @staticmethod
     def 	lowerCamelCase_	(						*__A   :							Optional[Any],**__A   :							int  ):
           pass







def 		A_							(						_lowerCAmelCase :     Image				):



      """simple docstring"""




      _lowerCamelCase					: Optional[Any]		    = hashlib.mda(image.tobytes()				)
      return m.hexdigest()[:10]







def 		A_							(						_lowerCAmelCase :     Image				):



      """simple docstring"""




      _lowerCamelCase					: Union[str, Any]		    = np.array(_lowerCAmelCase				)
      _lowerCamelCase					: str		    = npimg.shape
      return {"hash": hashimage(_lowerCAmelCase				), "shape": shape}



@is_pipeline_test
@require_vision
@require_torch
class 			UpperCAmelCase__    (							unittest.TestCase			):
    lowerCAmelCase_					     =		dict(
        (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
    lowerCAmelCase_					     =		dict(
        (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])			)



    def 	lowerCamelCase_	(						self   :							int,__A   :							Any,__A   :							Optional[int],__A   :							Optional[int]  ):
          _lowerCamelCase					: Tuple		    = MaskGenerationPipeline(model=__A,image_processor=__A  )
          return image_segmenter, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]



    def 	lowerCamelCase_	(						self   :							Tuple,__A   :							List[Any],__A   :							Union[str, Any]  ):
          pass



    @require_tf
    @unittest.skip("Image segmentation not implemented in TF"  )
    def 	lowerCamelCase_	(						self   :							str  ):
          pass



    @slow
    @require_torch
    def 	lowerCamelCase_	(						self   :							int  ):
          _lowerCamelCase					: int		    = pipeline("mask-generation",model="facebook/sam-vit-huge"  )

          _lowerCamelCase					: Tuple		    = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg",points_per_batch=2_5_6  )

          # Shortening by hashing
          _lowerCamelCase					: Optional[Any]		    = []
          for i, o in enumerate(outputs["masks"]  ):
                new_outupt += [{"mask": mask_to_test_readable(__A  ), "scores": outputs["scores"][i]}]

          # fmt: off
          self.assertEqual(
              nested_simplify(__A,decimals=4  ),[
                  {"mask": {"hash": "115ad19f5f", "shape": (4_8_0, 6_4_0)}, "scores": 1.0444},
                  {"mask": {"hash": "6affa964c6", "shape": (4_8_0, 6_4_0)}, "scores": 1.021},
                  {"mask": {"hash": "dfe28a0388", "shape": (4_8_0, 6_4_0)}, "scores": 1.0167},
                  {"mask": {"hash": "c0a5f4a318", "shape": (4_8_0, 6_4_0)}, "scores": 1.0132},
                  {"mask": {"hash": "fe8065c197", "shape": (4_8_0, 6_4_0)}, "scores": 1.0053},
                  {"mask": {"hash": "e2d0b7a0b7", "shape": (4_8_0, 6_4_0)}, "scores": 0.9967},
                  {"mask": {"hash": "453c7844bd", "shape": (4_8_0, 6_4_0)}, "scores": 0.993},
                  {"mask": {"hash": "3d44f2926d", "shape": (4_8_0, 6_4_0)}, "scores": 0.9909},
                  {"mask": {"hash": "64033ddc3f", "shape": (4_8_0, 6_4_0)}, "scores": 0.9879},
                  {"mask": {"hash": "801064ff79", "shape": (4_8_0, 6_4_0)}, "scores": 0.9834},
                  {"mask": {"hash": "6172f276ef", "shape": (4_8_0, 6_4_0)}, "scores": 0.9716},
                  {"mask": {"hash": "b49e60e084", "shape": (4_8_0, 6_4_0)}, "scores": 0.9612},
                  {"mask": {"hash": "a811e775fd", "shape": (4_8_0, 6_4_0)}, "scores": 0.9599},
                  {"mask": {"hash": "a6a8ebcf4b", "shape": (4_8_0, 6_4_0)}, "scores": 0.9552},
                  {"mask": {"hash": "9d8257e080", "shape": (4_8_0, 6_4_0)}, "scores": 0.9532},
                  {"mask": {"hash": "32de6454a8", "shape": (4_8_0, 6_4_0)}, "scores": 0.9516},
                  {"mask": {"hash": "af3d4af2c8", "shape": (4_8_0, 6_4_0)}, "scores": 0.9499},
                  {"mask": {"hash": "3c6db475fb", "shape": (4_8_0, 6_4_0)}, "scores": 0.9483},
                  {"mask": {"hash": "c290813fb9", "shape": (4_8_0, 6_4_0)}, "scores": 0.9464},
                  {"mask": {"hash": "b6f0b8f606", "shape": (4_8_0, 6_4_0)}, "scores": 0.943},
                  {"mask": {"hash": "92ce16bfdf", "shape": (4_8_0, 6_4_0)}, "scores": 0.943},
                  {"mask": {"hash": "c749b25868", "shape": (4_8_0, 6_4_0)}, "scores": 0.9408},
                  {"mask": {"hash": "efb6cab859", "shape": (4_8_0, 6_4_0)}, "scores": 0.9335},
                  {"mask": {"hash": "1ff2eafb30", "shape": (4_8_0, 6_4_0)}, "scores": 0.9326},
                  {"mask": {"hash": "788b798e24", "shape": (4_8_0, 6_4_0)}, "scores": 0.9262},
                  {"mask": {"hash": "abea804f0e", "shape": (4_8_0, 6_4_0)}, "scores": 0.8999},
                  {"mask": {"hash": "7b9e8ddb73", "shape": (4_8_0, 6_4_0)}, "scores": 0.8986},
                  {"mask": {"hash": "cd24047c8a", "shape": (4_8_0, 6_4_0)}, "scores": 0.8984},
                  {"mask": {"hash": "6943e6bcbd", "shape": (4_8_0, 6_4_0)}, "scores": 0.8873},
                  {"mask": {"hash": "b5f47c9191", "shape": (4_8_0, 6_4_0)}, "scores": 0.8871}
              ],)
          # fmt: on



    @require_torch
    @slow
    def 	lowerCamelCase_	(						self   :							Optional[Any]  ):
          _lowerCamelCase					: Union[str, Any]		    = "facebook/sam-vit-huge"
          _lowerCamelCase					: Tuple		    = pipeline("mask-generation",model=__A  )

          _lowerCamelCase					: Union[str, Any]		    = image_segmenter(
              "http://images.cocodataset.org/val2017/000000039769.jpg",pred_iou_thresh=1,points_per_batch=2_5_6  )

          # Shortening by hashing
          _lowerCamelCase					: List[Any]		    = []
          for i, o in enumerate(outputs["masks"]  ):
                new_outupt += [{"mask": mask_to_test_readable(__A  ), "scores": outputs["scores"][i]}]

          self.assertEqual(
              nested_simplify(__A,decimals=4  ),[
                  {"mask": {"hash": "115ad19f5f", "shape": (4_8_0, 6_4_0)}, "scores": 1.0444},
                  {"mask": {"hash": "6affa964c6", "shape": (4_8_0, 6_4_0)}, "scores": 1.0210},
                  {"mask": {"hash": "dfe28a0388", "shape": (4_8_0, 6_4_0)}, "scores": 1.0167},
                  {"mask": {"hash": "c0a5f4a318", "shape": (4_8_0, 6_4_0)}, "scores": 1.0132},
                  {"mask": {"hash": "fe8065c197", "shape": (4_8_0, 6_4_0)}, "scores": 1.0053},
              ],)



'''simple docstring'''



from collections import namedtuple

import requests
from lxml import html  # type: ignore

A__						:   Tuple				=    namedtuple("""covid_data""", """cases deaths recovered""")



def      UpperCAmelCase__   (      UpperCAmelCase_       :							str = "https://www.worldometers.info/coronavirus/"				)	->						covid_data:
     __lowerCamelCase  :      Union[str, Any]														=						'//div[@class = "maincounter-number"]/span/text()'
     return covid_data(*html.fromstring(requests.get(UpperCAmelCase_				).content				).xpath(UpperCAmelCase_				)				)


A__						:   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()))






