
from io import BytesIO
from typing import List, Union

import requests

from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_decord_available():
							import numpy as np
							from decord import VideoReader


if is_torch_available():
							from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

lowercase__		:Tuple   =    logging.get_logger(__name__)







@add_end_docstrings(SCREAMING_SNAKE_CASE__		)
class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							def __init__(				self   ,*A__   ,**A__):
												super().__init__(*A__   ,**A__)
												requires_backends(self   ,'''decord''')
												self.check_model_type(A__)




							def  A__      (				self   ,A__=None   ,A__=None   ,A__=None):
												lowercase							=						{}
												if frame_sampling_rate is not None:
																	lowercase							=						frame_sampling_rate
												if num_frames is not None:
																	lowercase							=						num_frames

												lowercase							=						{}
												if top_k is not None:
																	lowercase							=						top_k
												return preprocess_params, {}, postprocess_params




							def __call__(				self   ,A__   ,**A__):
												return super().__call__(A__   ,**A__)




							def  A__      (				self   ,A__   ,A__=None   ,A__=1):
												if num_frames is None:
																	lowercase							=						self.model.config.num_frames

												if video.startswith('''http://''') or video.startswith('''https://'''):
																	lowercase							=						BytesIO(requests.get(A__).content)

												lowercase							=						VideoReader(A__)
												videoreader.seek(0)

												lowercase							=						0
												lowercase							=						num_frames * frame_sampling_rate - 1
												lowercase							=						np.linspace(A__   ,A__   ,num=A__   ,dtype=np.intaa)

												lowercase							=						videoreader.get_batch(A__).asnumpy()
												lowercase							=						list(A__)

												lowercase							=						self.image_processor(A__   ,return_tensors=self.framework)
												return model_inputs




							def  A__      (				self   ,A__):
												lowercase							=						self.model(**A__)
												return model_outputs





							def  A__      (				self   ,A__   ,A__=5):
												if top_k > self.model.config.num_labels:
																	lowercase							=						self.model.config.num_labels

												if self.framework == "pt":
																	lowercase							=						model_outputs.logits.softmax(-1)[0]
																	lowercase	,						lowercase							=						probs.topk(A__)
												else:
																	raise ValueError(f'Unsupported framework: {self.framework}')

												lowercase							=						scores.tolist()
												lowercase							=						ids.tolist()
												return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(A__   ,A__)]






import requests
from bsa import BeautifulSoup



def        UpperCamelCase    (							lowerCAmelCase__ = "https://www.worldometers.info/coronavirus"		):
					'''simple docstring'''
					lowercase							=						BeautifulSoup(requests.get(lowerCAmelCase__		).text ,			'''html.parser'''		)
					lowercase							=						soup.findAll('''h1'''		)
					lowercase							=						soup.findAll('''div''' ,			{'''class''': '''maincounter-number'''}		)
					keys += soup.findAll('''span''' ,			{'''class''': '''panel-title'''}		)
					values += soup.findAll('''div''' ,			{'''class''': '''number-table-main'''}		)
					return {key.text.strip(): value.text.strip() for key, value in zip(lowerCAmelCase__ ,			lowerCAmelCase__		)}


if __name__ == "__main__":
							print("\033[1m" + "COVID-19 Status of the World" + "\033[0m\n")
							for key, value in world_covidaa_stats().items():
														print(F'{key}\n{value}\n')




