




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast







@require_vision
class 						lowercase							(   unittest.TestCase		):

     def 					a__	(  self				)						->							Any:
           _A							:     str			=						tempfile.mkdtemp()

           _A							:     str			=						BlipImageProcessor()
           _A							:     List[Any]			=						GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""				)

           _A							:     Union[str, Any]			=						BlipaProcessor(_a		,     _a				)

           processor.save_pretrained(self.tmpdirname				)

     def 					a__	(  self		,     **_a				)						->							List[Any]:
           return AutoProcessor.from_pretrained(self.tmpdirname		,     **_a				).tokenizer

     def 					a__	(  self		,     **_a				)						->							Optional[int]:
           return AutoProcessor.from_pretrained(self.tmpdirname		,     **_a				).image_processor

     def 					a__	(  self				)						->							Any:
           shutil.rmtree(self.tmpdirname				)

     def 					a__	(  self				)						->							Any:
           _A							:     List[str]			=						[np.random.randint(255		,     size=(3, 30, 400)		,     dtype=np.uinta				)]

           _A							:     List[str]			=						[Image.fromarray(np.moveaxis(_a		,     0		,     -1				)				) for x in image_inputs]

           return image_inputs

     def 					a__	(  self				)						->							List[Any]:
           _A							:     List[Any]			=						BlipaProcessor(tokenizer=self.get_tokenizer()		,     image_processor=self.get_image_processor()				)
           processor.save_pretrained(self.tmpdirname				)

           _A							:     Dict			=						self.get_tokenizer(bos_token="""(BOS)"""		,     eos_token="""(EOS)"""				)
           _A							:     Optional[int]			=						self.get_image_processor(do_normalize=_a		,     padding_value=1.0				)

           _A							:     List[Any]			=						BlipaProcessor.from_pretrained(
               self.tmpdirname		,     bos_token="""(BOS)"""		,     eos_token="""(EOS)"""		,     do_normalize=_a		,     padding_value=1.0				)

           self.assertEqual(processor.tokenizer.get_vocab()		,     tokenizer_add_kwargs.get_vocab()				)
           self.assertIsInstance(processor.tokenizer		,     _a				)

           self.assertEqual(processor.image_processor.to_json_string()		,     image_processor_add_kwargs.to_json_string()				)
           self.assertIsInstance(processor.image_processor		,     _a				)

     def 					a__	(  self				)						->							str:
           _A							:     Any			=						self.get_image_processor()
           _A							:     Optional[Any]			=						self.get_tokenizer()

           _A							:     List[Any]			=						BlipaProcessor(tokenizer=_a		,     image_processor=_a				)

           _A							:     List[str]			=						self.prepare_image_inputs()

           _A							:     Optional[Any]			=						image_processor(_a		,     return_tensors="""np"""				)
           _A							:     Optional[int]			=						processor(images=_a		,     return_tensors="""np"""				)

           for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()		,     input_processor[key].sum()		,     delta=1e-2				)

     def 					a__	(  self				)						->							List[Any]:
           _A							:     Tuple			=						self.get_image_processor()
           _A							:     List[str]			=						self.get_tokenizer()

           _A							:     str			=						BlipaProcessor(tokenizer=_a		,     image_processor=_a				)

           _A							:     Optional[Any]			=						"""lower newer"""

           _A							:     Tuple			=						processor(text=_a				)

           _A							:     Optional[int]			=						tokenizer(_a		,     return_token_type_ids=_a				)

           for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]		,     encoded_processor[key]				)

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     str			=						self.get_image_processor()
           _A							:     List[str]			=						self.get_tokenizer()

           _A							:     Dict			=						BlipaProcessor(tokenizer=_a		,     image_processor=_a				)

           _A							:     int			=						"""lower newer"""
           _A							:     List[str]			=						self.prepare_image_inputs()

           _A							:     Any			=						processor(text=_a		,     images=_a				)

           self.assertListEqual(list(inputs.keys()				)		,     ["""pixel_values""", """input_ids""", """attention_mask"""]				)

           # test if it raises when no input is passed
           with pytest.raises(_a				):
                 processor()

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     List[Any]			=						self.get_image_processor()
           _A							:     Optional[int]			=						self.get_tokenizer()

           _A							:     str			=						BlipaProcessor(tokenizer=_a		,     image_processor=_a				)

           _A							:     Union[str, Any]			=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           _A							:     Tuple			=						processor.batch_decode(_a				)
           _A							:     Optional[Any]			=						tokenizer.batch_decode(_a				)

           self.assertListEqual(_a		,     _a				)



     def 					a__	(  self				)						->							Any:
           _A							:     List[str]			=						self.get_image_processor()
           _A							:     Any			=						self.get_tokenizer()

           _A							:     Optional[Any]			=						BlipaProcessor(tokenizer=_a		,     image_processor=_a				)

           _A							:     Tuple			=						"""lower newer"""
           _A							:     str			=						self.prepare_image_inputs()

           _A							:     Any			=						processor(text=_a		,     images=_a				)

           # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
           self.assertListEqual(list(inputs.keys()				)		,     ["""pixel_values""", """input_ids""", """attention_mask"""]				)



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')




