


'''simple docstring'''



import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_ta import TaTokenizer
else:
	__lowercase      :		Tuple			=      None


__lowercase      :		Optional[Any]			=      logging.get_logger(__name__)

__lowercase      :		Any			=      {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__lowercase      :		Any			=      {
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__lowercase      :		Dict			=      {
    "t5-small": 5_12,
    "t5-base": 5_12,
    "t5-large": 5_12,
    "t5-3b": 5_12,
    "t5-11b": 5_12,
}





class 				__UpperCamelCase							(						__snake_case   ):
	A_           =       VOCAB_FILES_NAMES
	A_           =       PRETRAINED_VOCAB_FILES_MAP
	A_           =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A_           =       ["input_ids", "attention_mask"]
	A_           =       TaTokenizer

	A_           =       []




	def __init__(  self		,							__a=None		,							__a=None		,							__a="</s>"		,							__a="<unk>"		,							__a="<pad>"		,							__a=100		,							__a=None		,							**__a		,							):

						'''simple docstring'''





						if extra_ids > 0 and additional_special_tokens is None:
											__a     :			Optional[int]          =    [f"""<extra_id_{i}>""" for i in range(a__				)]
						elif extra_ids > 0 and additional_special_tokens is not None:
											# Check that we have the right number of extra special tokens
											__a     :			Any          =    len(set(filter(lambda __a				: bool('extra_id_' in str(a__				)				)		,							a__				)				)				)
											if extra_tokens != extra_ids:
																raise ValueError(
																    f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
																    ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
																    ' tokens'				)

						super().__init__(
						    a__		,							tokenizer_file=a__		,							eos_token=a__		,							unk_token=a__		,							pad_token=a__		,							extra_ids=a__		,							additional_special_tokens=a__		,							**a__		,							)

						__a     :			Optional[Any]          =    vocab_file
						__a     :			Union[str, Any]          =    False if not self.vocab_file else True
						__a     :			str          =    extra_ids




	@staticmethod
	def  __UpperCAmelCase				(  __a		,							__a		,							__a				):

						'''simple docstring'''





						if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
											__a     :			str          =    TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
											if init_max_model_length is not None and init_max_model_length != max_model_length:
																return init_max_model_length
											elif init_max_model_length is None:
																warnings.warn(
																    'This tokenizer was incorrectly instantiated with a model max length of'
																    f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
																    ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
																    ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
																    f""" {pretrained_model_name_or_path} automatically truncating your input to"""
																    f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
																    f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
																    ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
																    ' instantiate this tokenizer with `model_max_length` set to your preferred value.'		,							a__		,							)

						return max_model_length




	def  __UpperCAmelCase				(  self		,							__a		,							__a = None				):

						'''simple docstring'''





						if not self.can_save_slow_tokenizer:
											raise ValueError(
											    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
											    'tokenizer.'				)

						if not os.path.isdir(a__				):
											logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
											return
						__a     :			Union[str, Any]          =    os.path.join(
						    a__		,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

						if os.path.abspath(self.vocab_file				) != os.path.abspath(a__				):
											copyfile(self.vocab_file		,							a__				)
											logger.info(f"""Copy vocab file to {out_vocab_file}"""				)

						return (out_vocab_file,)




	def  __UpperCAmelCase				(  self		,							__a		,							__a = None				):

						'''simple docstring'''





						__a     :			Tuple          =    token_ids_a + [self.eos_token_id]
						if token_ids_a is None:
											return self.prefix_tokens + token_ids_a
						else:
											__a     :			List[str]          =    token_ids_a + [self.eos_token_id]
											return self.prefix_tokens + token_ids_a + token_ids_a




	def  __UpperCAmelCase				(  self		,							__a		,							__a = None				):

						'''simple docstring'''





						__a     :			Union[str, Any]          =    [self.eos_token_id]

						if token_ids_a is None:
											return len(token_ids_a + eos				) * [0]
						return len(token_ids_a + eos + token_ids_a + eos				) * [0]




	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						return list(
						    set(filter(lambda __a				: bool(re.search(r'<extra_id_\d+>'		,							a__				)				) is not None		,							self.additional_special_tokens				)				)				)





	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						return [self.convert_tokens_to_ids(a__				) for token in self.get_sentinel_tokens()]







import requests
from bsa import BeautifulSoup





def 	_lowerCAmelCase					( UpperCamelCase__:  str = "https://www.worldometers.info/coronavirus"    )			-> dict:





	"""simple docstring"""







	A             =					BeautifulSoup(requests.get(UpperCamelCase__    ).text				,       """html.parser"""    )
	A             =					soup.findAll("""h1"""    )
	A             =					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(UpperCamelCase__				,       UpperCamelCase__    )}


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''')



