





import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   ):
	if isinstance(__a   , __a   ):
		snake_case_		:			str											=	np.full((len(__a   ), sequence_length, 2)   , __a   )
	else:
		snake_case_		:			List[str]											=	np.full((len(__a   ), sequence_length)   , __a   )

	for i, tensor in enumerate(__a   ):
		if padding_side == "right":
			if isinstance(__a   , __a   ):
				snake_case_		:			Any											=	tensor[:sequence_length]
			else:
				snake_case_		:			int											=	tensor[:sequence_length]
		else:
			if isinstance(__a   , __a   ):
				snake_case_		:			Any											=	tensor[:sequence_length]
			else:
				snake_case_		:			int											=	tensor[:sequence_length]

	return out_tensor.tolist()


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	snake_case_		:			Tuple											=	ord(__a   )
	if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
		return True
	snake_case_		:			Dict											=	unicodedata.category(__a   )
	if cat.startswith('P'   ):
		return True
	return False


@dataclass
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			PreTrainedTokenizerBase
						__magic_name__:			Union[bool, str, PaddingStrategy]       =       True
						__magic_name__:			Optional[int]       =       None
						__magic_name__:			Optional[int]       =       None
						__magic_name__:			int       =       -100
						__magic_name__:			str       =       "pt"






						def UpperCAmelCase_				(      self      :      str		,   _A      :      List[str]	)							->    List[Any]:







							"""simple docstring"""


							import torch

							snake_case_		:			Dict											=	'label' if 'label' in features[0].keys() else 'labels'
							snake_case_		:			List[Any]											=	[feature[label_name] for feature in features] if label_name in features[0].keys() else None
							snake_case_		:			Optional[int]											=	self.tokenizer.pad(
							    _A		,   padding=self.padding		,   max_length=self.max_length		,   pad_to_multiple_of=self.pad_to_multiple_of		,   return_tensors='pt' if labels is None else None		,   )

							if labels is None:
								return batch

							snake_case_		:			Optional[Any]											=	torch.tensor(batch['entity_ids']	).shape[1]
							snake_case_		:			List[str]											=	self.tokenizer.padding_side
							if padding_side == "right":
								snake_case_		:			Union[str, Any]											=	[
								    list(_A	) + [self.label_pad_token_id] * (sequence_length - len(_A	)) for label in labels
								]
							else:
								snake_case_		:			Tuple											=	[
								    [self.label_pad_token_id] * (sequence_length - len(_A	)) + list(_A	) for label in labels
								]

							snake_case_		:			Any											=	[feature['ner_tags'] for feature in features]
							snake_case_		:			Optional[Any]											=	padding_tensor(_A		,   -1		,   _A		,   _A	)
							snake_case_		:			Union[str, Any]											=	[feature['original_entity_spans'] for feature in features]
							snake_case_		:			Optional[int]											=	padding_tensor(_A		,   (-1, -1)		,   _A		,   _A	)
							snake_case_		:			Optional[Any]											=	{k: torch.tensor(_A		,   dtype=torch.intaa	) for k, v in batch.items()}

							return batch








import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		"""%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))

						print("""Googling.....""")

						_SCREAMING_SNAKE_CASE      	=		F'''https://www.google.com/search?q={query}&num=100'''

						_SCREAMING_SNAKE_CASE      	=		requests.get(
						    url,
						    headers={"""User-Agent""": str(UserAgent().random)},
						)

						try:
												_SCREAMING_SNAKE_CASE      	=		(
												    BeautifulSoup(res.text, """html.parser""")
												    .find("""div""", attrs={"""class""": """yuRUbf"""})
												    .find("""a""")
												    .get("""href""")
												)

						except AttributeError:
												_SCREAMING_SNAKE_CASE      	=		parse_qs(
												    BeautifulSoup(res.text, """html.parser""")
												    .find("""div""", attrs={"""class""": """kCrYT"""})
												    .find("""a""")
												    .get("""href""")
												)["""url"""][0]

						webbrowser.open(link)

