




import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin




class 			UpperCAmelCase_      (   A		,      unittest.TestCase       ):




							'''simple docstring'''



							a__						  =     PhobertTokenizer
							a__						  =     False





							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					str:
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										SCREAMING_SNAKE_CASE           =					["""T@@""", """i""", """I""", """R@@""", """r""", """e@@"""]
										SCREAMING_SNAKE_CASE           =					dict(zip(a       ,     range(len(a			)			)			)			)
										SCREAMING_SNAKE_CASE           =					["""#version: 0.2""", """l à</w>"""]
										SCREAMING_SNAKE_CASE           =					{"""unk_token""": """<unk>"""}

										SCREAMING_SNAKE_CASE           =					os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES["""vocab_file"""]			)
										SCREAMING_SNAKE_CASE           =					os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES["""merges_file"""]			)

										with open(self.vocab_file       ,     """w"""       ,     encoding="""utf-8"""			) as fp:
													for token in vocab_tokens:
																fp.write(f"""{token} {vocab_tokens[token]}\n"""			)
										with open(self.merges_file       ,     """w"""       ,     encoding="""utf-8"""			) as fp:
													fp.write("""\n""".join(a			)			)





							def    _UpperCAmelCase     (					self       :	Any       ,     **a       :	Optional[Any]			)			->					Any:
										kwargs.update(self.special_tokens_map			)
										return PhobertTokenizer.from_pretrained(self.tmpdirname       ,     **a			)





							def    _UpperCAmelCase     (					self       :	List[str]       ,     a       :	Any			)			->					int:
										SCREAMING_SNAKE_CASE           =					"""Tôi là VinAI Research"""
										SCREAMING_SNAKE_CASE           =					"""T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
										return input_text, output_text







							def    _UpperCAmelCase     (					self       :	Any			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					PhobertTokenizer(self.vocab_file       ,     self.merges_file       ,     **self.special_tokens_map			)
										SCREAMING_SNAKE_CASE           =					"""Tôi là VinAI Research"""
										SCREAMING_SNAKE_CASE           =					"""T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
										SCREAMING_SNAKE_CASE           =					tokenizer.tokenize(a			)
										print(a			)
										self.assertListEqual(a       ,     a			)

										SCREAMING_SNAKE_CASE           =					tokens + [tokenizer.unk_token]

										SCREAMING_SNAKE_CASE           =					[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(a			)       ,     a			)












import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

__A						:							int							=  {
    """User-Agent""": """Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
    """ (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"""
}
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE = "dhaka"					,	SCREAMING_SNAKE_CASE = 5   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					min(SCREAMING_SNAKE_CASE					,	50   )  # Prevent abuse!
			SCREAMING_SNAKE_CASE           =					{
			    """q""": query,
			    """tbm""": """isch""",
			    """hl""": """en""",
			    """ijn""": """0""",
			}

			SCREAMING_SNAKE_CASE           =					requests.get("""https://www.google.com/search"""					,	params=SCREAMING_SNAKE_CASE					,	headers=SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					BeautifulSoup(html.text					,	"""html.parser"""   )
			SCREAMING_SNAKE_CASE           =					"""""".join(
			    re.findall(r"""AF_initDataCallback\(([^<]+)\);"""					,	str(soup.select("""script"""   )   )   )   )

			SCREAMING_SNAKE_CASE           =					json.dumps(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					json.loads(SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					re.findall(
			    r"""\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","""					,	SCREAMING_SNAKE_CASE					,	)
			if not matched_google_image_data:
						return 0

			SCREAMING_SNAKE_CASE           =					re.sub(
			    r"""\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]"""					,	""""""					,	str(SCREAMING_SNAKE_CASE   )					,	)

			SCREAMING_SNAKE_CASE           =					re.findall(
			    r"""(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]"""					,	SCREAMING_SNAKE_CASE					,	)
			for index, fixed_full_res_image in enumerate(SCREAMING_SNAKE_CASE   ):
						if index >= max_images:
									return index
						SCREAMING_SNAKE_CASE           =					bytes(SCREAMING_SNAKE_CASE					,	"""ascii"""   ).decode(
						    """unicode-escape"""   )
						SCREAMING_SNAKE_CASE           =					bytes(SCREAMING_SNAKE_CASE					,	"""ascii"""   ).decode(
						    """unicode-escape"""   )
						SCREAMING_SNAKE_CASE           =					urllib.request.build_opener()
						SCREAMING_SNAKE_CASE           =					[
						    (
						        """User-Agent""",
						        """Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
						        """ (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582""",
						    )
						]
						urllib.request.install_opener(SCREAMING_SNAKE_CASE   )
						SCREAMING_SNAKE_CASE           =					f"""query_{query.replace(" "					,	"_"   )}"""
						if not os.path.exists(SCREAMING_SNAKE_CASE   ):
									os.makedirs(SCREAMING_SNAKE_CASE   )
						urllib.request.urlretrieve(  # noqa: S310
						    SCREAMING_SNAKE_CASE					,	f"""{path_name}/original_size_img_{index}.jpg"""   )
			return index


if __name__ == "__main__":
		try:
				__A						:							List[Any]							=  download_images_from_google_query(sys.argv[1])
				print(f'''{image_count} images were downloaded to disk.''')
		except IndexError:
				print("""Please provide a search term.""")
				raise






