






"""simple docstring"""







import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase            =	get_tests_dir('fixtures/test_sentencepiece.model')


@require_sentencepiece
@require_tokenizers
class      UpperCamelCase		(__snake_case						,						unittest.TestCase     ):
						_SCREAMING_SNAKE_CASE  : str												=		XLMRobertaTokenizer
						_SCREAMING_SNAKE_CASE  : Dict												=		XLMRobertaTokenizerFast
						_SCREAMING_SNAKE_CASE  : List[Any]												=		True
						_SCREAMING_SNAKE_CASE  : Tuple												=		True






						def      __snake_case      (		self  :Tuple				)      ->Any:
										super().setUp()

										# We have a SentencePiece fixture for testing
										lowercase    :      Dict       = XLMRobertaTokenizer(__magic_name__			, keep_accents=__magic_name__				)
										tokenizer.save_pretrained(self.tmpdirname				)






						def      __snake_case      (		self  :Optional[Any]				)      ->Optional[Any]:
										lowercase    :      int       = """<pad>"""
										lowercase    :      Union[str, Any]       = 1

										self.assertEqual(self.get_tokenizer()._convert_token_to_id(__magic_name__				)			, __magic_name__				)
										self.assertEqual(self.get_tokenizer()._convert_id_to_token(__magic_name__				)			, __magic_name__				)






						def      __snake_case      (		self  :int				)      ->Optional[int]:
										lowercase    :      Optional[Any]       = list(self.get_tokenizer().get_vocab().keys()				)

										self.assertEqual(vocab_keys[0]			, """<s>"""				)
										self.assertEqual(vocab_keys[1]			, """<pad>"""				)
										self.assertEqual(vocab_keys[-1]			, """<mask>"""				)
										self.assertEqual(len(__magic_name__				)			, 1_002				)






						def      __snake_case      (		self  :Tuple				)      ->Any:
										self.assertEqual(self.get_tokenizer().vocab_size			, 1_002				)






						def      __snake_case      (		self  :str				)      ->List[str]:
										lowercase    :      str       = XLMRobertaTokenizer(__magic_name__			, keep_accents=__magic_name__				)

										lowercase    :      Dict       = tokenizer.tokenize("""This is a test"""				)
										self.assertListEqual(__magic_name__			, ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]				)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(__magic_name__				)			, [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]			, )

										lowercase    :      Any       = tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
										self.assertListEqual(
										    __magic_name__			, [
										        SPIECE_UNDERLINE + """I""",
										        SPIECE_UNDERLINE + """was""",
										        SPIECE_UNDERLINE + """b""",
										        """or""",
										        """n""",
										        SPIECE_UNDERLINE + """in""",
										        SPIECE_UNDERLINE + """""",
										        """9""",
										        """2""",
										        """0""",
										        """0""",
										        """0""",
										        """,""",
										        SPIECE_UNDERLINE + """and""",
										        SPIECE_UNDERLINE + """this""",
										        SPIECE_UNDERLINE + """is""",
										        SPIECE_UNDERLINE + """f""",
										        """al""",
										        """s""",
										        """é""",
										        """.""",
										    ]			, )
										lowercase    :      Dict       = tokenizer.convert_tokens_to_ids(__magic_name__				)
										self.assertListEqual(
										    __magic_name__			, [
										        value + tokenizer.fairseq_offset
										        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
										        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
										    ]			, )

										lowercase    :      Tuple       = tokenizer.convert_ids_to_tokens(__magic_name__				)
										self.assertListEqual(
										    __magic_name__			, [
										        SPIECE_UNDERLINE + """I""",
										        SPIECE_UNDERLINE + """was""",
										        SPIECE_UNDERLINE + """b""",
										        """or""",
										        """n""",
										        SPIECE_UNDERLINE + """in""",
										        SPIECE_UNDERLINE + """""",
										        """<unk>""",
										        """2""",
										        """0""",
										        """0""",
										        """0""",
										        """,""",
										        SPIECE_UNDERLINE + """and""",
										        SPIECE_UNDERLINE + """this""",
										        SPIECE_UNDERLINE + """is""",
										        SPIECE_UNDERLINE + """f""",
										        """al""",
										        """s""",
										        """<unk>""",
										        """.""",
										    ]			, )






						def      __snake_case      (		self  :Union[str, Any]				)      ->List[str]:
										if not self.test_slow_tokenizer:
														# as we don't have a slow version, we can't compare the outputs between slow and fast versions
														return

										lowercase    :      Optional[Any]       = (self.rust_tokenizer_class, """hf-internal-testing/tiny-xlm-roberta""", {})
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
																		lowercase    :      Dict       = self.rust_tokenizer_class.from_pretrained(__magic_name__			, **__magic_name__				)
																		lowercase    :      List[str]       = self.tokenizer_class.from_pretrained(__magic_name__			, **__magic_name__				)

																		lowercase    :      List[str]       = tempfile.mkdtemp()

																		lowercase    :      Dict       = tokenizer_r.save_pretrained(__magic_name__				)
																		lowercase    :      Optional[int]       = tokenizer_p.save_pretrained(__magic_name__				)

																		# Checks it save with the same files + the tokenizer.json file for the fast one
																		self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files				)				)
																		lowercase    :      Union[str, Any]       = tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f				)
																		self.assertSequenceEqual(__magic_name__			, __magic_name__				)

																		# Checks everything loads correctly in the same way
																		lowercase    :      str       = tokenizer_r.from_pretrained(__magic_name__				)
																		lowercase    :      Dict       = tokenizer_p.from_pretrained(__magic_name__				)

																		# Check special tokens are set accordingly on Rust and Python
																		for key in tokenizer_pp.special_tokens_map:
																						self.assertTrue(hasattr(__magic_name__			, __magic_name__				)				)
																						# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																						# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																		shutil.rmtree(__magic_name__				)

																		# Save tokenizer rust, legacy_format=True
																		lowercase    :      str       = tempfile.mkdtemp()

																		lowercase    :      Dict       = tokenizer_r.save_pretrained(__magic_name__			, legacy_format=__magic_name__				)
																		lowercase    :      Tuple       = tokenizer_p.save_pretrained(__magic_name__				)

																		# Checks it save with the same files
																		self.assertSequenceEqual(__magic_name__			, __magic_name__				)

																		# Checks everything loads correctly in the same way
																		lowercase    :      Optional[Any]       = tokenizer_r.from_pretrained(__magic_name__				)
																		lowercase    :      Optional[Any]       = tokenizer_p.from_pretrained(__magic_name__				)

																		# Check special tokens are set accordingly on Rust and Python
																		for key in tokenizer_pp.special_tokens_map:
																						self.assertTrue(hasattr(__magic_name__			, __magic_name__				)				)

																		shutil.rmtree(__magic_name__				)

																		# Save tokenizer rust, legacy_format=False
																		lowercase    :      str       = tempfile.mkdtemp()

																		lowercase    :      Dict       = tokenizer_r.save_pretrained(__magic_name__			, legacy_format=__magic_name__				)
																		lowercase    :      Tuple       = tokenizer_p.save_pretrained(__magic_name__				)

																		# Checks it saved the tokenizer.json file
																		self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files				)				)

																		# Checks everything loads correctly in the same way
																		lowercase    :      List[Any]       = tokenizer_r.from_pretrained(__magic_name__				)
																		lowercase    :      Optional[int]       = tokenizer_p.from_pretrained(__magic_name__				)

																		# Check special tokens are set accordingly on Rust and Python
																		for key in tokenizer_pp.special_tokens_map:
																						self.assertTrue(hasattr(__magic_name__			, __magic_name__				)				)

																		shutil.rmtree(__magic_name__				)






						@cached_property
						def      __snake_case      (		self  :Any				)      ->int:
										return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base"""				)






						def      __snake_case      (		self  :Tuple				)      ->Any:
										with tempfile.NamedTemporaryFile() as f:
														shutil.copyfile(__magic_name__			, f.name				)
														lowercase    :      Optional[Any]       = XLMRobertaTokenizer(f.name			, keep_accents=__magic_name__				)
														lowercase    :      Optional[Any]       = pickle.dumps(__magic_name__				)
										pickle.loads(__magic_name__				)






						def      __snake_case      (		self  :int				)      ->int:
										if not self.test_rust_tokenizer:
														return

										lowercase    :      str       = self.get_tokenizer()
										lowercase    :      Optional[Any]       = self.get_rust_tokenizer()

										lowercase    :      Any       = """I was born in 92000, and this is falsé."""

										lowercase    :      Optional[int]       = tokenizer.tokenize(__magic_name__				)
										lowercase    :      str       = rust_tokenizer.tokenize(__magic_name__				)
										self.assertListEqual(__magic_name__			, __magic_name__				)

										lowercase    :      Any       = tokenizer.encode(__magic_name__			, add_special_tokens=__magic_name__				)
										lowercase    :      List[str]       = rust_tokenizer.encode(__magic_name__			, add_special_tokens=__magic_name__				)
										self.assertListEqual(__magic_name__			, __magic_name__				)

										lowercase    :      List[str]       = self.get_rust_tokenizer()
										lowercase    :      Union[str, Any]       = tokenizer.encode(__magic_name__				)
										lowercase    :      List[str]       = rust_tokenizer.encode(__magic_name__				)
										self.assertListEqual(__magic_name__			, __magic_name__				)






						@slow
						def      __snake_case      (		self  :List[str]				)      ->Dict:
										lowercase    :      Dict       = """Hello World!"""
										lowercase    :      List[Any]       = [0, 35_378, 6_661, 38, 2]
										# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
										# xlmr.eval()
										# xlmr.encode(symbols)

										self.assertListEqual(__magic_name__			, self.big_tokenizer.encode(__magic_name__				)				)






						@slow
						def      __snake_case      (		self  :Optional[Any]				)      ->List[Any]:
										lowercase    :      Any       = (
										    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
										    """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
										)
										lowercase    :      Optional[Any]       = [
										    0,
										    3_293,
										    83,
										    10,
										    4_552,
										    4_989,
										    7_986,
										    678,
										    10,
										    5_915,
										    111,
										    179_459,
										    124_850,
										    4,
										    6_044,
										    237,
										    12,
										    6,
										    5,
										    6,
										    4,
										    6_780,
										    705,
										    15,
										    1_388,
										    44,
										    378,
										    10_114,
										    711,
										    152,
										    20,
										    6,
										    5,
										    22_376,
										    642,
										    1_221,
										    15_190,
										    34_153,
										    450,
										    5_608,
										    959,
										    1_119,
										    57_702,
										    136,
										    186,
										    47,
										    1_098,
										    29_367,
										    47,
										    # 4426, # What fairseq tokenizes from "<unk>": "_<"
										    # 3678, # What fairseq tokenizes from "<unk>": "unk"
										    # 2740, # What fairseq tokenizes from "<unk>": ">"
										    3,  # What we tokenize from "<unk>": "<unk>"
										    6,  # Residue from the tokenization: an extra sentencepiece underline
										    4,
										    6_044,
										    237,
										    6_284,
										    50_901,
										    528,
										    31,
										    90,
										    34,
										    927,
										    2,
										]
										# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
										# xlmr.eval()
										# xlmr.encode(symbols)

										self.assertListEqual(__magic_name__			, self.big_tokenizer.encode(__magic_name__				)				)






						@slow
						def      __snake_case      (		self  :Any				)      ->List[str]:
										# fmt: off
										lowercase    :      Optional[Any]       = {"""input_ids""": [[0, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 5_428, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 12_399, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 2_789, 1_328, 4_589, 42, 122_009, 115_774, 23, 805, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [0, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
										# fmt: on

										self.tokenizer_integration_test_util(
										    expected_encoding=__magic_name__			, model_name="""xlm-roberta-base"""			, revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"""			, )








"""simple docstring"""







from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame


def      UpperCamelCase       ( _A = "laptop" )     ->       DataFrame:
				lowercase    :      List[str]       = F"""https://www.amazon.in/laptop/s?k={product}"""
				lowercase    :      str       = {
				    """User-Agent""": """Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36""",
				    """Accept-Language""": """en-US, en;q=0.5""",
				}
				lowercase    :      str       = BeautifulSoup(requests.get(_A  ,				headers=_A ).text )
				# Initialize a Pandas dataframe with the column titles
				lowercase    :      Optional[Any]       = DataFrame(
				    columns=[
				        """Product Title""",
				        """Product Link""",
				        """Current Price of the product""",
				        """Product Rating""",
				        """MRP of the product""",
				        """Discount""",
				    ] )
				# Loop through each entry and store them in the dataframe
				for item, _ in zip_longest(
				    soup.find_all(
				        """div"""  ,				attrs={"""class""": """s-result-item""", """data-component-type""": """s-search-result"""}  ,				)  ,				soup.find_all("""div"""  ,				attrs={"""class""": """a-row a-size-base a-color-base"""} )  ,				):
								try:
												lowercase    :      str       = item.ha.text
												lowercase    :      Tuple       = """https://www.amazon.in/""" + item.ha.a["""href"""]
												lowercase    :      Tuple       = item.find("""span"""  ,				attrs={"""class""": """a-offscreen"""} ).text
												try:
																lowercase    :      Any       = item.find("""span"""  ,				attrs={"""class""": """a-icon-alt"""} ).text
												except AttributeError:
																lowercase    :      Tuple       = """Not available"""
												try:
																lowercase    :      Any       = (
																    """₹"""
																    + item.find(
																        """span"""  ,				attrs={"""class""": """a-price a-text-price"""} ).text.split("""₹""" )[1]
																)
												except AttributeError:
																lowercase    :      Any       = """"""
												try:
																lowercase    :      List[str]       = float(
																    (
																        (
																            float(product_mrp.strip("""₹""" ).replace(""","""  ,				"""""" ) )
																            - float(product_price.strip("""₹""" ).replace(""","""  ,				"""""" ) )
																        )
																        / float(product_mrp.strip("""₹""" ).replace(""","""  ,				"""""" ) )
																    )
																    * 100 )
												except ValueError:
																lowercase    :      Tuple       = float("""nan""" )
								except AttributeError:
												pass
								lowercase    :      Union[str, Any]       = [
								    product_title,
								    product_link,
								    product_price,
								    product_rating,
								    product_mrp,
								    discount,
								]
				lowercase    :      Tuple       = """ """
				lowercase    :      List[str]       = """ """
				data_frame.index += 1
				return data_frame


if __name__ == "__main__":
				_lowerCAmelCase            =	'headphones'
				get_amazon_product_data(product).to_csv(F'Amazon Product Data for {product}.csv')
