



'''simple docstring'''





import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowercase_									=    get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")






@require_sentencepiece
@require_tokenizers
class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Optional[int]		=			GPTSwaTokenizer
 __lowerCamelCase       :			Optional[Any]		=			False
 __lowerCamelCase       :			List[Any]		=			True
 __lowerCamelCase       :			Union[str, Any]		=			False



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      super().setUp()

      # We have a SentencePiece fixture for testing
      _a           =    GPTSwaTokenizer(A					,  eos_token='''<unk>'''					,  bos_token='''<unk>'''					,  pad_token='''<unk>'''			)

      tokenizer.save_pretrained(self.tmpdirname			)



 def    a__    (self					,  A			)       ->	str:


      """simple docstring"""



      _a           =    '''This is a test'''
      _a           =    '''This is a test'''
      return input_text, output_text



 def    a__    (self			)       ->	List[str]:


      """simple docstring"""



      _a           =    '''<s>'''
      _a           =    1

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



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    list(self.get_tokenizer().get_vocab().keys()			)

      self.assertEqual(vocab_keys[0]					,  '''<unk>'''			)
      self.assertEqual(vocab_keys[1]					,  '''<s>'''			)
      self.assertEqual(vocab_keys[-1]					,  '''j'''			)
      self.assertEqual(len(A			)					,  2_000			)



 def    a__    (self			)       ->	List[str]:


      """simple docstring"""



      self.assertEqual(self.get_tokenizer().vocab_size					,  2_000			)



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    GPTSwaTokenizer(A			)

      _a           =    tokenizer.tokenize('''This is a test'''			)
      self.assertListEqual(A					,  ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']			)

      self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)					,  [465, 287, 265, 631, 842]			)

      _a           =    tokenizer.tokenize('''I was born in 92000, and this is falsé.'''			)
      # fmt: off
      self.assertListEqual(
          A					,  ['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']					,  )
      # fmt: on

      _a           =    tokenizer.convert_tokens_to_ids(A			)
      self.assertListEqual(
          A					,  [262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]					,  )

      _a           =    tokenizer.convert_ids_to_tokens(A			)
      # fmt: off
      self.assertListEqual(
          A					,  ['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']			)
      # fmt: on



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    GPTSwaTokenizer(A			)
      _a           =    ['''This is a test''', '''I was born in 92000, and this is falsé.''']
      _a           =    [
          [465, 287, 265, 631, 842],
          [262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
      ]

      # Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
      for text, expected_ids in zip(A					,  A			):
           self.assertListEqual(tokenizer.encode_fast(A			)					,  A			)

      # Test that decode_fast returns the input text
      for text, token_ids in zip(A					,  A			):
           self.assertEqual(tokenizer.decode_fast(A			)					,  A			)



 @slow
 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    [
          '''<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')''',
          '''Hey there, how are you doing this fine day?''',
          '''This is a text with a trailing spaces followed by a dot     .''',
          '''Häj sväjs lillebrör! =)''',
          '''Det är inget fel på Mr. Cool''',
      ]

      # fmt: off
      _a           =    {'''input_ids''': [[63_423, 5, 6_811, 14_954, 282, 816, 3_821, 63_466, 63_425, 63_462, 18, 63_978, 678, 301, 1_320, 63_423, 63_455, 63_458, 18, 63_982, 4_246, 3_940, 1_901, 47_789, 5_547, 18_994], [19_630, 1_100, 63_446, 1_342, 633, 544, 4_488, 593, 5_102, 2_416, 63_495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_652, 428, 268, 1_936, 515, 268, 58_593, 22_413, 9_106, 546, 268, 33_213, 63_979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55_130, 63_450, 924, 63_449, 2_249, 4_062, 1_558, 318, 63_504, 21_498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2_827, 2_559, 332, 6_575, 63_443, 26_801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[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, 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]], '''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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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], [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]]}
      # fmt: on
      self.tokenizer_integration_test_util(
          expected_encoding=A					,  model_name='''AI-Sweden/gpt-sw3-126m'''					,  sequences=A					,  )








'''simple docstring'''





from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame




def lowerCAmelCase    (__A = "laptop"):



     """simple docstring"""



     _a           =    F'''https://www.amazon.in/laptop/s?k={product}'''
     _a           =    {
         '''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''',
     }
     _a           =    BeautifulSoup(requests.get(__A    ,      headers=__A).text)
     # Initialize a Pandas dataframe with the column titles
     _a           =    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:
               _a           =    item.ha.text
               _a           =    '''https://www.amazon.in/''' + item.ha.a['''href''']
               _a           =    item.find('''span'''    ,      attrs={'''class''': '''a-offscreen'''}).text
               try:
                    _a           =    item.find('''span'''    ,      attrs={'''class''': '''a-icon-alt'''}).text
               except AttributeError:
                    _a           =    '''Not available'''
               try:
                    _a           =    (
                        '''₹'''
                        + item.find(
                            '''span'''    ,      attrs={'''class''': '''a-price a-text-price'''}).text.split('''₹''')[1]
                    )
               except AttributeError:
                    _a           =    ''''''
               try:
                    _a           =    float(
                        (
                            (
                                float(product_mrp.strip('''₹''').replace(''','''    ,      ''''''))
                                - float(product_price.strip('''₹''').replace(''','''    ,      ''''''))
                            )
                            / float(product_mrp.strip('''₹''').replace(''','''    ,      ''''''))
                        )
                        * 100)
               except ValueError:
                    _a           =    float('''nan''')
          except AttributeError:
               pass
          _a           =    [
              product_title,
              product_link,
              product_price,
              product_rating,
              product_mrp,
              discount,
          ]
     _a           =    ''' '''
     _a           =    ''' '''
     data_frame.index += 1
     return data_frame


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



