



'''simple docstring'''





import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			str		=			None
 __lowerCamelCase       :			Optional[Any]		=			None



 @property
 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      return self.feat_extract_tester.prepare_feat_extract_dict()



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


      """simple docstring"""



      _a           =    self.feature_extraction_class(**self.feat_extract_dict			)
      self.assertTrue(hasattr(A					,  '''feature_size'''			)			)
      self.assertTrue(hasattr(A					,  '''sampling_rate'''			)			)
      self.assertTrue(hasattr(A					,  '''padding_value'''			)			)



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    self.feat_extract_tester.prepare_inputs_for_common()
      _a           =    self.feature_extraction_class(**self.feat_extract_dict			)
      _a           =    feat_extract.model_input_names[0]

      _a           =    BatchFeature({input_name: speech_inputs}			)

      self.assertTrue(all(len(A			) == len(A			) for x, y in zip(A					,  processed_features[input_name]			)			)			)

      _a           =    self.feat_extract_tester.prepare_inputs_for_common(equal_length=A			)
      _a           =    BatchFeature({input_name: speech_inputs}					,  tensor_type='''np'''			)

      _a           =    processed_features[input_name]

      if len(batch_features_input.shape			) < 3:
           _a           =    batch_features_input[:, :, None]

      self.assertTrue(
          batch_features_input.shape
          == (self.feat_extract_tester.batch_size, len(speech_inputs[0]			), self.feat_extract_tester.feature_size)			)



 @require_torch
 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.feat_extract_tester.prepare_inputs_for_common(equal_length=A			)
      _a           =    self.feature_extraction_class(**self.feat_extract_dict			)
      _a           =    feat_extract.model_input_names[0]

      _a           =    BatchFeature({input_name: speech_inputs}					,  tensor_type='''pt'''			)

      _a           =    processed_features[input_name]

      if len(batch_features_input.shape			) < 3:
           _a           =    batch_features_input[:, :, None]

      self.assertTrue(
          batch_features_input.shape
          == (self.feat_extract_tester.batch_size, len(speech_inputs[0]			), self.feat_extract_tester.feature_size)			)



 @require_tf
 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    self.feat_extract_tester.prepare_inputs_for_common(equal_length=A			)
      _a           =    self.feature_extraction_class(**self.feat_extract_dict			)
      _a           =    feat_extract.model_input_names[0]

      _a           =    BatchFeature({input_name: speech_inputs}					,  tensor_type='''tf'''			)

      _a           =    processed_features[input_name]

      if len(batch_features_input.shape			) < 3:
           _a           =    batch_features_input[:, :, None]

      self.assertTrue(
          batch_features_input.shape
          == (self.feat_extract_tester.batch_size, len(speech_inputs[0]			), self.feat_extract_tester.feature_size)			)



 def    a__    (self					,  A=False			)       ->	Any:


      """simple docstring"""



      def _inputs_have_equal_length(A			):
           _a           =    len(input[0]			)
           for input_slice in input[1:]:
                if len(A			) != length:
                     return False
           return True

      def _inputs_are_equal(A					,  A			):
           if len(A			) != len(A			):
                return False

           for input_slice_a, input_slice_a in zip(A					,  A			):
                if not np.allclose(np.asarray(A			)					,  np.asarray(A			)					,  atol=1E-3			):
                     return False
           return True

      _a           =    self.feature_extraction_class(**self.feat_extract_dict			)
      _a           =    self.feat_extract_tester.prepare_inputs_for_common(numpify=A			)
      _a           =    feat_extract.model_input_names[0]

      _a           =    BatchFeature({input_name: speech_inputs}			)

      _a           =    self.feat_extract_tester.seq_length_diff
      _a           =    self.feat_extract_tester.max_seq_length + pad_diff
      _a           =    self.feat_extract_tester.min_seq_length
      _a           =    self.feat_extract_tester.batch_size
      _a           =    self.feat_extract_tester.feature_size

      # test padding for List[int] + numpy
      _a           =    feat_extract.pad(A					,  padding=A			)
      _a           =    input_a[input_name]

      _a           =    feat_extract.pad(A					,  padding='''longest'''			)
      _a           =    input_a[input_name]

      _a           =    feat_extract.pad(A					,  padding='''max_length'''					,  max_length=len(speech_inputs[-1]			)			)
      _a           =    input_a[input_name]

      _a           =    feat_extract.pad(A					,  padding='''longest'''					,  return_tensors='''np'''			)
      _a           =    input_a[input_name]

      # max_length parameter has to be provided when setting `padding="max_length"`
      with self.assertRaises(A			):
           feat_extract.pad(A					,  padding='''max_length'''			)[input_name]

      _a           =    feat_extract.pad(
          A					,  padding='''max_length'''					,  max_length=A					,  return_tensors='''np'''			)
      _a           =    input_a[input_name]

      self.assertFalse(_inputs_have_equal_length(A			)			)
      self.assertTrue(_inputs_have_equal_length(A			)			)
      self.assertTrue(_inputs_have_equal_length(A			)			)
      self.assertTrue(_inputs_are_equal(A					,  A			)			)
      self.assertTrue(len(input_a[0]			) == pad_min_length			)
      self.assertTrue(len(input_a[1]			) == pad_min_length + pad_diff			)
      self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]			))			)
      self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)			)

      if feature_size > 1:
           self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size			)

      # test padding for `pad_to_multiple_of` for List[int] + numpy
      _a           =    feat_extract.pad(A					,  pad_to_multiple_of=10			)
      _a           =    input_a[input_name]

      _a           =    feat_extract.pad(A					,  padding='''longest'''					,  pad_to_multiple_of=10			)
      _a           =    input_a[input_name]

      _a           =    feat_extract.pad(
          A					,  padding='''max_length'''					,  pad_to_multiple_of=10					,  max_length=A			)
      _a           =    input_a[input_name]

      _a           =    feat_extract.pad(
          A					,  padding='''max_length'''					,  pad_to_multiple_of=10					,  max_length=A					,  return_tensors='''np'''					,  )
      _a           =    input_a[input_name]

      self.assertTrue(all(len(A			) % 10 == 0 for x in input_a			)			)
      self.assertTrue(_inputs_are_equal(A					,  A			)			)

      _a           =    pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
      self.assertTrue(all(len(A			) == expected_mult_pad_length for x in input_a			)			)
      self.assertEqual(input_a.shape[:2]					,  (batch_size, expected_mult_pad_length)			)

      if feature_size > 1:
           self.assertTrue(input_a.shape[2] == feature_size			)

      # Check padding value is correct
      _a           =    (np.ones(self.feat_extract_tester.feature_size			) * feat_extract.padding_value).sum()
      self.assertTrue(
          abs(np.asarray(input_a[0]			)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)			)
          < 1E-3			)
      self.assertTrue(
          abs(
              np.asarray(input_a[1]			)[pad_min_length + pad_diff :].sum()
              - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)			)
          < 1E-3			)
      self.assertTrue(
          abs(
              np.asarray(input_a[2]			)[pad_min_length + 2 * pad_diff :].sum()
              - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)			)
          < 1E-3			)
      self.assertTrue(
          abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)			) < 1E-3			)
      self.assertTrue(
          abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)			)
          < 1E-3			)



 def    a__    (self					,  A=False			)       ->	List[Any]:


      """simple docstring"""



      def _inputs_have_equal_length(A			):
           _a           =    len(input[0]			)
           for input_slice in input[1:]:
                if len(A			) != length:
                     return False
           return True

      def _inputs_are_equal(A					,  A			):
           if len(A			) != len(A			):
                return False

           for input_slice_a, input_slice_a in zip(A					,  A			):
                if not np.allclose(np.asarray(A			)					,  np.asarray(A			)					,  atol=1E-3			):
                     return False
           return True

      _a           =    self.feature_extraction_class(**self.feat_extract_dict			)
      _a           =    self.feat_extract_tester.prepare_inputs_for_common(numpify=A			)
      _a           =    feat_extract.model_input_names[0]

      _a           =    BatchFeature({input_name: speech_inputs}			)

      # truncate to smallest
      _a           =    feat_extract.pad(
          A					,  padding='''max_length'''					,  max_length=len(speech_inputs[0]			)					,  truncation=A			)
      _a           =    input_a[input_name]

      _a           =    feat_extract.pad(A					,  padding='''max_length'''					,  max_length=len(speech_inputs[0]			)			)
      _a           =    input_a[input_name]

      self.assertTrue(_inputs_have_equal_length(A			)			)
      self.assertFalse(_inputs_have_equal_length(A			)			)

      # truncate to smallest with np
      _a           =    feat_extract.pad(
          A					,  padding='''max_length'''					,  max_length=len(speech_inputs[0]			)					,  return_tensors='''np'''					,  truncation=A					,  )
      _a           =    input_a[input_name]

      _a           =    feat_extract.pad(
          A					,  padding='''max_length'''					,  max_length=len(speech_inputs[0]			)					,  return_tensors='''np'''			)
      _a           =    input_a[input_name]

      self.assertTrue(_inputs_have_equal_length(A			)			)
      self.assertTrue(input_a.shape[1] == len(speech_inputs[0]			)			)

      # since truncation forces padding to be smaller than longest input
      # function can't return `np.ndarray`, but has to return list
      self.assertFalse(_inputs_have_equal_length(A			)			)

      # truncate to middle
      _a           =    feat_extract.pad(
          A					,  padding='''max_length'''					,  max_length=len(speech_inputs[1]			)					,  truncation=A					,  return_tensors='''np'''					,  )
      _a           =    input_a[input_name]

      _a           =    feat_extract.pad(
          A					,  padding='''max_length'''					,  max_length=len(speech_inputs[1]			)					,  truncation=A			)
      _a           =    input_a[input_name]

      _a           =    feat_extract.pad(
          A					,  padding='''max_length'''					,  max_length=len(speech_inputs[1]			)					,  return_tensors='''np'''			)
      _a           =    input_a[input_name]

      self.assertTrue(input_a.shape[1] == len(speech_inputs[1]			)			)
      self.assertTrue(_inputs_have_equal_length(A			)			)
      self.assertTrue(_inputs_have_equal_length(A			)			)
      self.assertTrue(_inputs_are_equal(A					,  A			)			)

      # since truncation forces padding to be smaller than longest input
      # function can't return `np.ndarray`, but has to return list
      self.assertFalse(_inputs_have_equal_length(A			)			)
      self.assertTrue(len(input_a[-1]			) == len(speech_inputs[-1]			)			)

      # padding has to be max_length when setting `truncation=True`
      with self.assertRaises(A			):
           feat_extract.pad(A					,  truncation=A			)[input_name]

      # padding has to be max_length when setting `truncation=True`
      with self.assertRaises(A			):
           feat_extract.pad(A					,  padding='''longest'''					,  truncation=A			)[input_name]

      # padding has to be max_length when setting `truncation=True`
      with self.assertRaises(A			):
           feat_extract.pad(A					,  padding='''longest'''					,  truncation=A			)[input_name]

      # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
      with self.assertRaises(A			):
           feat_extract.pad(A					,  padding='''max_length'''					,  truncation=A			)[input_name]

      # test truncation for `pad_to_multiple_of` for List[int] + numpy
      _a           =    12
      _a           =    feat_extract.pad(
          A					,  padding='''max_length'''					,  max_length=len(speech_inputs[0]			)					,  pad_to_multiple_of=A					,  truncation=A					,  )
      _a           =    input_a[input_name]

      _a           =    feat_extract.pad(
          A					,  padding='''max_length'''					,  max_length=len(speech_inputs[0]			)					,  pad_to_multiple_of=A					,  )
      _a           =    input_a[input_name]

      # retrieve expected_length as multiple of pad_to_multiple_of
      _a           =    len(speech_inputs[0]			)
      if expected_length % pad_to_multiple_of != 0:
           _a           =    ((len(speech_inputs[0]			) // pad_to_multiple_of) + 1) * pad_to_multiple_of

      self.assertTrue(len(input_a[0]			) == expected_length			)
      self.assertTrue(_inputs_have_equal_length(A			)			)
      self.assertFalse(_inputs_have_equal_length(A			)			)



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


      """simple docstring"""



      self._check_padding(numpify=A			)



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


      """simple docstring"""



      self._check_padding(numpify=A			)



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


      """simple docstring"""



      self._check_truncation(numpify=A			)



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


      """simple docstring"""



      self._check_truncation(numpify=A			)



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


      """simple docstring"""



      _a           =    self.feature_extraction_class(**self.feat_extract_dict			)
      _a           =    self.feat_extract_tester.prepare_inputs_for_common()
      _a           =    feat_extract.model_input_names[0]

      _a           =    BatchFeature({input_name: speech_inputs}			)

      _a           =    feat_extract.pad(A					,  padding='''longest'''					,  return_tensors='''np'''			)[input_name]
      _a           =    feat_extract.pad(A					,  padding='''longest'''					,  return_tensors='''pt'''			)[input_name]

      self.assertTrue(abs(input_np.astype(np.floataa			).sum() - input_pt.numpy().astype(np.floataa			).sum()			) < 1E-2			)



 @require_tf
 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    self.feature_extraction_class(**self.feat_extract_dict			)
      _a           =    self.feat_extract_tester.prepare_inputs_for_common()
      _a           =    feat_extract.model_input_names[0]

      _a           =    BatchFeature({input_name: speech_inputs}			)

      _a           =    feat_extract.pad(A					,  padding='''longest'''					,  return_tensors='''np'''			)[input_name]
      _a           =    feat_extract.pad(A					,  padding='''longest'''					,  return_tensors='''tf'''			)[input_name]

      self.assertTrue(abs(input_np.astype(np.floataa			).sum() - input_tf.numpy().astype(np.floataa			).sum()			) < 1E-2			)



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.feat_extract_dict
      _a           =    True
      _a           =    self.feature_extraction_class(**A			)
      _a           =    self.feat_extract_tester.prepare_inputs_for_common()
      _a           =    [len(A			) for x in speech_inputs]
      _a           =    feat_extract.model_input_names[0]

      _a           =    BatchFeature({input_name: speech_inputs}			)

      _a           =    feat_extract.pad(A					,  padding='''longest'''					,  return_tensors='''np'''			)
      self.assertIn('''attention_mask'''					,  A			)
      self.assertListEqual(list(processed.attention_mask.shape			)					,  list(processed[input_name].shape[:2]			)			)
      self.assertListEqual(processed.attention_mask.sum(-1			).tolist()					,  A			)



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


      """simple docstring"""



      _a           =    self.feat_extract_dict
      _a           =    True
      _a           =    self.feature_extraction_class(**A			)
      _a           =    self.feat_extract_tester.prepare_inputs_for_common()
      _a           =    [len(A			) for x in speech_inputs]
      _a           =    feat_extract.model_input_names[0]

      _a           =    BatchFeature({input_name: speech_inputs}			)
      _a           =    min(A			)

      _a           =    feat_extract.pad(
          A					,  padding='''max_length'''					,  max_length=A					,  truncation=A					,  return_tensors='''np'''			)
      self.assertIn('''attention_mask'''					,  A			)
      self.assertListEqual(
          list(processed_pad.attention_mask.shape			)					,  [processed_pad[input_name].shape[0], max_length]			)
      self.assertListEqual(
          processed_pad.attention_mask[:, :max_length].sum(-1			).tolist()					,  [max_length for x in speech_inputs]			)








'''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""")



