


'''simple docstring'''



from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame







def 			_UpperCamelCase					(						UpperCamelCase__ = "laptop"    ):
			UpperCAmelCase__     :    Union[str, Any]         =   f'''https://www.amazon.in/laptop/s?k={product}'''
			UpperCAmelCase__     :    List[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""",
			}
			UpperCAmelCase__     :    List[Any]         =   BeautifulSoup(requests.get(A__       ,					headers=A__    ).text    )
			# Initialize a Pandas dataframe with the column titles
			UpperCAmelCase__     :    str         =   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:
									UpperCAmelCase__     :    Optional[int]         =   item.ha.text
									UpperCAmelCase__     :    Union[str, Any]         =   """https://www.amazon.in/""" + item.ha.a["""href"""]
									UpperCAmelCase__     :    List[Any]         =   item.find("""span"""       ,					attrs={"""class""": """a-offscreen"""}    ).text
									try:
												UpperCAmelCase__     :    List[Any]         =   item.find("""span"""       ,					attrs={"""class""": """a-icon-alt"""}    ).text
									except AttributeError:
												UpperCAmelCase__     :    Tuple         =   """Not available"""
									try:
												UpperCAmelCase__     :    str         =   (
												    """₹"""
												    + item.find(
												        """span"""       ,					attrs={"""class""": """a-price a-text-price"""}    ).text.split("""₹"""    )[1]
												)
									except AttributeError:
												UpperCAmelCase__     :    Dict         =   """"""
									try:
												UpperCAmelCase__     :    List[str]         =   float(
												    (
												        (
												            float(product_mrp.strip("""₹"""    ).replace(""","""       ,					""""""    )    )
												            - float(product_price.strip("""₹"""    ).replace(""","""       ,					""""""    )    )
												        )
												        / float(product_mrp.strip("""₹"""    ).replace(""","""       ,					""""""    )    )
												    )
												    * 1_0_0    )
									except ValueError:
												UpperCAmelCase__     :    Any         =   float("""nan"""    )
						except AttributeError:
									pass
						UpperCAmelCase__     :    str         =   [
						    product_title,
						    product_link,
						    product_price,
						    product_rating,
						    product_mrp,
						    discount,
						]
			UpperCAmelCase__     :    List[Any]         =   """ """
			UpperCAmelCase__     :    Union[str, Any]         =   """ """
			data_frame.index += 1
			return data_frame


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


'''simple docstring'''






import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     DebertaForMaskedLM,
     DebertaForQuestionAnswering,
     DebertaForSequenceClassification,
     DebertaForTokenClassification,
     DebertaModel,
 )
 from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  def __init__(      self   :     Optional[Any]					,lowercase__   :     Optional[Any]					,lowercase__   :     Optional[int]=1_3					,lowercase__   :     Any=7					,lowercase__   :     Union[str, Any]=True					,lowercase__   :     Optional[int]=True					,lowercase__   :     List[str]=True					,lowercase__   :     str=True					,lowercase__   :     Dict=9_9					,lowercase__   :     Union[str, Any]=3_2					,lowercase__   :     List[str]=5					,lowercase__   :     int=4					,lowercase__   :     Dict=3_7					,lowercase__   :     Union[str, Any]="gelu"					,lowercase__   :     str=0.1					,lowercase__   :     List[str]=0.1					,lowercase__   :     Any=5_1_2					,lowercase__   :     Optional[int]=1_6					,lowercase__   :     Optional[int]=2					,lowercase__   :     Optional[int]=0.0_2					,lowercase__   :     Dict=False					,lowercase__   :     Optional[int]=True					,lowercase__   :     str="None"					,lowercase__   :     Optional[int]=3					,lowercase__   :     List[Any]=4					,lowercase__   :     Union[str, Any]=None					,):
   __lowercase							  = parent
   __lowercase							  = batch_size
   __lowercase							  = seq_length
   __lowercase							  = is_training
   __lowercase							  = use_input_mask
   __lowercase							  = use_token_type_ids
   __lowercase							  = use_labels
   __lowercase							  = vocab_size
   __lowercase							  = hidden_size
   __lowercase							  = num_hidden_layers
   __lowercase							  = num_attention_heads
   __lowercase							  = intermediate_size
   __lowercase							  = hidden_act
   __lowercase							  = hidden_dropout_prob
   __lowercase							  = attention_probs_dropout_prob
   __lowercase							  = max_position_embeddings
   __lowercase							  = type_vocab_size
   __lowercase							  = type_sequence_label_size
   __lowercase							  = initializer_range
   __lowercase							  = num_labels
   __lowercase							  = num_choices
   __lowercase							  = relative_attention
   __lowercase							  = position_biased_input
   __lowercase							  = pos_att_type
   __lowercase							  = scope






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = ids_tensor([self.batch_size, self.seq_length]					,self.vocab_size			)

   __lowercase							  = None
   if self.use_input_mask:
    __lowercase							  = ids_tensor([self.batch_size, self.seq_length]					,vocab_size=2			)

   __lowercase							  = None
   if self.use_token_type_ids:
    __lowercase							  = ids_tensor([self.batch_size, self.seq_length]					,self.type_vocab_size			)

   __lowercase							  = None
   __lowercase							  = None
   __lowercase							  = None
   if self.use_labels:
    __lowercase							  = ids_tensor([self.batch_size]					,self.type_sequence_label_size			)
    __lowercase							  = ids_tensor([self.batch_size, self.seq_length]					,self.num_labels			)
    __lowercase							  = ids_tensor([self.batch_size]					,self.num_choices			)

   __lowercase							  = self.get_config()

   return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   return DebertaConfig(
       vocab_size=self.vocab_size					,hidden_size=self.hidden_size					,num_hidden_layers=self.num_hidden_layers					,num_attention_heads=self.num_attention_heads					,intermediate_size=self.intermediate_size					,hidden_act=self.hidden_act					,hidden_dropout_prob=self.hidden_dropout_prob					,attention_probs_dropout_prob=self.attention_probs_dropout_prob					,max_position_embeddings=self.max_position_embeddings					,type_vocab_size=self.type_vocab_size					,initializer_range=self.initializer_range					,relative_attention=self.relative_attention					,position_biased_input=self.position_biased_input					,pos_att_type=self.pos_att_type					,)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   __lowercase							  = self.get_config()
   __lowercase							  = 3_0_0
   return config






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     Optional[Any]			):
   self.parent.assertListEqual(list(result.loss.size()			)					,[]			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     List[Any]					,lowercase__   :     Dict					,lowercase__   :     List[Any]					,lowercase__   :     Tuple					,lowercase__   :     Dict					,lowercase__   :     str					,lowercase__   :     Union[str, Any]			):
   __lowercase							  = DebertaModel(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,attention_mask=lowercase__					,token_type_ids=lowercase__			)[0]
   __lowercase							  = model(lowercase__					,token_type_ids=lowercase__			)[0]
   __lowercase							  = model(lowercase__			)[0]

   self.parent.assertListEqual(list(sequence_output.size()			)					,[self.batch_size, self.seq_length, self.hidden_size]			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple					,lowercase__   :     str					,lowercase__   :     Tuple					,lowercase__   :     Optional[Any]					,lowercase__   :     Dict					,lowercase__   :     int					,lowercase__   :     Tuple					,lowercase__   :     int			):
   __lowercase							  = DebertaForMaskedLM(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,attention_mask=lowercase__					,token_type_ids=lowercase__					,labels=lowercase__			)

   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length, self.vocab_size)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     int					,lowercase__   :     Optional[int]					,lowercase__   :     Optional[int]					,lowercase__   :     int					,lowercase__   :     Union[str, Any]					,lowercase__   :     Dict					,lowercase__   :     List[Any]					,lowercase__   :     Optional[Any]			):
   __lowercase							  = self.num_labels
   __lowercase							  = DebertaForSequenceClassification(lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,attention_mask=lowercase__					,token_type_ids=lowercase__					,labels=lowercase__			)
   self.parent.assertListEqual(list(result.logits.size()			)					,[self.batch_size, self.num_labels]			)
   self.check_loss_output(lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     Optional[int]					,lowercase__   :     Optional[int]					,lowercase__   :     Optional[int]					,lowercase__   :     Tuple					,lowercase__   :     List[str]					,lowercase__   :     List[str]					,lowercase__   :     Optional[int]			):
   __lowercase							  = self.num_labels
   __lowercase							  = DebertaForTokenClassification(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,attention_mask=lowercase__					,token_type_ids=lowercase__					,labels=lowercase__			)
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length, self.num_labels)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     Any					,lowercase__   :     Any					,lowercase__   :     Dict					,lowercase__   :     Any					,lowercase__   :     Optional[Any]					,lowercase__   :     int					,lowercase__   :     List[str]			):
   __lowercase							  = DebertaForQuestionAnswering(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(
       lowercase__					,attention_mask=lowercase__					,token_type_ids=lowercase__					,start_positions=lowercase__					,end_positions=lowercase__					,)
   self.parent.assertEqual(result.start_logits.shape					,(self.batch_size, self.seq_length)			)
   self.parent.assertEqual(result.end_logits.shape					,(self.batch_size, self.seq_length)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   __lowercase							  = self.prepare_config_and_inputs()
   (
       (
       __lowercase
   )  ,	(
       __lowercase
   )  ,	(
       __lowercase
   )  ,	(
       __lowercase
   )  ,	(
       __lowercase
   )  ,	(
       __lowercase
   )  ,	(
       __lowercase
   )  ,	
   )							  = config_and_inputs
   __lowercase							  = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
   return config, inputs_dict






@require_torch
class 			lowercase_	(lowerCamelCase__				,       lowerCamelCase__				,       unittest.TestCase      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Tuple							=			(
      (
          DebertaModel,
          DebertaForMaskedLM,
          DebertaForSequenceClassification,
          DebertaForTokenClassification,
          DebertaForQuestionAnswering,
      )
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			(
      {
          'feature-extraction': DebertaModel,
          'fill-mask': DebertaForMaskedLM,
          'question-answering': DebertaForQuestionAnswering,
          'text-classification': DebertaForSequenceClassification,
          'token-classification': DebertaForTokenClassification,
          'zero-shot': DebertaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE    :  Tuple							=			True
  SCREAMING_SNAKE_CASE    :  List[str]							=			False
  SCREAMING_SNAKE_CASE    :  List[str]							=			False
  SCREAMING_SNAKE_CASE    :  Any							=			False
  SCREAMING_SNAKE_CASE    :  List[Any]							=			False






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   __lowercase							  = DebertaModelTester(self			)
   __lowercase							  = ConfigTester(self					,config_class=lowercase__					,hidden_size=3_7			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   self.config_tester.run_common_tests()






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_deberta_model(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_deberta_for_sequence_classification(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_deberta_for_masked_lm(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_deberta_for_question_answering(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_deberta_for_token_classification(*lowercase__			)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __lowercase							  = DebertaModel.from_pretrained(lowercase__			)
    self.assertIsNotNone(lowercase__			)






@require_torch
@require_sentencepiece
@require_tokenizers
class 			lowercase_	(unittest.TestCase      ):




  """simple docstring"""




  @unittest.skip(reason='''Model not available yet'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   pass






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = DebertaModel.from_pretrained('''microsoft/deberta-base'''			)

   __lowercase							  = torch.tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]			)
   __lowercase							  = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
   with torch.no_grad():
    __lowercase							  = model(lowercase__					,attention_mask=lowercase__			)[0]
   # compare the actual values for a slice.
   __lowercase							  = torch.tensor(
       [[[-0.5_9_8_6, -0.8_0_5_5, -0.8_4_6_2], [1.4_4_8_4, -0.9_3_4_8, -0.8_0_5_9], [0.3_1_2_3, 0.0_0_3_2, -1.4_1_3_1]]]			)
   self.assertTrue(torch.allclose(output[:, 1:4, 1:4]					,lowercase__					,atol=1e-4			)					,F"{output[:, 1:4, 1:4]}"			)

