





'''simple docstring'''




import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
  import torch

  from transformers import (
      MraForMaskedLM,
      MraForMultipleChoice,
      MraForQuestionAnswering,
      MraForSequenceClassification,
      MraForTokenClassification,
      MraModel,
  )
  from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST


class  SCREAMING_SNAKE_CASE    :


     def __init__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase=2						,	_UpperCAmelCase=8						,	_UpperCAmelCase=True						,	_UpperCAmelCase=True						,	_UpperCAmelCase=True						,	_UpperCAmelCase=True						,	_UpperCAmelCase=99						,	_UpperCAmelCase=16						,	_UpperCAmelCase=5						,	_UpperCAmelCase=2						,	_UpperCAmelCase=36						,	_UpperCAmelCase="gelu"						,	_UpperCAmelCase=0.0						,	_UpperCAmelCase=0.0						,	_UpperCAmelCase=512						,	_UpperCAmelCase=16						,	_UpperCAmelCase=2						,	_UpperCAmelCase=0.02						,	_UpperCAmelCase=3						,	_UpperCAmelCase=4						,	_UpperCAmelCase=None						,	):

          '''simple docstring'''






          __A						: Union[str, Any]      =       parent
          __A						: int      =       batch_size
          __A						: List[Any]      =       seq_length
          __A						: Optional[int]      =       is_training
          __A						: Dict      =       use_input_mask
          __A						: Tuple      =       use_token_type_ids
          __A						: Optional[int]      =       use_labels
          __A						: Tuple      =       vocab_size
          __A						: Dict      =       hidden_size
          __A						: Any      =       num_hidden_layers
          __A						: int      =       num_attention_heads
          __A						: Optional[Any]      =       intermediate_size
          __A						: List[str]      =       hidden_act
          __A						: Union[str, Any]      =       hidden_dropout_prob
          __A						: Optional[Any]      =       attention_probs_dropout_prob
          __A						: List[Any]      =       max_position_embeddings
          __A						: Tuple      =       type_vocab_size
          __A						: Any      =       type_sequence_label_size
          __A						: str      =       initializer_range
          __A						: Optional[Any]      =       num_labels
          __A						: List[Any]      =       num_choices
          __A						: Union[str, Any]      =       scope


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size)

          __A						: Tuple      =       None
          if self.use_input_mask:
               __A						: Dict      =       random_attention_mask([self.batch_size, self.seq_length])

          __A						: Dict      =       None
          if self.use_token_type_ids:
               __A						: Union[str, Any]      =       ids_tensor([self.batch_size, self.seq_length]						,	self.type_vocab_size)

          __A						: Tuple      =       None
          __A						: Tuple      =       None
          __A						: int      =       None
          if self.use_labels:
               __A						: int      =       ids_tensor([self.batch_size]						,	self.type_sequence_label_size)
               __A						: Any      =       ids_tensor([self.batch_size, self.seq_length]						,	self.num_labels)
               __A						: Optional[Any]      =       ids_tensor([self.batch_size]						,	self.num_choices)

          __A						: Optional[Any]      =       self.get_config()

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return MraConfig(
              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						,	is_decoder=A_						,	initializer_range=self.initializer_range						,	)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Tuple      =       self.get_config()
          __A						: Tuple      =       300
          return config


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          (
              (
              __A				
          ) ,(
              __A				
          ) ,(
              __A				
          ) ,(
              __A				
          ) ,(
              __A				
          ) ,(
              __A				
          ) ,(
              __A				
          ) ,				
          )						: List[Any]      =       self.prepare_config_and_inputs()

          __A						: List[str]      =       True
          __A						: Optional[Any]      =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
          __A						: List[str]      =       ids_tensor([self.batch_size, self.seq_length]						,	vocab_size=2)

          return (
              config,
              input_ids,
              token_type_ids,
              input_mask,
              sequence_labels,
              token_labels,
              choice_labels,
              encoder_hidden_states,
              encoder_attention_mask,
          )


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Any      =       MraModel(config=A_)
          model.to(A_)
          model.eval()
          __A						: Any      =       model(A_						,	attention_mask=A_						,	token_type_ids=A_)
          __A						: Optional[int]      =       model(A_						,	token_type_ids=A_)
          __A						: Union[str, Any]      =       model(A_)
          self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size))


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	):

          '''simple docstring'''






          __A						: Optional[int]      =       True
          __A						: Tuple      =       MraModel(A_)
          model.to(A_)
          model.eval()
          __A						: List[str]      =       model(
              A_						,	attention_mask=A_						,	token_type_ids=A_						,	encoder_hidden_states=A_						,	encoder_attention_mask=A_						,	)
          __A						: int      =       model(
              A_						,	attention_mask=A_						,	token_type_ids=A_						,	encoder_hidden_states=A_						,	)
          __A						: Optional[Any]      =       model(A_						,	attention_mask=A_						,	token_type_ids=A_)
          self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size))


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: List[Any]      =       MraForMaskedLM(config=A_)
          model.to(A_)
          model.eval()
          __A						: List[Any]      =       model(A_						,	attention_mask=A_						,	token_type_ids=A_						,	labels=A_)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size))


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Dict      =       MraForQuestionAnswering(config=A_)
          model.to(A_)
          model.eval()
          __A						: Tuple      =       model(
              A_						,	attention_mask=A_						,	token_type_ids=A_						,	start_positions=A_						,	end_positions=A_						,	)
          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						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Dict      =       self.num_labels
          __A						: Dict      =       MraForSequenceClassification(A_)
          model.to(A_)
          model.eval()
          __A						: Optional[Any]      =       model(A_						,	attention_mask=A_						,	token_type_ids=A_						,	labels=A_)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels))


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Optional[int]      =       self.num_labels
          __A						: List[Any]      =       MraForTokenClassification(config=A_)
          model.to(A_)
          model.eval()
          __A						: List[Any]      =       model(A_						,	attention_mask=A_						,	token_type_ids=A_						,	labels=A_)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels))


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: List[Any]      =       self.num_choices
          __A						: List[Any]      =       MraForMultipleChoice(config=A_)
          model.to(A_)
          model.eval()
          __A						: Optional[int]      =       input_ids.unsqueeze(1).expand(-1						,	self.num_choices						,	-1).contiguous()
          __A						: Union[str, Any]      =       token_type_ids.unsqueeze(1).expand(-1						,	self.num_choices						,	-1).contiguous()
          __A						: List[Any]      =       input_mask.unsqueeze(1).expand(-1						,	self.num_choices						,	-1).contiguous()
          __A						: Any      =       model(
              A_						,	attention_mask=A_						,	token_type_ids=A_						,	labels=A_						,	)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_choices))


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[str]      =       self.prepare_config_and_inputs()
          (
              (
              __A				
          ) ,(
              __A				
          ) ,(
              __A				
          ) ,(
              __A				
          ) ,(
              __A				
          ) ,(
              __A				
          ) ,(
              __A				
          ) ,				
          )						: Union[str, Any]      =       config_and_inputs
          __A						: Optional[Any]      =       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
          return config, inputs_dict




@require_torch
class  SCREAMING_SNAKE_CASE    (a__					,		unittest.TestCase							):
     lowerCAmelCase					=       (
         (
             MraModel,
             MraForMaskedLM,
             MraForMultipleChoice,
             MraForQuestionAnswering,
             MraForSequenceClassification,
             MraForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     lowerCAmelCase					=       False
     lowerCAmelCase					=       False
     lowerCAmelCase					=       False
     lowerCAmelCase					=       False

     lowerCAmelCase					=       ()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       MraModelTester(self)
          __A						: Dict      =       ConfigTester(self						,	config_class=A_						,	hidden_size=37)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          self.config_tester.run_common_tests()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: str      =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A_)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[str]      =       self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
               __A						: Optional[int]      =       type
               self.model_tester.create_and_check_model(*A_)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[Any]      =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*A_)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[int]      =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*A_)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*A_)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*A_)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: int      =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*A_)


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __A						: List[str]      =       MraModel.from_pretrained(A_)
               self.assertIsNotNone(A_)


     @unittest.skip(reason='MRA does not output attentions')
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return




@require_torch
class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       MraModel.from_pretrained('uw-madison/mra-base-512-4')
          __A						: List[str]      =       torch.arange(256).unsqueeze(0)

          with torch.no_grad():
               __A						: Optional[int]      =       model(A_)[0]

          __A						: Dict      =       torch.Size((1, 256, 768))
          self.assertEqual(output.shape						,	A_)

          __A						: str      =       torch.tensor(
              [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]])

          self.assertTrue(torch.allclose(output[:, :3, :3]						,	A_						,	atol=1e-4))


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4')
          __A						: str      =       torch.arange(256).unsqueeze(0)

          with torch.no_grad():
               __A						: List[str]      =       model(A_)[0]

          __A						: Dict      =       5_0265

          __A						: str      =       torch.Size((1, 256, vocab_size))
          self.assertEqual(output.shape						,	A_)

          __A						: Optional[Any]      =       torch.tensor(
              [[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]])

          self.assertTrue(torch.allclose(output[:, :3, :3]						,	A_						,	atol=1e-4))


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Tuple      =       MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3')
          __A						: Dict      =       torch.arange(4096).unsqueeze(0)

          with torch.no_grad():
               __A						: Optional[Any]      =       model(A_)[0]

          __A						: str      =       5_0265

          __A						: Union[str, Any]      =       torch.Size((1, 4096, vocab_size))
          self.assertEqual(output.shape						,	A_)

          __A						: int      =       torch.tensor(
              [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]])

          self.assertTrue(torch.allclose(output[:, :3, :3]						,	A_						,	atol=1e-4))





from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       str = "laptop"     ):


      '''simple docstring'''



      lowerCamelCase_		     =    f"""https://www.amazon.in/laptop/s?k={product}"""
      lowerCamelCase_		     =    {
          'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36',
          'Accept-Language': 'en-US, en;q=0.5',
      }
      lowerCamelCase_		     =    BeautifulSoup(requests.get(lowercase	,							headers=lowercase     ).text     )
      # Initialize a Pandas dataframe with the column titles
      lowerCamelCase_		     =    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:
                  lowerCamelCase_		     =    item.ha.text
                  lowerCamelCase_		     =    'https://www.amazon.in/' + item.ha.a['href']
                  lowerCamelCase_		     =    item.find('span'	,							attrs={'class': 'a-offscreen'}     ).text
                  try:
                        lowerCamelCase_		     =    item.find('span'	,							attrs={'class': 'a-icon-alt'}     ).text
                  except AttributeError:
                        lowerCamelCase_		     =    'Not available'
                  try:
                        lowerCamelCase_		     =    (
                            '₹'
                            + item.find(
                                'span'	,							attrs={'class': 'a-price a-text-price'}     ).text.split('₹'     )[1]
                        )
                  except AttributeError:
                        lowerCamelCase_		     =    ''
                  try:
                        lowerCamelCase_		     =    float(
                            (
                                (
                                    float(product_mrp.strip('₹'     ).replace(','	,							''     )     )
                                    - float(product_price.strip('₹'     ).replace(','	,							''     )     )
                                )
                                / float(product_mrp.strip('₹'     ).replace(','	,							''     )     )
                            )
                            * 1_00     )
                  except ValueError:
                        lowerCamelCase_		     =    float('nan'     )
            except AttributeError:
                  pass
            lowerCamelCase_		     =    [
                product_title,
                product_link,
                product_price,
                product_rating,
                product_mrp,
                discount,
            ]
      lowerCamelCase_		     =    ' '
      lowerCamelCase_		     =    ' '
      data_frame.index += 1
      return data_frame


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



