from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame





def    __lowercase     (						lowerCamelCase					:     str = "laptop"	):
 UpperCamelCase_					:						Tuple =					F"https://www.amazon.in/laptop/s?k={product}"
 UpperCamelCase_					:						int =					{
     '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',
 }
 UpperCamelCase_					:						Tuple =					BeautifulSoup(requests.get(lowerCamelCase  ,  headers=lowerCamelCase	).text	)
 # Initialize a Pandas dataframe with the column titles
 UpperCamelCase_					:						List[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:
   UpperCamelCase_					:						int =					item.ha.text
   UpperCamelCase_					:						Tuple =					'https://www.amazon.in/' + item.ha.a['href']
   UpperCamelCase_					:						Optional[int] =					item.find('span'  ,  attrs={'class': 'a-offscreen'}	).text
   try:
    UpperCamelCase_					:						Union[str, Any] =					item.find('span'  ,  attrs={'class': 'a-icon-alt'}	).text
   except AttributeError:
    UpperCamelCase_					:						Optional[int] =					'Not available'
   try:
    UpperCamelCase_					:						Optional[int] =					(
        '₹'
        + item.find(
            'span'  ,  attrs={'class': 'a-price a-text-price'}	).text.split('₹'	)[1]
    )
   except AttributeError:
    UpperCamelCase_					:						Dict =					''
   try:
    UpperCamelCase_					:						Optional[Any] =					float(
        (
            (
                float(product_mrp.strip('₹'	).replace(','  ,  ''	)	)
                - float(product_price.strip('₹'	).replace(','  ,  ''	)	)
            )
            / float(product_mrp.strip('₹'	).replace(','  ,  ''	)	)
        )
        * 100	)
   except ValueError:
    UpperCamelCase_					:						str =					float('nan'	)
  except AttributeError:
   pass
  UpperCamelCase_					:						Union[str, Any] =					[
      product_title,
      product_link,
      product_price,
      product_rating,
      product_mrp,
      discount,
  ]
 UpperCamelCase_					:						Union[str, Any] =					' '
 UpperCamelCase_					:						Dict =					' '
 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""")




import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
   import tensorflow as tf

   from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments





@require_tf
class 		_lowercase						(					unittest.TestCase       ):

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int   ,      snake_case	:							Union[str, Any]			)   ->						Optional[int]:

  """simple docstring"""





  for model_result in results.values():
   for batch_size, sequence_length in zip(model_result['bs']   ,      model_result['ss']			):
    UpperCamelCase_					:						Optional[Any] =					model_result['result'][batch_size][sequence_length]
    self.assertIsNotNone(snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						Dict =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      eager_mode=snake_case   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						Tuple =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						List[str] =					benchmark.run()
  self.check_results_dict_not_empty(results.time_inference_result			)
  self.check_results_dict_not_empty(results.memory_inference_result			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					'sgugger/tiny-distilbert-classification'
  UpperCamelCase_					:						int =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      only_pretrain_model=snake_case   ,      )
  UpperCamelCase_					:						int =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						Tuple =					benchmark.run()
  self.check_results_dict_not_empty(results.time_inference_result			)
  self.check_results_dict_not_empty(results.memory_inference_result			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						str =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						Any =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						List[str] =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						Optional[int] =					benchmark.run()
  self.check_results_dict_not_empty(results.time_inference_result			)
  self.check_results_dict_not_empty(results.memory_inference_result			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						str =					AutoConfig.from_pretrained(snake_case			)
  UpperCamelCase_					:						List[str] =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      eager_mode=snake_case   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						int =					TensorFlowBenchmark(snake_case   ,      [config]			)
  UpperCamelCase_					:						Optional[Any] =					benchmark.run()
  self.check_results_dict_not_empty(results.time_inference_result			)
  self.check_results_dict_not_empty(results.memory_inference_result			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						int =					AutoConfig.from_pretrained(snake_case			)
  UpperCamelCase_					:						Any =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						Tuple =					TensorFlowBenchmark(snake_case   ,      [config]			)
  UpperCamelCase_					:						Tuple =					benchmark.run()
  self.check_results_dict_not_empty(results.time_inference_result			)
  self.check_results_dict_not_empty(results.memory_inference_result			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						Tuple =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						Optional[int] =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						str =					benchmark.run()
  self.check_results_dict_not_empty(results.time_train_result			)
  self.check_results_dict_not_empty(results.memory_train_result			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						List[str] =					AutoConfig.from_pretrained(snake_case			)
  UpperCamelCase_					:						int =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						List[Any] =					TensorFlowBenchmark(snake_case   ,      [config]			)
  UpperCamelCase_					:						int =					benchmark.run()
  self.check_results_dict_not_empty(results.time_train_result			)
  self.check_results_dict_not_empty(results.memory_train_result			)

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

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'patrickvonplaten/t5-tiny-random'
  UpperCamelCase_					:						List[str] =					AutoConfig.from_pretrained(snake_case			)
  UpperCamelCase_					:						List[str] =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						int =					TensorFlowBenchmark(snake_case   ,      configs=[config]			)
  UpperCamelCase_					:						Optional[Any] =					benchmark.run()
  self.check_results_dict_not_empty(results.time_inference_result			)
  self.check_results_dict_not_empty(results.memory_inference_result			)

 @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU'			)			) == 0   ,      'Cannot do xla on CPU.'			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						int =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						Union[str, Any] =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      use_xla=snake_case   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						Union[str, Any] =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						List[str] =					benchmark.run()
  self.check_results_dict_not_empty(results.time_inference_result			)
  self.check_results_dict_not_empty(results.memory_inference_result			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					'sshleifer/tiny-gpt2'
  with tempfile.TemporaryDirectory() as tmp_dir:
   UpperCamelCase_					:						List[Any] =					TensorFlowBenchmarkArguments(
       models=[MODEL_ID]   ,      inference=snake_case   ,      save_to_csv=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      inference_time_csv_file=os.path.join(snake_case   ,      'inf_time.csv'			)   ,      inference_memory_csv_file=os.path.join(snake_case   ,      'inf_mem.csv'			)   ,      env_info_csv_file=os.path.join(snake_case   ,      'env.csv'			)   ,      multi_process=snake_case   ,      )
   UpperCamelCase_					:						List[str] =					TensorFlowBenchmark(snake_case			)
   benchmark.run()
   self.assertTrue(Path(os.path.join(snake_case   ,      'inf_time.csv'			)			).exists()			)
   self.assertTrue(Path(os.path.join(snake_case   ,      'inf_mem.csv'			)			).exists()			)
   self.assertTrue(Path(os.path.join(snake_case   ,      'env.csv'			)			).exists()			)


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

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					'sshleifer/tiny-gpt2'

  def _check_summary_is_not_empty(snake_case	:							Union[str, Any]			):
   self.assertTrue(hasattr(snake_case   ,      'sequential'			)			)
   self.assertTrue(hasattr(snake_case   ,      'cumulative'			)			)
   self.assertTrue(hasattr(snake_case   ,      'current'			)			)
   self.assertTrue(hasattr(snake_case   ,      'total'			)			)

  with tempfile.TemporaryDirectory() as tmp_dir:
   UpperCamelCase_					:						str =					TensorFlowBenchmarkArguments(
       models=[MODEL_ID]   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      log_filename=os.path.join(snake_case   ,      'log.txt'			)   ,      log_print=snake_case   ,      trace_memory_line_by_line=snake_case   ,      eager_mode=snake_case   ,      multi_process=snake_case   ,      )
   UpperCamelCase_					:						Tuple =					TensorFlowBenchmark(snake_case			)
   UpperCamelCase_					:						Union[str, Any] =					benchmark.run()
   _check_summary_is_not_empty(result.inference_summary			)
   self.assertTrue(Path(os.path.join(snake_case   ,      'log.txt'			)			).exists()			)



