
'''simple docstring'''






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame





def    UpperCAmelCase							(  UpperCAmelCase__							:							str = "laptop"):
				lowerCamelCase					:			Tuple									= F'''https://www.amazon.in/laptop/s?k={product}'''
				lowerCamelCase					:			str									= {
				    '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					:			int									= BeautifulSoup(requests.get(UpperCAmelCase__       ,							headers=UpperCAmelCase__).text)
				# Initialize a Pandas dataframe with the column titles
				lowerCamelCase					:			Tuple									= 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					:			Union[str, Any]									= item.ha.text
												lowerCamelCase					:			Tuple									= 'https://www.amazon.in/' + item.ha.a['href']
												lowerCamelCase					:			Optional[int]									= item.find('span'       ,							attrs={'class': 'a-offscreen'}).text
												try:
																lowerCamelCase					:			Tuple									= item.find('span'       ,							attrs={'class': 'a-icon-alt'}).text
												except AttributeError:
																lowerCamelCase					:			List[Any]									= 'Not available'
												try:
																lowerCamelCase					:			List[Any]									= (
																    '₹'
																    + item.find(
																        'span'       ,							attrs={'class': 'a-price a-text-price'}).text.split('₹')[1]
																)
												except AttributeError:
																lowerCamelCase					:			List[Any]									= ''
												try:
																lowerCamelCase					:			Any									= float(
																    (
																        (
																            float(product_mrp.strip('₹').replace(','       ,							''))
																            - float(product_price.strip('₹').replace(','       ,							''))
																        )
																        / float(product_mrp.strip('₹').replace(','       ,							''))
																    )
																    * 1_00)
												except ValueError:
																lowerCamelCase					:			str									= float('nan')
								except AttributeError:
												pass
								lowerCamelCase					:			Dict									= [
								    product_title,
								    product_link,
								    product_price,
								    product_rating,
								    product_mrp,
								    discount,
								]
				lowerCamelCase					:			List[Any]									= ' '
				lowerCamelCase					:			List[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 os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A			      =      logging.get_logger(__name__)


A			      =      '▁'

A			      =      {'vocab_file': 'spiece.model'}

A			      =      {
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    }
}

A			      =      {
    'google/reformer-crime-and-punishment': 52_4288,
}




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	VOCAB_FILES_NAMES
					_lowerCAmelCase			     =	PRETRAINED_VOCAB_FILES_MAP
					_lowerCAmelCase			     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_lowerCAmelCase			     =	['''input_ids''', '''attention_mask''']
					def __init__(     self,    A,    A="</s>",    A="<unk>",    A=[],    A = None,    **A,    ):

									"""simple docstring"""


									lowerCamelCase					:			str									= {} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    eos_token=A,    unk_token=A,    additional_special_tokens=A,    sp_model_kwargs=self.sp_model_kwargs,    **A,    )

									lowerCamelCase					:			Any									= vocab_file
									lowerCamelCase					:			List[Any]									= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
									self.sp_model.Load(A      )
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return self.sp_model.get_piece_size()
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= {self.convert_ids_to_tokens(A      ): i for i in range(self.vocab_size      )}
									vocab.update(self.added_tokens_encoder      )
									return vocab
					def __getstate__(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.__dict__.copy()
									lowerCamelCase					:			List[Any]									= None
									return state
					def __setstate__(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= d

									# for backward compatibility
									if not hasattr(self,    'sp_model_kwargs'      ):
													lowerCamelCase					:			List[str]									= {}

									lowerCamelCase					:			Optional[int]									= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
									self.sp_model.Load(self.vocab_file      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									return self.sp_model.encode(A,    out_type=A      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									return self.sp_model.piece_to_id(A      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									if index < self.sp_model.get_piece_size():
													lowerCamelCase					:			Union[str, Any]									= self.sp_model.IdToPiece(A      )
									return token
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= []
									lowerCamelCase					:			Optional[int]									= ''
									for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																	out_string += self.sp_model.decode(A      ) + token
																	lowerCamelCase					:			Any									= []
													else:
																	current_sub_tokens.append(A      )
									out_string += self.sp_model.decode(A      )
									return out_string.strip()




					def     UpperCAmelCase_							(     self,    A,    A = None      ):

									"""simple docstring"""


									if not os.path.isdir(A      ):
													logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
													return
									lowerCamelCase					:			str									= os.path.join(
									    A,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

									if os.path.abspath(self.vocab_file      ) != os.path.abspath(A      ) and os.path.isfile(self.vocab_file      ):
													copyfile(self.vocab_file,    A      )
									elif not os.path.isfile(self.vocab_file      ):
													with open(A,    'wb'      ) as fi:
																	lowerCamelCase					:			Optional[Any]									= self.sp_model.serialized_model_proto()
																	fi.write(A      )

									return (out_vocab_file,)
