



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


UpperCAmelCase							 =					logging.get_logger(__name__)

UpperCAmelCase							 =					{"""vocab_file""": """spiece.model"""}

UpperCAmelCase							 =					{
    """vocab_file""": {
        """bert_for_seq_generation""": (
            """https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
        ),
    }
}

UpperCAmelCase							 =					{"""bert_for_seq_generation""": 512}

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      Optional[Any]         =					VOCAB_FILES_NAMES
							UpperCamelCase__				:      Any         =					PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase__				:      List[str]         =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase__				:      List[int]         =					[]
							UpperCamelCase__				:      Optional[int]         =					["""input_ids""", """attention_mask"""]


							def __init__(							self  ,  a_  ,  a_="<s>"  ,  a_="</s>"  ,  a_="<unk>"  ,  a_="<pad>"  ,  a_="<::::>"  ,  a_ = None  ,  **a_  ,  ):
											a__				      =		{} if sp_model_kwargs is None else sp_model_kwargs

											# Add extra_ids to the special token list
											super().__init__(
											    bos_token=a_  ,  eos_token=a_  ,  unk_token=a_  ,  pad_token=a_  ,  sep_token=a_  ,  sp_model_kwargs=self.sp_model_kwargs  ,  **a_  ,  )

											a__				      =		vocab_file

											a__				      =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(a_		)


							@property
							def _a      (							self		):
											return self.sp_model.get_piece_size()


							def _a      (							self		):
											a__				      =		{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		):
											a__				      =		self.__dict__.copy()
											a__				      =		None
											return state


							def __setstate__(							self  ,  a_		):
											a__				      =		d

											# for backward compatibility
											if not hasattr(self  ,  """sp_model_kwargs"""		):
															a__				      =		{}

											a__				      =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(self.vocab_file		)


							def _a      (							self  ,  a_		):
											return self.sp_model.encode(a_  ,  out_type=a_		)


							def _a      (							self  ,  a_		):
											return self.sp_model.piece_to_id(a_		)


							def _a      (							self  ,  a_		):
											a__				      =		self.sp_model.IdToPiece(a_		)
											return token


							def _a      (							self  ,  a_		):
											a__				      =		[]
											a__				      =		""""""
											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
																			a__				      =		[]
															else:
																			current_sub_tokens.append(a_		)
											out_string += self.sp_model.decode(a_		)
											return out_string.strip()







							def _a      (							self  ,  a_  ,  a_ = None		):
											if not os.path.isdir(a_		):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
															return
											a__				      =		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:
																			a__				      =		self.sp_model.serialized_model_proto()
																			fi.write(a_		)

											return (out_vocab_file,)






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame

def 			A_      (	__a     :				str = "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__":
	UpperCAmelCase							 =					"""headphones"""
	get_amazon_product_data(product).to_csv(f"""Amazon Product Data for {product}.csv""")

