



"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE  :						Any    							=      logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      {'''vocab_file''': '''spiece.model'''}

__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      {
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    }
}

__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      {
    '''albert-base-v1''': 5_1_2,
    '''albert-large-v1''': 5_1_2,
    '''albert-xlarge-v1''': 5_1_2,
    '''albert-xxlarge-v1''': 5_1_2,
    '''albert-base-v2''': 5_1_2,
    '''albert-large-v2''': 5_1_2,
    '''albert-xlarge-v2''': 5_1_2,
    '''albert-xxlarge-v2''': 5_1_2,
}

__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      '''▁'''


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					lowercase__   :       List[str]										= VOCAB_FILES_NAMES
					lowercase__   :       List[Any]										= PRETRAINED_VOCAB_FILES_MAP
					lowercase__   :       Tuple										= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=False				,       lowerCamelCase__="[CLS]"				,       lowerCamelCase__="[SEP]"				,       lowerCamelCase__="<unk>"				,       lowerCamelCase__="[SEP]"				,       lowerCamelCase__="<pad>"				,       lowerCamelCase__="[CLS]"				,       lowerCamelCase__="[MASK]"				,       lowerCamelCase__ = None				,       **lowerCamelCase__				,       ):
									# Mask token behave like a normal word, i.e. include the space before it and
									# is included in the raw text, there should be a match in a non-normalized sentence.
									_lowerCamelCase        =				(
									    AddedToken(lowerCamelCase__				,       lstrip=lowerCamelCase__				,       rstrip=lowerCamelCase__				,       normalized=lowerCamelCase__   )
									    if isinstance(lowerCamelCase__				,       lowerCamelCase__   )
									    else mask_token
									)

									_lowerCamelCase        =				{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    do_lower_case=lowerCamelCase__				,       remove_space=lowerCamelCase__				,       keep_accents=lowerCamelCase__				,       bos_token=lowerCamelCase__				,       eos_token=lowerCamelCase__				,       unk_token=lowerCamelCase__				,       sep_token=lowerCamelCase__				,       pad_token=lowerCamelCase__				,       cls_token=lowerCamelCase__				,       mask_token=lowerCamelCase__				,       sp_model_kwargs=self.sp_model_kwargs				,       **lowerCamelCase__				,       )

									_lowerCamelCase        =				do_lower_case
									_lowerCamelCase        =				remove_space
									_lowerCamelCase        =				keep_accents
									_lowerCamelCase        =				vocab_file

									_lowerCamelCase        =				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
									self.sp_model.Load(lowerCamelCase__   )

					@property
					def 			snake_case__      (    self   ):
									return len(self.sp_model   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				{self.convert_ids_to_tokens(lowerCamelCase__   ): i for i in range(self.vocab_size   )}
									vocab.update(self.added_tokens_encoder   )
									return vocab

					def __getstate__(    self   ):
									_lowerCamelCase        =				self.__dict__.copy()
									_lowerCamelCase        =				None
									return state

					def __setstate__(    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				d

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

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

					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									if self.remove_space:
													_lowerCamelCase        =				''' '''.join(inputs.strip().split()   )
									else:
													_lowerCamelCase        =				inputs
									_lowerCamelCase        =				outputs.replace('''``'''				,       '''"'''   ).replace('''\'\''''				,       '''"'''   )

									if not self.keep_accents:
													_lowerCamelCase        =				unicodedata.normalize('''NFKD'''				,       lowerCamelCase__   )
													_lowerCamelCase        =				''''''.join([c for c in outputs if not unicodedata.combining(lowerCamelCase__   )]   )
									if self.do_lower_case:
													_lowerCamelCase        =				outputs.lower()

									return outputs

					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				self.preprocess_text(lowerCamelCase__   )
									_lowerCamelCase        =				self.sp_model.encode(lowerCamelCase__				,       out_type=lowerCamelCase__   )
									_lowerCamelCase        =				[]
									for piece in pieces:
													if len(lowerCamelCase__   ) > 1 and piece[-1] == str(''','''   ) and piece[-2].isdigit():
																	_lowerCamelCase        =				self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCamelCase__				,       ''''''   )   )
																	if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																					if len(cur_pieces[0]   ) == 1:
																									_lowerCamelCase        =				cur_pieces[1:]
																					else:
																									_lowerCamelCase        =				cur_pieces[0][1:]
																	cur_pieces.append(piece[-1]   )
																	new_pieces.extend(lowerCamelCase__   )
													else:
																	new_pieces.append(lowerCamelCase__   )

									return new_pieces

					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									return self.sp_model.PieceToId(lowerCamelCase__   )

					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									return self.sp_model.IdToPiece(lowerCamelCase__   )

					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				[]
									_lowerCamelCase        =				''''''
									_lowerCamelCase        =				False
									for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																	if not prev_is_special:
																					out_string += " "
																	out_string += self.sp_model.decode(lowerCamelCase__   ) + token
																	_lowerCamelCase        =				True
																	_lowerCamelCase        =				[]
													else:
																	current_sub_tokens.append(lowerCamelCase__   )
																	_lowerCamelCase        =				False
									out_string += self.sp_model.decode(lowerCamelCase__   )
									return out_string.strip()

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__ = None   ):
									_lowerCamelCase        =				[self.sep_token_id]
									_lowerCamelCase        =				[self.cls_token_id]
									if token_ids_a is None:
													return cls + token_ids_a + sep
									return cls + token_ids_a + sep + token_ids_a + sep

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__ = None				,       lowerCamelCase__ = False   ):

									if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=lowerCamelCase__				,       token_ids_a=lowerCamelCase__				,       already_has_special_tokens=lowerCamelCase__   )

									if token_ids_a is not None:
													return [1] + ([0] * len(lowerCamelCase__   )) + [1] + ([0] * len(lowerCamelCase__   )) + [1]
									return [1] + ([0] * len(lowerCamelCase__   )) + [1]

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__ = None   ):
									_lowerCamelCase        =				[self.sep_token_id]
									_lowerCamelCase        =				[self.cls_token_id]

									if token_ids_a is None:
													return len(cls + token_ids_a + sep   ) * [0]
									return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__ = None   ):
									if not os.path.isdir(lowerCamelCase__   ):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
													return
									_lowerCamelCase        =				os.path.join(
									    lowerCamelCase__				,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

									if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowerCamelCase__   ) and os.path.isfile(self.vocab_file   ):
													copyfile(self.vocab_file				,       lowerCamelCase__   )
									elif not os.path.isfile(self.vocab_file   ):
													with open(lowerCamelCase__				,       '''wb'''   ) as fi:
																	_lowerCamelCase        =				self.sp_model.serialized_model_proto()
																	fi.write(lowerCamelCase__   )

									return (out_vocab_file,)





"""simple docstring"""
from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame



def        lowerCAmelCase_(   lowercase_     :							str = "laptop"							)	->       DataFrame:
				_lowerCamelCase        =				F"""https://www.amazon.in/laptop/s?k={product}"""
				_lowerCamelCase        =				{
				    '''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''',
				}
				_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__":
			__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      '''headphones'''
			get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")
