



"""simple docstring"""






import json
import os
from typing import Dict, List, Optional, Tuple

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


__snake_case							:   Optional[int]      =    logging.get_logger(__name__)


__snake_case							:   Tuple      =    {
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

__snake_case							:   Optional[Any]      =    {
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

__snake_case							:   Union[str, Any]      =    '</w>'
__snake_case							:   List[str]      =    '@@ '


def 			_lowercase				(  __snake_case	)						->   Tuple:
			__lowerCAmelCase      :  str												=       set()
			__lowerCAmelCase      :  List[str]												=       word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)	)
						__lowerCAmelCase      :  Any												=       char
			return pairs


# Speech2Text2 has no max input length
__snake_case							:   Optional[int]      =    {'facebook/s2t-wav2vec2-large-en-de': 1_024}





class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE          =	PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE          =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE          =	['input_ids', 'attention_mask']

	def __init__(     self:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		List[Any]="<s>"       ,			_SCREAMING_SNAKE_CASE:		Dict="<pad>"       ,			_SCREAMING_SNAKE_CASE:		List[str]="</s>"       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]="<unk>"       ,			_SCREAMING_SNAKE_CASE:		List[str]=False       ,			_SCREAMING_SNAKE_CASE:		List[str]=None       ,			**_SCREAMING_SNAKE_CASE:		Union[str, Any]       ,			)				->       str:

				"""simple docstring"""

				super().__init__(
				    unk_token=_SCREAMING_SNAKE_CASE       ,			bos_token=_SCREAMING_SNAKE_CASE       ,			eos_token=_SCREAMING_SNAKE_CASE       ,			pad_token=_SCREAMING_SNAKE_CASE       ,			do_lower_case=_SCREAMING_SNAKE_CASE       ,			**_SCREAMING_SNAKE_CASE       ,			)

				__lowerCAmelCase      :  Dict												=       do_lower_case

				with open(_SCREAMING_SNAKE_CASE       ,			encoding="utf-8") as vocab_handle:
							__lowerCAmelCase      :  Optional[Any]												=       json.load(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  str												=       {v: k for k, v in self.encoder.items()}

				if merges_file is None:
							logger.info(F"""No merges files provided. {self.__class__.__name__} can only be used for decoding.""")

							__lowerCAmelCase      :  Dict												=       None
							__lowerCAmelCase      :  List[Any]												=       None
				else:
							with open(_SCREAMING_SNAKE_CASE       ,			encoding="utf-8") as merges_handle:
										__lowerCAmelCase      :  Optional[Any]												=       merges_handle.read().split("\n")[:-1]

							__lowerCAmelCase      :  Tuple												=       [tuple(merge.split()[:2]) for merge in merges]
							__lowerCAmelCase      :  str												=       dict(zip(_SCREAMING_SNAKE_CASE       ,			range(len(_SCREAMING_SNAKE_CASE))))
							__lowerCAmelCase      :  int												=       {}

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       int:

				"""simple docstring"""

				return len(self.decoder)

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

				"""simple docstring"""

				return dict(self.encoder       ,			**self.added_tokens_encoder)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict       ,			_SCREAMING_SNAKE_CASE:		Optional[int])				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  List[Any]												=       tuple(token[:-1]) + (token[-1] + BPE_TOKEN_MERGES,)
				if token in self.cache:
							return self.cache[token]
				__lowerCAmelCase      :  Union[str, Any]												=       get_pairs(_SCREAMING_SNAKE_CASE)

				if not pairs:
							return token

				while True:
							__lowerCAmelCase      :  Optional[Any]												=       min(_SCREAMING_SNAKE_CASE       ,			key=lambda _SCREAMING_SNAKE_CASE: self.bpe_ranks.get(_SCREAMING_SNAKE_CASE       ,			float("inf")))
							if bigram not in self.bpe_ranks:
										break
							__lowerCAmelCase						,	__lowerCAmelCase      :  Dict												=       bigram
							__lowerCAmelCase      :  Optional[Any]												=       []
							__lowerCAmelCase      :  List[Any]												=       0
							while i < len(_SCREAMING_SNAKE_CASE):
										try:
													__lowerCAmelCase      :  Dict												=       word.index(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)
										except ValueError:
													new_word.extend(word[i:])
													break
										else:
													new_word.extend(word[i:j])
													__lowerCAmelCase      :  Optional[Any]												=       j

										if word[i] == first and i < len(_SCREAMING_SNAKE_CASE) - 1 and word[i + 1] == second:
													new_word.append(first + second)
													i += 2
										else:
													new_word.append(word[i])
													i += 1
							__lowerCAmelCase      :  Optional[int]												=       tuple(_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase      :  Optional[Any]												=       new_word
							if len(_SCREAMING_SNAKE_CASE) == 1:
										break
							else:
										__lowerCAmelCase      :  Tuple												=       get_pairs(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  List[Any]												=       " ".join(_SCREAMING_SNAKE_CASE)
				if word == "\n  " + BPE_TOKEN_MERGES:
							__lowerCAmelCase      :  List[Any]												=       "\n" + BPE_TOKEN_MERGES

				if word.endswith(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase      :  Tuple												=       word.replace(_SCREAMING_SNAKE_CASE       ,			"")

				__lowerCAmelCase      :  Optional[int]												=       word.replace(" "       ,			_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Union[str, Any]												=       word
				return word

	def 							_SCREAMING_SNAKE_CASE   (     self:		str       ,			_SCREAMING_SNAKE_CASE:		int)				->       Dict:

				"""simple docstring"""

				if self.bpe_ranks is None:
							raise ValueError(
							    "This tokenizer was instantiated without a `merges.txt` file, so"
							    " that it can only be used for decoding, not for encoding."
							    "Make sure to provide `merges.txt` file at instantiation to enable "
							    "encoding.")

				if self.do_lower_case:
							__lowerCAmelCase      :  List[str]												=       text.lower()

				__lowerCAmelCase      :  str												=       text.split()

				__lowerCAmelCase      :  Any												=       []
				for token in text:
							if token:
										split_tokens.extend(list(self.bpe(_SCREAMING_SNAKE_CASE).split(" ")))

				return split_tokens

	def 							_SCREAMING_SNAKE_CASE   (     self:		str       ,			_SCREAMING_SNAKE_CASE:		str)				->       int:

				"""simple docstring"""

				return self.encoder.get(_SCREAMING_SNAKE_CASE       ,			self.encoder.get(self.unk_token))

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		int)				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       self.decoder.get(_SCREAMING_SNAKE_CASE       ,			self.unk_token)
				return result

	def 							_SCREAMING_SNAKE_CASE   (     self:		str       ,			_SCREAMING_SNAKE_CASE:		List[str])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       " ".join(_SCREAMING_SNAKE_CASE)

				# make sure @@ tokens are concatenated
				__lowerCAmelCase      :  Optional[int]												=       "".join(string.split(_SCREAMING_SNAKE_CASE))

				return string







	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str]       ,			_SCREAMING_SNAKE_CASE:		str       ,			_SCREAMING_SNAKE_CASE:		Optional[str] = None)				->       Tuple[str]:

				"""simple docstring"""

				if not os.path.isdir(_SCREAMING_SNAKE_CASE):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
							return
				__lowerCAmelCase      :  List[str]												=       os.path.join(
				    _SCREAMING_SNAKE_CASE       ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
				__lowerCAmelCase      :  Optional[Any]												=       os.path.join(
				    _SCREAMING_SNAKE_CASE       ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"])

				with open(_SCREAMING_SNAKE_CASE       ,			"w"       ,			encoding="utf-8") as f:
							f.write(json.dumps(self.encoder       ,			indent=2       ,			sort_keys=_SCREAMING_SNAKE_CASE       ,			ensure_ascii=_SCREAMING_SNAKE_CASE) + "\n")

				__lowerCAmelCase      :  str												=       0
				if self.bpe_ranks is None:
							return (vocab_file,)

				with open(_SCREAMING_SNAKE_CASE       ,			"w"       ,			encoding="utf-8") as writer:
							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,			key=lambda _SCREAMING_SNAKE_CASE: kv[1]):
										if index != token_index:
													logger.warning(
													    F"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
													    " Please check that the tokenizer is not corrupted!")
													__lowerCAmelCase      :  Union[str, Any]												=       token_index
										writer.write(" ".join(_SCREAMING_SNAKE_CASE) + "\n")
										index += 1

				return (vocab_file, merges_file)




"""simple docstring"""






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame


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


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