

'''simple docstring'''



from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def 						__lowerCamelCase	(       __snake_case					:		str = "laptop"	)	->							List[Any]:






						"""simple docstring"""





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


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




'''simple docstring'''



import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	['image_processor', 'tokenizer']
			__snake_case        =	'FlavaImageProcessor'
			__snake_case        =	('BertTokenizer', 'BertTokenizerFast')


			def __init__(				self	:						Optional[int]		,      lowerCAmelCase_	:						List[str]=None		,      lowerCAmelCase_	:						Optional[Any]=None		,      **lowerCAmelCase_	:						Optional[Any]			)      ->		List[str]:





									'''simple docstring'''
									A__					:       Any     =None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
															    """ instead."""		,      lowerCAmelCase_		,      )
															A__					:       Optional[Any]     =kwargs.pop("""feature_extractor"""			)

									A__					:       List[Any]     =image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError("""You need to specify an `image_processor`."""			)
									if tokenizer is None:
															raise ValueError("""You need to specify a `tokenizer`."""			)

									super().__init__(lowerCAmelCase_		,      lowerCAmelCase_			)
									A__					:       int     =self.image_processor


			def __call__(				self	:						Union[str, Any]		,      lowerCAmelCase_	:						Optional[ImageInput] = None		,      lowerCAmelCase_	:						Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None		,      lowerCAmelCase_	:						bool = True		,      lowerCAmelCase_	:						Union[bool, str, PaddingStrategy] = False		,      lowerCAmelCase_	:						Union[bool, str, TruncationStrategy] = False		,      lowerCAmelCase_	:						Optional[int] = None		,      lowerCAmelCase_	:						int = 0		,      lowerCAmelCase_	:						Optional[int] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = True		,      lowerCAmelCase_	:						Optional[Union[str, TensorType]] = None		,      **lowerCAmelCase_	:						Optional[int]		,      )      ->		str:





									'''simple docstring'''

									if text is None and images is None:
															raise ValueError("""You have to specify either text or images. Both cannot be none."""			)

									if text is not None:
															A__					:       int     =self.tokenizer(
															    text=lowerCAmelCase_		,      add_special_tokens=lowerCAmelCase_		,      padding=lowerCAmelCase_		,      truncation=lowerCAmelCase_		,      max_length=lowerCAmelCase_		,      stride=lowerCAmelCase_		,      pad_to_multiple_of=lowerCAmelCase_		,      return_token_type_ids=lowerCAmelCase_		,      return_attention_mask=lowerCAmelCase_		,      return_overflowing_tokens=lowerCAmelCase_		,      return_special_tokens_mask=lowerCAmelCase_		,      return_offsets_mapping=lowerCAmelCase_		,      return_length=lowerCAmelCase_		,      verbose=lowerCAmelCase_		,      return_tensors=lowerCAmelCase_		,      **lowerCAmelCase_		,      )
									if images is not None:
															A__					:       List[str]     =self.image_processor(
															    lowerCAmelCase_		,      return_image_mask=lowerCAmelCase_		,      return_codebook_pixels=lowerCAmelCase_		,      return_tensors=lowerCAmelCase_		,      **lowerCAmelCase_		,      )

									if text is not None and images is not None:
															encoding.update(lowerCAmelCase_			)
															return encoding
									elif text is not None:
															return encoding
									else:
															return BatchEncoding(data=dict(**lowerCAmelCase_			)		,      tensor_type=lowerCAmelCase_			)


			def lowercase__						(				self	:						Any		,      *lowerCAmelCase_	:						str		,      **lowerCAmelCase_	:						Optional[int]			)      ->		str:





									'''simple docstring'''
									return self.tokenizer.batch_decode(*lowerCAmelCase_		,      **lowerCAmelCase_			)


			def lowercase__						(				self	:						List[Any]		,      *lowerCAmelCase_	:						List[Any]		,      **lowerCAmelCase_	:						Any			)      ->		Tuple:





									'''simple docstring'''
									return self.tokenizer.decode(*lowerCAmelCase_		,      **lowerCAmelCase_			)


			@property
			def lowercase__						(				self	:						Tuple			)      ->		Tuple:





									'''simple docstring'''
									A__					:       Any     =self.tokenizer.model_input_names
									A__					:       Optional[Any]     =self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


			@property
			def lowercase__						(				self	:						Tuple			)      ->		int:





									'''simple docstring'''
									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""		,      lowerCAmelCase_		,      )
									return self.image_processor_class



			@property
			def lowercase__						(				self	:						Tuple			)      ->		Dict:





									'''simple docstring'''
									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""		,      lowerCAmelCase_		,      )
									return self.image_processor

