
from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame


def 		UpperCAmelCase     (      a_ = "laptop"		)						->      DataFrame:







				"""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\n        (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(","						,  ""		)		)
																    )
																    * 1_0_0		)
												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__":
					SCREAMING_SNAKE_CASE				:List[Any]									=							'headphones'
					get_amazon_product_data(product).to_csv(f'''Amazon Product Data for {product}.csv''')








import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase__       		=  50_0000

lowerCamelCase__		,      lowerCamelCase__       		=  os.path.split(__file__)
lowerCamelCase__       		=  os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))






@get_duration
def 	UpperCamelCase	(							snake_case__  : datasets.Dataset						,**snake_case__  : Any	):




     '''simple docstring'''






     __snake_case      :List[str] 							=    dataset.map(**snake_case__	)






@get_duration
def 	UpperCamelCase	(							snake_case__  : datasets.Dataset						,**snake_case__  : Dict	):




     '''simple docstring'''






     __snake_case      :int 							=    dataset.filter(**snake_case__	)






def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :str 							=    {"""num examples""": SPEED_TEST_N_EXAMPLES}
     with tempfile.TemporaryDirectory() as tmp_dir:
          __snake_case      :Dict 							=    datasets.Features({"""text""": datasets.Value("""string"""	), """numbers""": datasets.Value("""float32"""	)}	)
          __snake_case      :Optional[int] 							=    generate_example_dataset(
              os.path.join(snake_case__						,"""dataset.arrow"""	)						,snake_case__						,num_examples=snake_case__	)

          __snake_case      :str 							=    transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""						,use_fast=snake_case__	)

          def tokenize(snake_case__  : Optional[int]	):
               return tokenizer(examples["""text"""]	)

          __snake_case      :Optional[Any] 							=    map(snake_case__	)

          __snake_case      :Optional[Any] 							=    map(snake_case__						,batched=snake_case__	)

          __snake_case      :Optional[Any] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""numpy"""	):
               __snake_case      :int 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""pandas"""	):
               __snake_case      :List[Any] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""torch"""						,columns="""numbers"""	):
               __snake_case      :str 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""tensorflow"""						,columns="""numbers"""	):
               __snake_case      :Optional[int] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          __snake_case      :Dict 							=    map(snake_case__						,function=snake_case__						,batched=snake_case__	)

          __snake_case      :Tuple 							=    filter(snake_case__	)

          # Activate later when tokenizer support batched inputs
          # with dataset.formatted_as(type='numpy'):
          #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

     with open(snake_case__						,"""wb"""	) as f:
          f.write(json.dumps(snake_case__	).encode("""utf-8"""	)	)


if __name__ == "__main__":  # useful to run the profiler
     benchmark_map_filter()


