






"""simple docstring"""






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame



def  __lowerCamelCase					(					lowerCAmelCase__ = "laptop"				):
				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(lowerCAmelCase__       ,headers=lowerCAmelCase__				).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__":
					SCREAMING_SNAKE_CASE						:						Union[str, Any]					   =       '''headphones'''
					get_amazon_product_data(product).to_csv(f'''Amazon Product Data for {product}.csv''')










"""simple docstring"""






import os

import numpy
import onnx



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       a.name
				A__							 =       b.name

				A__							 =       ''
				A__							 =       ''

				A__							 =       a == b

				A__							 =       name_a
				A__							 =       name_b

				return res



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				for i, input_name in enumerate(node_proto.input				):
								if input_name == name:
												node_proto.input.insert(lowerCAmelCase__       ,lowerCAmelCase__				)
												node_proto.input.pop(i + 1				)

				if node_proto.op_type == "If":
								_graph_replace_input_with(node_proto.attribute[0].g       ,lowerCAmelCase__       ,lowerCAmelCase__				)
								_graph_replace_input_with(node_proto.attribute[1].g       ,lowerCAmelCase__       ,lowerCAmelCase__				)
				if node_proto.op_type == "Loop":
								_graph_replace_input_with(node_proto.attribute[0].g       ,lowerCAmelCase__       ,lowerCAmelCase__				)



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				for n in graph_proto.node:
								_node_replace_input_with(lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				)



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       list(model.graph.initializer				)
				A__							 =       list(model_without_ext.graph.initializer				)
				for i, ref_i in ind_to_replace:
								assert inits_with_data[i].name == inits[i].name
								assert inits_with_data[ref_i].name == inits[ref_i].name
								assert i > ref_i

								A__							 =       inits[i].name
								A__							 =       inits[ref_i].name

								model_without_ext.graph.initializer.remove(inits[i]				)

								# for n in model.graph.node:
								_graph_replace_input_with(model_without_ext.graph       ,lowerCAmelCase__       ,lowerCAmelCase__				)







def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__							 =       os.path.dirname(lowerCAmelCase__				)
				A__							 =       os.path.basename(lowerCAmelCase__				)

				A__							 =       onnx.load(os.path.join(lowerCAmelCase__       ,lowerCAmelCase__				)				)

				A__							 =       list(model.graph.initializer				)

				A__							 =       set()
				A__							 =       {}
				A__							 =       []

				A__							 =       0

				for i in range(len(lowerCAmelCase__				)				):
								if i in dup_set:
												continue

								for j in range(i + 1       ,len(lowerCAmelCase__				)				):
												if j in dup_set:
																continue
												if _is_equal_tensor_proto(inits[i]       ,inits[j]				):
																dup_set.add(lowerCAmelCase__				)
																dup_set.add(lowerCAmelCase__				)

																A__							 =       inits[j].data_type
																A__							 =       numpy.prod(inits[j].dims				)
																if dtype == 1:
																				mem_size *= 4
																elif dtype == 6:
																				mem_size *= 4
																elif dtype == 7 or dtype == 11:
																				mem_size *= 8
																else:
																				print('unexpected data type: '       ,lowerCAmelCase__				)
																total_reduced_size += mem_size

																A__							 =       inits[i].name
																A__							 =       inits[j].name

																if name_i in dup_map:
																				dup_map[name_i].append(lowerCAmelCase__				)
																else:
																				A__							 =       [name_j]
																ind_to_replace.append((j, i)				)

				print('total reduced size: '       ,total_reduced_size / 1024 / 1024 / 1024       ,'GB'				)

				A__							 =       sorted(lowerCAmelCase__				)
				_remove_dup_initializers_from_model(lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				)

				A__							 =       'optimized_' + model_file_name
				A__							 =       os.path.join(lowerCAmelCase__       ,lowerCAmelCase__				)
				onnx.save(lowerCAmelCase__       ,lowerCAmelCase__				)

				return new_model


