
"""simple docstring"""







from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image


lowerCamelCase__   					=		["text", "image", "audio"]
def     __lowerCAmelCase				(_UpperCamelCase		):
	__lowerCAmelCase			:						Union[str, Any]									=      []

	for input_type in input_types:
		if input_type == "text":
			inputs.append('Text input'		)
		elif input_type == "image":
			inputs.append(
			    Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'		)		) / '000000039769.png'		).resize((512, 512)		)		)
		elif input_type == "audio":
			inputs.append(torch.ones(3000		)		)
		elif isinstance(_snake_case       ,  _snake_case		):
			inputs.append(create_inputs(_snake_case		)		)
		else:
			raise ValueError(F"Invalid type requested: {input_type}"		)

	return inputs
def     __lowerCAmelCase				(_UpperCamelCase		):
	__lowerCAmelCase			:						Union[str, Any]									=      []

	for output in outputs:
		if isinstance(_snake_case       ,  (str, AgentText)		):
			output_types.append('text'		)
		elif isinstance(_snake_case       ,  (Image.Image, AgentImage)		):
			output_types.append('image'		)
		elif isinstance(_snake_case       ,  (torch.Tensor, AgentAudio)		):
			output_types.append('audio'		)
		else:
			raise ValueError(F"Invalid output: {output}"		)

	return output_types




@is_tool_test
class  A__       :

		def 							__lowerCamelCase							(      self ):
			self.assertTrue(hasattr(self.tool       ,		'inputs' ) )
			self.assertTrue(hasattr(self.tool       ,		'outputs' ) )

			__lowerCAmelCase			:						Union[str, Any]									=      self.tool.inputs
			for _input in inputs:
				if isinstance(_input       ,		_a ):
					for __input in _input:
						self.assertTrue(__input in authorized_types )
				else:
					self.assertTrue(_input in authorized_types )

			__lowerCAmelCase			:						List[Any]									=      self.tool.outputs
			for _output in outputs:
				self.assertTrue(_output in authorized_types )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Union[str, Any]									=      create_inputs(self.tool.inputs )
			__lowerCAmelCase			:						List[str]									=      self.tool(*_a )

			# There is a single output
			if len(self.tool.outputs ) == 1:
				__lowerCAmelCase			:						Any									=      [outputs]

			self.assertListEqual(output_types(_a )       ,		self.tool.outputs )

		def 							__lowerCamelCase							(      self ):
			self.assertTrue(hasattr(self.tool       ,		'description' ) )
			self.assertTrue(hasattr(self.tool       ,		'default_checkpoint' ) )
			self.assertTrue(self.tool.description.startswith('This is a tool that' ) )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Optional[Any]									=      create_inputs(self.tool.inputs )
			__lowerCAmelCase			:						Dict									=      self.tool(*_a )

			if not isinstance(_a       ,		_a ):
				__lowerCAmelCase			:						List[Any]									=      [outputs]

			self.assertEqual(len(_a )       ,		len(self.tool.outputs ) )

			for output, output_type in zip(_a       ,		self.tool.outputs ):
				__lowerCAmelCase			:						str									=      AGENT_TYPE_MAPPING[output_type]
				self.assertTrue(isinstance(_a       ,		_a ) )







		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Tuple									=      create_inputs(self.tool.inputs )

			__lowerCAmelCase			:						Optional[Any]									=      []

			for _input, input_type in zip(_a       ,		self.tool.inputs ):
				if isinstance(_a       ,		_a ):
					_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
				else:
					_inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )

        # Should not raise an error
			__lowerCAmelCase			:						List[Any]									=      self.tool(*_a )

			if not isinstance(_a       ,		_a ):
				__lowerCAmelCase			:						str									=      [outputs]

			self.assertEqual(len(_a )       ,		len(self.tool.outputs ) )

from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame






def   lowerCAmelCase_						(					_snake_case      :  str = "laptop"   )				->		DataFrame:







	'''simple docstring'''


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