



'''simple docstring'''

from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame






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







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


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




'''simple docstring'''

import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
				import torch

				from transformers.generation import (
				    MaxLengthCriteria,
				    MaxNewTokensCriteria,
				    MaxTimeCriteria,
				    StoppingCriteriaList,
				    validate_stopping_criteria,
				)

@require_torch
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						def     __snake_case   ( self     ,	A_				)     ->     str:
							lowerCAmelCase   =     3
							lowerCAmelCase   =     250

							lowerCAmelCase   =     ids_tensor((batch_size, length)     ,	A_				)
							lowerCAmelCase   =     torch.ones((batch_size, length)     ,	device=A_     ,	dtype=torch.float				) / length
							return input_ids, scores


						def     __snake_case   ( self				)     ->     Any:
							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(5				)

							lowerCAmelCase   =     StoppingCriteriaList(
							    [
							        MaxLengthCriteria(max_length=10				),
							        MaxTimeCriteria(max_time=0.1				),
							    ]				)

							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(9				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(10				)
							self.assertTrue(criteria(A_     ,	A_				)				)


						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     MaxLengthCriteria(max_length=10				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(5				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(9				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(10				)
							self.assertTrue(criteria(A_     ,	A_				)				)


						def     __snake_case   ( self				)     ->     List[Any]:
							lowerCAmelCase   =     MaxNewTokensCriteria(start_length=5     ,	max_new_tokens=5				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(5				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(9				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(10				)
							self.assertTrue(criteria(A_     ,	A_				)				)

							lowerCAmelCase   =     StoppingCriteriaList([criteria]				)
							self.assertEqual(criteria_list.max_length     ,	10				)


						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(5				)

							lowerCAmelCase   =     MaxTimeCriteria(max_time=0.1				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase   =     MaxTimeCriteria(max_time=0.1     ,	initial_timestamp=time.time() - 0.2				)
							self.assertTrue(criteria(A_     ,	A_				)				)




						def     __snake_case   ( self				)     ->     Optional[int]:
							validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)     ,	10				)

							with self.assertWarns(A_				):
								validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)     ,	11				)

							lowerCAmelCase   =     validate_stopping_criteria(StoppingCriteriaList()     ,	11				)

							self.assertEqual(len(A_				)     ,	1				)