
"""simple docstring"""







import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin




class  A__       (	_lowerCamelCase):
		# to overwrite at feature extractactor specific tests
		A_						:       int				   =	None
		A_						:       int				   =	None

		@property
		def 							__lowerCamelCase							(      self ):
			return self.feat_extract_tester.prepare_feat_extract_dict()

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						str									=      self.feature_extraction_class(**self.feat_extract_dict )
			self.assertTrue(hasattr(_a       ,		'feature_size' ) )
			self.assertTrue(hasattr(_a       ,		'sampling_rate' ) )
			self.assertTrue(hasattr(_a       ,		'padding_value' ) )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Dict									=      self.feat_extract_tester.prepare_inputs_for_common()
			__lowerCAmelCase			:						Dict									=      self.feature_extraction_class(**self.feat_extract_dict )
			__lowerCAmelCase			:						List[Any]									=      feat_extract.model_input_names[0]

			__lowerCAmelCase			:						Dict									=      BatchFeature({input_name: speech_inputs} )

			self.assertTrue(all(len(_a ) == len(_a ) for x, y in zip(_a       ,		processed_features[input_name] ) ) )

			__lowerCAmelCase			:						int									=      self.feat_extract_tester.prepare_inputs_for_common(equal_length=_a )
			__lowerCAmelCase			:						str									=      BatchFeature({input_name: speech_inputs}       ,		tensor_type='np' )

			__lowerCAmelCase			:						int									=      processed_features[input_name]

			if len(batch_features_input.shape ) < 3:
				__lowerCAmelCase			:						List[str]									=      batch_features_input[:, :, None]

			self.assertTrue(
			    batch_features_input.shape
			    == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )

		@require_torch
		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						List[str]									=      self.feat_extract_tester.prepare_inputs_for_common(equal_length=_a )
			__lowerCAmelCase			:						str									=      self.feature_extraction_class(**self.feat_extract_dict )
			__lowerCAmelCase			:						Tuple									=      feat_extract.model_input_names[0]

			__lowerCAmelCase			:						Optional[int]									=      BatchFeature({input_name: speech_inputs}       ,		tensor_type='pt' )

			__lowerCAmelCase			:						str									=      processed_features[input_name]

			if len(batch_features_input.shape ) < 3:
				__lowerCAmelCase			:						Optional[int]									=      batch_features_input[:, :, None]

			self.assertTrue(
			    batch_features_input.shape
			    == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )

		@require_tf
		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Tuple									=      self.feat_extract_tester.prepare_inputs_for_common(equal_length=_a )
			__lowerCAmelCase			:						Dict									=      self.feature_extraction_class(**self.feat_extract_dict )
			__lowerCAmelCase			:						Dict									=      feat_extract.model_input_names[0]

			__lowerCAmelCase			:						Optional[int]									=      BatchFeature({input_name: speech_inputs}       ,		tensor_type='tf' )

			__lowerCAmelCase			:						Union[str, Any]									=      processed_features[input_name]

			if len(batch_features_input.shape ) < 3:
				__lowerCAmelCase			:						Union[str, Any]									=      batch_features_input[:, :, None]

			self.assertTrue(
			    batch_features_input.shape
			    == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE=False ):
			def _inputs_have_equal_length(_SCREAMING_SNAKE_CASE ):
				__lowerCAmelCase			:						Tuple									=      len(input[0] )
				for input_slice in input[1:]:
					if len(_a ) != length:
						return False
				return True

			def _inputs_are_equal(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
				if len(_a ) != len(_a ):
					return False

				for input_slice_a, input_slice_a in zip(_a       ,		_a ):
					if not np.allclose(np.asarray(_a )       ,		np.asarray(_a )       ,		atol=1E-3 ):
						return False
				return True

			__lowerCAmelCase			:						Dict									=      self.feature_extraction_class(**self.feat_extract_dict )
			__lowerCAmelCase			:						List[Any]									=      self.feat_extract_tester.prepare_inputs_for_common(numpify=_a )
			__lowerCAmelCase			:						List[Any]									=      feat_extract.model_input_names[0]

			__lowerCAmelCase			:						Union[str, Any]									=      BatchFeature({input_name: speech_inputs} )

			__lowerCAmelCase			:						Tuple									=      self.feat_extract_tester.seq_length_diff
			__lowerCAmelCase			:						List[Any]									=      self.feat_extract_tester.max_seq_length + pad_diff
			__lowerCAmelCase			:						Any									=      self.feat_extract_tester.min_seq_length
			__lowerCAmelCase			:						Dict									=      self.feat_extract_tester.batch_size
			__lowerCAmelCase			:						Optional[Any]									=      self.feat_extract_tester.feature_size

			# test padding for List[int] + numpy
			__lowerCAmelCase			:						int									=      feat_extract.pad(_a       ,		padding=_a )
			__lowerCAmelCase			:						int									=      input_a[input_name]

			__lowerCAmelCase			:						int									=      feat_extract.pad(_a       ,		padding='longest' )
			__lowerCAmelCase			:						int									=      input_a[input_name]

			__lowerCAmelCase			:						List[Any]									=      feat_extract.pad(_a       ,		padding='max_length'       ,		max_length=len(speech_inputs[-1] ) )
			__lowerCAmelCase			:						List[Any]									=      input_a[input_name]

			__lowerCAmelCase			:						str									=      feat_extract.pad(_a       ,		padding='longest'       ,		return_tensors='np' )
			__lowerCAmelCase			:						Any									=      input_a[input_name]

			# max_length parameter has to be provided when setting `padding="max_length"`
			with self.assertRaises(_a ):
				feat_extract.pad(_a       ,		padding='max_length' )[input_name]

			__lowerCAmelCase			:						List[Any]									=      feat_extract.pad(
			    _a       ,		padding='max_length'       ,		max_length=_a       ,		return_tensors='np' )
			__lowerCAmelCase			:						Optional[int]									=      input_a[input_name]

			self.assertFalse(_inputs_have_equal_length(_a ) )
			self.assertTrue(_inputs_have_equal_length(_a ) )
			self.assertTrue(_inputs_have_equal_length(_a ) )
			self.assertTrue(_inputs_are_equal(_a       ,		_a ) )
			self.assertTrue(len(input_a[0] ) == pad_min_length )
			self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
			self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
			self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )

			if feature_size > 1:
				self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )

			# test padding for `pad_to_multiple_of` for List[int] + numpy
			__lowerCAmelCase			:						str									=      feat_extract.pad(_a       ,		pad_to_multiple_of=10 )
			__lowerCAmelCase			:						int									=      input_a[input_name]

			__lowerCAmelCase			:						Union[str, Any]									=      feat_extract.pad(_a       ,		padding='longest'       ,		pad_to_multiple_of=10 )
			__lowerCAmelCase			:						Dict									=      input_a[input_name]

			__lowerCAmelCase			:						List[Any]									=      feat_extract.pad(
			    _a       ,		padding='max_length'       ,		pad_to_multiple_of=10       ,		max_length=_a )
			__lowerCAmelCase			:						int									=      input_a[input_name]

			__lowerCAmelCase			:						Optional[Any]									=      feat_extract.pad(
			    _a       ,		padding='max_length'       ,		pad_to_multiple_of=10       ,		max_length=_a       ,		return_tensors='np'       ,		)
			__lowerCAmelCase			:						str									=      input_a[input_name]

			self.assertTrue(all(len(_a ) % 10 == 0 for x in input_a ) )
			self.assertTrue(_inputs_are_equal(_a       ,		_a ) )

			__lowerCAmelCase			:						List[str]									=      pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
			self.assertTrue(all(len(_a ) == expected_mult_pad_length for x in input_a ) )
			self.assertEqual(input_a.shape[:2]       ,		(batch_size, expected_mult_pad_length) )

			if feature_size > 1:
				self.assertTrue(input_a.shape[2] == feature_size )

			# Check padding value is correct
			__lowerCAmelCase			:						Optional[int]									=      (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
			self.assertTrue(
			    abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
			    < 1E-3 )
			self.assertTrue(
			    abs(
			        np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
			        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
			    < 1E-3 )
			self.assertTrue(
			    abs(
			        np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
			        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
			    < 1E-3 )
			self.assertTrue(
			    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1E-3 )
			self.assertTrue(
			    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
			    < 1E-3 )

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE=False ):
			def _inputs_have_equal_length(_SCREAMING_SNAKE_CASE ):
				__lowerCAmelCase			:						Optional[Any]									=      len(input[0] )
				for input_slice in input[1:]:
					if len(_a ) != length:
						return False
				return True

			def _inputs_are_equal(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
				if len(_a ) != len(_a ):
					return False

				for input_slice_a, input_slice_a in zip(_a       ,		_a ):
					if not np.allclose(np.asarray(_a )       ,		np.asarray(_a )       ,		atol=1E-3 ):
						return False
				return True

			__lowerCAmelCase			:						List[str]									=      self.feature_extraction_class(**self.feat_extract_dict )
			__lowerCAmelCase			:						Any									=      self.feat_extract_tester.prepare_inputs_for_common(numpify=_a )
			__lowerCAmelCase			:						Any									=      feat_extract.model_input_names[0]

			__lowerCAmelCase			:						Union[str, Any]									=      BatchFeature({input_name: speech_inputs} )

			# truncate to smallest
			__lowerCAmelCase			:						Tuple									=      feat_extract.pad(
			    _a       ,		padding='max_length'       ,		max_length=len(speech_inputs[0] )       ,		truncation=_a )
			__lowerCAmelCase			:						Union[str, Any]									=      input_a[input_name]

			__lowerCAmelCase			:						List[Any]									=      feat_extract.pad(_a       ,		padding='max_length'       ,		max_length=len(speech_inputs[0] ) )
			__lowerCAmelCase			:						Union[str, Any]									=      input_a[input_name]

			self.assertTrue(_inputs_have_equal_length(_a ) )
			self.assertFalse(_inputs_have_equal_length(_a ) )

			# truncate to smallest with np
			__lowerCAmelCase			:						Optional[int]									=      feat_extract.pad(
			    _a       ,		padding='max_length'       ,		max_length=len(speech_inputs[0] )       ,		return_tensors='np'       ,		truncation=_a       ,		)
			__lowerCAmelCase			:						int									=      input_a[input_name]

			__lowerCAmelCase			:						Optional[int]									=      feat_extract.pad(
			    _a       ,		padding='max_length'       ,		max_length=len(speech_inputs[0] )       ,		return_tensors='np' )
			__lowerCAmelCase			:						Optional[Any]									=      input_a[input_name]

			self.assertTrue(_inputs_have_equal_length(_a ) )
			self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )

			# since truncation forces padding to be smaller than longest input
			# function can't return `np.ndarray`, but has to return list
			self.assertFalse(_inputs_have_equal_length(_a ) )

			# truncate to middle
			__lowerCAmelCase			:						Optional[int]									=      feat_extract.pad(
			    _a       ,		padding='max_length'       ,		max_length=len(speech_inputs[1] )       ,		truncation=_a       ,		return_tensors='np'       ,		)
			__lowerCAmelCase			:						List[str]									=      input_a[input_name]

			__lowerCAmelCase			:						Tuple									=      feat_extract.pad(
			    _a       ,		padding='max_length'       ,		max_length=len(speech_inputs[1] )       ,		truncation=_a )
			__lowerCAmelCase			:						List[Any]									=      input_a[input_name]

			__lowerCAmelCase			:						int									=      feat_extract.pad(
			    _a       ,		padding='max_length'       ,		max_length=len(speech_inputs[1] )       ,		return_tensors='np' )
			__lowerCAmelCase			:						Union[str, Any]									=      input_a[input_name]

			self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
			self.assertTrue(_inputs_have_equal_length(_a ) )
			self.assertTrue(_inputs_have_equal_length(_a ) )
			self.assertTrue(_inputs_are_equal(_a       ,		_a ) )

			# since truncation forces padding to be smaller than longest input
			# function can't return `np.ndarray`, but has to return list
			self.assertFalse(_inputs_have_equal_length(_a ) )
			self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )

			# padding has to be max_length when setting `truncation=True`
			with self.assertRaises(_a ):
				feat_extract.pad(_a       ,		truncation=_a )[input_name]

			# padding has to be max_length when setting `truncation=True`
			with self.assertRaises(_a ):
				feat_extract.pad(_a       ,		padding='longest'       ,		truncation=_a )[input_name]

			# padding has to be max_length when setting `truncation=True`
			with self.assertRaises(_a ):
				feat_extract.pad(_a       ,		padding='longest'       ,		truncation=_a )[input_name]

			# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
			with self.assertRaises(_a ):
				feat_extract.pad(_a       ,		padding='max_length'       ,		truncation=_a )[input_name]

			# test truncation for `pad_to_multiple_of` for List[int] + numpy
			__lowerCAmelCase			:						List[str]									=      12
			__lowerCAmelCase			:						Tuple									=      feat_extract.pad(
			    _a       ,		padding='max_length'       ,		max_length=len(speech_inputs[0] )       ,		pad_to_multiple_of=_a       ,		truncation=_a       ,		)
			__lowerCAmelCase			:						Optional[int]									=      input_a[input_name]

			__lowerCAmelCase			:						Tuple									=      feat_extract.pad(
			    _a       ,		padding='max_length'       ,		max_length=len(speech_inputs[0] )       ,		pad_to_multiple_of=_a       ,		)
			__lowerCAmelCase			:						str									=      input_a[input_name]

			# retrieve expected_length as multiple of pad_to_multiple_of
			__lowerCAmelCase			:						List[Any]									=      len(speech_inputs[0] )
			if expected_length % pad_to_multiple_of != 0:
				__lowerCAmelCase			:						Tuple									=      ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

			self.assertTrue(len(input_a[0] ) == expected_length )
			self.assertTrue(_inputs_have_equal_length(_a ) )
			self.assertFalse(_inputs_have_equal_length(_a ) )

		def 							__lowerCamelCase							(      self ):
			self._check_padding(numpify=_a )

		def 							__lowerCamelCase							(      self ):
			self._check_padding(numpify=_a )

		def 							__lowerCamelCase							(      self ):
			self._check_truncation(numpify=_a )

		def 							__lowerCamelCase							(      self ):
			self._check_truncation(numpify=_a )

		@require_torch
		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						int									=      self.feature_extraction_class(**self.feat_extract_dict )
			__lowerCAmelCase			:						str									=      self.feat_extract_tester.prepare_inputs_for_common()
			__lowerCAmelCase			:						Optional[Any]									=      feat_extract.model_input_names[0]

			__lowerCAmelCase			:						List[Any]									=      BatchFeature({input_name: speech_inputs} )

			__lowerCAmelCase			:						List[str]									=      feat_extract.pad(_a       ,		padding='longest'       ,		return_tensors='np' )[input_name]
			__lowerCAmelCase			:						Dict									=      feat_extract.pad(_a       ,		padding='longest'       ,		return_tensors='pt' )[input_name]

			self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1E-2 )

		@require_tf
		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						int									=      self.feature_extraction_class(**self.feat_extract_dict )
			__lowerCAmelCase			:						Optional[int]									=      self.feat_extract_tester.prepare_inputs_for_common()
			__lowerCAmelCase			:						Any									=      feat_extract.model_input_names[0]

			__lowerCAmelCase			:						Union[str, Any]									=      BatchFeature({input_name: speech_inputs} )

			__lowerCAmelCase			:						Tuple									=      feat_extract.pad(_a       ,		padding='longest'       ,		return_tensors='np' )[input_name]
			__lowerCAmelCase			:						List[str]									=      feat_extract.pad(_a       ,		padding='longest'       ,		return_tensors='tf' )[input_name]

			self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1E-2 )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						int									=      self.feat_extract_dict
			__lowerCAmelCase			:						int									=      True
			__lowerCAmelCase			:						Union[str, Any]									=      self.feature_extraction_class(**_a )
			__lowerCAmelCase			:						List[Any]									=      self.feat_extract_tester.prepare_inputs_for_common()
			__lowerCAmelCase			:						List[str]									=      [len(_a ) for x in speech_inputs]
			__lowerCAmelCase			:						Dict									=      feat_extract.model_input_names[0]

			__lowerCAmelCase			:						Union[str, Any]									=      BatchFeature({input_name: speech_inputs} )

			__lowerCAmelCase			:						Optional[Any]									=      feat_extract.pad(_a       ,		padding='longest'       ,		return_tensors='np' )
			self.assertIn('attention_mask'       ,		_a )
			self.assertListEqual(list(processed.attention_mask.shape )       ,		list(processed[input_name].shape[:2] ) )
			self.assertListEqual(processed.attention_mask.sum(-1 ).tolist()       ,		_a )







		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Optional[int]									=      self.feat_extract_dict
			__lowerCAmelCase			:						Dict									=      True
			__lowerCAmelCase			:						Any									=      self.feature_extraction_class(**_a )
			__lowerCAmelCase			:						str									=      self.feat_extract_tester.prepare_inputs_for_common()
			__lowerCAmelCase			:						Union[str, Any]									=      [len(_a ) for x in speech_inputs]
			__lowerCAmelCase			:						Dict									=      feat_extract.model_input_names[0]

			__lowerCAmelCase			:						List[Any]									=      BatchFeature({input_name: speech_inputs} )
			__lowerCAmelCase			:						Dict									=      min(_a )

			__lowerCAmelCase			:						Dict									=      feat_extract.pad(
			    _a       ,		padding='max_length'       ,		max_length=_a       ,		truncation=_a       ,		return_tensors='np' )
			self.assertIn('attention_mask'       ,		_a )
			self.assertListEqual(
			    list(processed_pad.attention_mask.shape )       ,		[processed_pad[input_name].shape[0], max_length] )
			self.assertListEqual(
			    processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist()       ,		[max_length for x in speech_inputs] )

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")
