



import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list






@require_torchaudio
@require_sentencepiece
class 						__a (     unittest.TestCase				):



	def 		UpperCamelCase							(						self							:    int)->							Union[str, Any]:
						__lowerCAmelCase	   ="""laion/clap-htsat-unfused"""
						__lowerCAmelCase	   =tempfile.mkdtemp()



	def 		UpperCamelCase							(						self							:    List[Any]				,	**snake_case_							:    List[str])->							List[str]:
						return RobertaTokenizer.from_pretrained(self.checkpoint				,	**snake_case_)



	def 		UpperCamelCase							(						self							:    Optional[Any]				,	**snake_case_							:    Optional[int])->							Any:
						return ClapFeatureExtractor.from_pretrained(self.checkpoint				,	**snake_case_)



	def 		UpperCamelCase							(						self							:    int)->							Tuple:
						shutil.rmtree(self.tmpdirname)



	def 		UpperCamelCase							(						self							:    Optional[Any])->							str:
						__lowerCAmelCase	   =self.get_tokenizer()
						__lowerCAmelCase	   =self.get_feature_extractor()

						__lowerCAmelCase	   =ClapProcessor(tokenizer=snake_case_				,	feature_extractor=snake_case_)

						processor.save_pretrained(self.tmpdirname)
						__lowerCAmelCase	   =ClapProcessor.from_pretrained(self.tmpdirname)

						self.assertEqual(processor.tokenizer.get_vocab()				,	tokenizer.get_vocab())
						self.assertIsInstance(processor.tokenizer				,	snake_case_)

						self.assertEqual(processor.feature_extractor.to_json_string()				,	feature_extractor.to_json_string())
						self.assertIsInstance(processor.feature_extractor				,	snake_case_)



	def 		UpperCamelCase							(						self							:    Dict)->							int:
						__lowerCAmelCase	   =ClapProcessor(tokenizer=self.get_tokenizer()				,	feature_extractor=self.get_feature_extractor())
						processor.save_pretrained(self.tmpdirname)

						__lowerCAmelCase	   =self.get_tokenizer(bos_token="""(BOS)"""				,	eos_token="""(EOS)""")
						__lowerCAmelCase	   =self.get_feature_extractor(do_normalize=snake_case_				,	padding_value=1.0)

						__lowerCAmelCase	   =ClapProcessor.from_pretrained(
						    self.tmpdirname				,	bos_token="""(BOS)"""				,	eos_token="""(EOS)"""				,	do_normalize=snake_case_				,	padding_value=1.0)

						self.assertEqual(processor.tokenizer.get_vocab()				,	tokenizer_add_kwargs.get_vocab())
						self.assertIsInstance(processor.tokenizer				,	snake_case_)

						self.assertEqual(processor.feature_extractor.to_json_string()				,	feature_extractor_add_kwargs.to_json_string())
						self.assertIsInstance(processor.feature_extractor				,	snake_case_)



	def 		UpperCamelCase							(						self							:    str)->							Dict:
						__lowerCAmelCase	   =self.get_feature_extractor()
						__lowerCAmelCase	   =self.get_tokenizer()

						__lowerCAmelCase	   =ClapProcessor(tokenizer=snake_case_				,	feature_extractor=snake_case_)

						__lowerCAmelCase	   =floats_list((3, 10_00))

						__lowerCAmelCase	   =feature_extractor(snake_case_				,	return_tensors="""np""")
						__lowerCAmelCase	   =processor(audios=snake_case_				,	return_tensors="""np""")

						for key in input_feat_extract.keys():
											self.assertAlmostEqual(input_feat_extract[key].sum()				,	input_processor[key].sum()				,	delta=1e-2)



	def 		UpperCamelCase							(						self							:    List[Any])->							Tuple:
						__lowerCAmelCase	   =self.get_feature_extractor()
						__lowerCAmelCase	   =self.get_tokenizer()

						__lowerCAmelCase	   =ClapProcessor(tokenizer=snake_case_				,	feature_extractor=snake_case_)

						__lowerCAmelCase	   ="""This is a test string"""

						__lowerCAmelCase	   =processor(text=snake_case_)

						__lowerCAmelCase	   =tokenizer(snake_case_)

						for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]				,	encoded_processor[key])



	def 		UpperCamelCase							(						self							:    List[str])->							Dict:
						__lowerCAmelCase	   =self.get_feature_extractor()
						__lowerCAmelCase	   =self.get_tokenizer()

						__lowerCAmelCase	   =ClapProcessor(tokenizer=snake_case_				,	feature_extractor=snake_case_)

						__lowerCAmelCase	   =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						__lowerCAmelCase	   =processor.batch_decode(snake_case_)
						__lowerCAmelCase	   =tokenizer.batch_decode(snake_case_)

						self.assertListEqual(snake_case_				,	snake_case_)



	def 		UpperCamelCase							(						self							:    Union[str, Any])->							List[Any]:
						__lowerCAmelCase	   =self.get_feature_extractor()
						__lowerCAmelCase	   =self.get_tokenizer()

						__lowerCAmelCase	   =ClapProcessor(tokenizer=snake_case_				,	feature_extractor=snake_case_)

						self.assertListEqual(
						    processor.model_input_names[2:]				,	feature_extractor.model_input_names				,	msg="""`processor` and `feature_extractor` model input names do not match"""				,	)









from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame






def 					__lowerCAmelCase		(  __lowerCamelCase	:    str = "laptop"	)  ->						DataFrame:
					__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(__lowerCamelCase				,   headers=__lowerCamelCase	).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__":
				lowercase_	       =			'''headphones'''
				get_amazon_product_data(product).to_csv(F"Amazon Product Data for {product}.csv")




