





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE  :						int    							=      {
    '''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
    '''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
    '''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
    '''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      [
			    '''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''Wav2Vec2ForAudioFrameClassification''',
			    '''Wav2Vec2ForCTC''',
			    '''Wav2Vec2ForMaskedLM''',
			    '''Wav2Vec2ForPreTraining''',
			    '''Wav2Vec2ForSequenceClassification''',
			    '''Wav2Vec2ForXVector''',
			    '''Wav2Vec2Model''',
			    '''Wav2Vec2PreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Tuple    							=      [
			    '''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFWav2Vec2ForCTC''',
			    '''TFWav2Vec2Model''',
			    '''TFWav2Vec2PreTrainedModel''',
			    '''TFWav2Vec2ForSequenceClassification''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Dict    							=      [
			    '''FlaxWav2Vec2ForCTC''',
			    '''FlaxWav2Vec2ForPreTraining''',
			    '''FlaxWav2Vec2Model''',
			    '''FlaxWav2Vec2PreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
			from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
			from .processing_wavaveca import WavaVecaProcessor
			from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavaveca import (
						    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavaVecaForAudioFrameClassification,
						    WavaVecaForCTC,
						    WavaVecaForMaskedLM,
						    WavaVecaForPreTraining,
						    WavaVecaForSequenceClassification,
						    WavaVecaForXVector,
						    WavaVecaModel,
						    WavaVecaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_wavaveca import (
						    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFWavaVecaForCTC,
						    TFWavaVecaForSequenceClassification,
						    TFWavaVecaModel,
						    TFWavaVecaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_wavaveca import (
						    FlaxWavaVecaForCTC,
						    FlaxWavaVecaForPreTraining,
						    FlaxWavaVecaModel,
						    FlaxWavaVecaPreTrainedModel,
						)


else:
			import sys

			__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





"""simple docstring"""
from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame



def        lowerCAmelCase_(   lowercase_     :							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(lowercase_						,   headers=lowercase_							).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(''','''						,   ''''''							)							)
																    )
																    * 1_00							)
												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__":
			__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      '''headphones'''
			get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")
