





'''simple docstring'''




import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowercase__    :      int						=    random.Random()

if is_torch_available():
  import torch






def        _lowerCAmelCase			(  __snake_case  : Optional[int] ,							__snake_case  : Optional[int]=1.0 ,							__snake_case  : Union[str, Any]=None ,							__snake_case  : Optional[int]=None			)     ->     Any:



     if rng is None:
          __A						: str      =       global_rng

     __A						: List[str]      =       []
     for batch_idx in range(shape[0]			):
          values.append([]			)
          for _ in range(shape[1]			):
               values[-1].append(rng.random() * scale			)

     return values


class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):


     def __init__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase=7						,	_UpperCAmelCase=400						,	_UpperCAmelCase=2000						,	_UpperCAmelCase=1						,	_UpperCAmelCase=0.0						,	_UpperCAmelCase=1_6000						,	_UpperCAmelCase=True						,	_UpperCAmelCase=True						,	):

          '''simple docstring'''






          __A						: Union[str, Any]      =       parent
          __A						: str      =       batch_size
          __A						: Union[str, Any]      =       min_seq_length
          __A						: int      =       max_seq_length
          __A						: List[str]      =       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
          __A						: str      =       feature_size
          __A						: Dict      =       padding_value
          __A						: List[str]      =       sampling_rate
          __A						: Dict      =       return_attention_mask
          __A						: Optional[int]      =       do_normalize


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return {
              "feature_size": self.feature_size,
              "padding_value": self.padding_value,
              "sampling_rate": self.sampling_rate,
              "return_attention_mask": self.return_attention_mask,
              "do_normalize": self.do_normalize,
          }


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase=False						,	_UpperCAmelCase=False):

          '''simple docstring'''






          def _flatten(_UpperCAmelCase):
               return list(itertools.chain(*A_))

          if equal_length:
               __A						: List[Any]      =       floats_list((self.batch_size, self.max_seq_length))
          else:
               # make sure that inputs increase in size
               __A						: Optional[int]      =       [
                   _flatten(floats_list((x, self.feature_size)))
                   for x in range(self.min_seq_length						,	self.max_seq_length						,	self.seq_length_diff)
               ]

          if numpify:
               __A						: Union[str, Any]      =       [np.asarray(A_) for x in speech_inputs]

          return speech_inputs




@require_torch
@require_torchaudio
class  SCREAMING_SNAKE_CASE    (a__					,		unittest.TestCase							):
     lowerCAmelCase					=       ASTFeatureExtractor


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Union[str, Any]      =       ASTFeatureExtractionTester(self)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[int]      =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
          # create three inputs of length 800, 1000, and 1200
          __A						: List[str]      =       [floats_list((1, x))[0] for x in range(800						,	1400						,	200)]
          __A						: Union[str, Any]      =       [np.asarray(A_) for speech_input in speech_inputs]

          # Test not batched input
          __A						: Union[str, Any]      =       feat_extract(speech_inputs[0]						,	return_tensors='np').input_values
          __A						: Dict      =       feat_extract(np_speech_inputs[0]						,	return_tensors='np').input_values
          self.assertTrue(np.allclose(A_						,	A_						,	atol=1e-3))

          # Test batched
          __A						: List[Any]      =       feat_extract(A_						,	padding=A_						,	return_tensors='np').input_values
          __A						: int      =       feat_extract(A_						,	padding=A_						,	return_tensors='np').input_values
          for enc_seq_a, enc_seq_a in zip(A_						,	A_):
               self.assertTrue(np.allclose(A_						,	A_						,	atol=1e-3))

          # Test 2-D numpy arrays are batched.
          __A						: Tuple      =       [floats_list((1, x))[0] for x in (800, 800, 800)]
          __A						: Optional[Any]      =       np.asarray(A_)
          __A						: Optional[int]      =       feat_extract(A_						,	return_tensors='np').input_values
          __A						: Optional[int]      =       feat_extract(A_						,	return_tensors='np').input_values
          for enc_seq_a, enc_seq_a in zip(A_						,	A_):
               self.assertTrue(np.allclose(A_						,	A_						,	atol=1e-3))


     @require_torch
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          import torch

          __A						: Optional[int]      =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
          __A						: List[Any]      =       np.random.rand(100).astype(np.floataa)
          __A						: Any      =       np_speech_inputs.tolist()

          for inputs in [py_speech_inputs, np_speech_inputs]:
               __A						: Any      =       feature_extractor.pad([{'input_values': inputs}]						,	return_tensors='np')
               self.assertTrue(np_processed.input_values.dtype == np.floataa)
               __A						: Optional[Any]      =       feature_extractor.pad([{'input_values': inputs}]						,	return_tensors='pt')
               self.assertTrue(pt_processed.input_values.dtype == torch.floataa)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          from datasets import load_dataset

          __A						: Optional[Any]      =       load_dataset('hf-internal-testing/librispeech_asr_dummy'						,	'clean'						,	split='validation')
          # automatic decoding with librispeech
          __A						: Union[str, Any]      =       ds.sort('id').select(range(A_))[:num_samples]['audio']

          return [x["array"] for x in speech_samples]


     @require_torch
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[str]      =       torch.tensor(
              [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
               -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
               -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
               -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869])
          # fmt: on

          __A						: str      =       self._load_datasamples(1)
          __A						: Dict      =       ASTFeatureExtractor()
          __A						: Optional[int]      =       feature_extractor(A_						,	return_tensors='pt').input_values
          self.assertEquals(input_values.shape						,	(1, 1024, 128))
          self.assertTrue(torch.allclose(input_values[0, 0, :30]						,	A_						,	atol=1e-4))





from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       str = "laptop"     ):


      '''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\n        (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__":
 lowerCamelCase				:		Tuple     =   "headphones"
 get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")



