



'''simple docstring'''





import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


lowercase_									=    logging.get_logger(__name__)






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			Tuple		=			'linear'
 __lowerCamelCase       :			Optional[Any]		=			'cosine'
 __lowerCamelCase       :			Optional[int]		=			'cosine_with_restarts'
 __lowerCamelCase       :			Optional[Any]		=			'polynomial'
 __lowerCamelCase       :			Optional[int]		=			'constant'
 __lowerCamelCase       :			Dict		=			'constant_with_warmup'
 __lowerCamelCase       :			Optional[int]		=			'piecewise_constant'





def lowerCAmelCase    (__A    ,      __A = -1):



     """simple docstring"""



     return LambdaLR(__A    ,      lambda __A: 1    ,      last_epoch=__A)




def lowerCAmelCase    (__A    ,      __A    ,      __A = -1):



     """simple docstring"""



     def lr_lambda(__A):
          if current_step < num_warmup_steps:
               return float(__A) / float(max(1.0    ,      __A))
          return 1.0

     return LambdaLR(__A    ,      __A    ,      last_epoch=__A)




def lowerCAmelCase    (__A    ,      __A    ,      __A = -1):



     """simple docstring"""



     _a           =    {}
     _a           =    step_rules.split(''',''')
     for rule_str in rule_list[:-1]:
          _a    ,       _a           =    rule_str.split(''':''')
          _a           =    int(__A)
          _a           =    float(__A)
          _a           =    value
     _a           =    float(rule_list[-1])

     def create_rules_function(__A    ,      __A):
          def rule_func(__A) -> float:
               _a           =    sorted(rules_dict.keys())
               for i, sorted_step in enumerate(__A):
                    if steps < sorted_step:
                         return rules_dict[sorted_steps[i]]
               return last_lr_multiple

          return rule_func

     _a           =    create_rules_function(__A    ,      __A)

     return LambdaLR(__A    ,      __A    ,      last_epoch=__A)




def lowerCAmelCase    (__A    ,      __A    ,      __A    ,      __A=-1):



     """simple docstring"""



     def lr_lambda(__A):
          if current_step < num_warmup_steps:
               return float(__A) / float(max(1    ,      __A))
          return max(
              0.0    ,      float(num_training_steps - current_step) / float(max(1    ,      num_training_steps - num_warmup_steps)))

     return LambdaLR(__A    ,      __A    ,      __A)




def lowerCAmelCase    (__A    ,      __A    ,      __A    ,      __A = 0.5    ,      __A = -1):



     """simple docstring"""



     def lr_lambda(__A):
          if current_step < num_warmup_steps:
               return float(__A) / float(max(1    ,      __A))
          _a           =    float(current_step - num_warmup_steps) / float(max(1    ,      num_training_steps - num_warmup_steps))
          return max(0.0    ,      0.5 * (1.0 + math.cos(math.pi * float(__A) * 2.0 * progress)))

     return LambdaLR(__A    ,      __A    ,      __A)




def lowerCAmelCase    (__A    ,      __A    ,      __A    ,      __A = 1    ,      __A = -1):



     """simple docstring"""



     def lr_lambda(__A):
          if current_step < num_warmup_steps:
               return float(__A) / float(max(1    ,      __A))
          _a           =    float(current_step - num_warmup_steps) / float(max(1    ,      num_training_steps - num_warmup_steps))
          if progress >= 1.0:
               return 0.0
          return max(0.0    ,      0.5 * (1.0 + math.cos(math.pi * ((float(__A) * progress) % 1.0))))

     return LambdaLR(__A    ,      __A    ,      __A)




def lowerCAmelCase    (__A    ,      __A    ,      __A    ,      __A=1e-7    ,      __A=1.0    ,      __A=-1):



     """simple docstring"""



     _a           =    optimizer.defaults['''lr''']
     if not (lr_init > lr_end):
          raise ValueError(F'''lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})''')

     def lr_lambda(__A):
          if current_step < num_warmup_steps:
               return float(__A) / float(max(1    ,      __A))
          elif current_step > num_training_steps:
               return lr_end / lr_init  # as LambdaLR multiplies by lr_init
          else:
               _a           =    lr_init - lr_end
               _a           =    num_training_steps - num_warmup_steps
               _a           =    1 - (current_step - num_warmup_steps) / decay_steps
               _a           =    lr_range * pct_remaining**power + lr_end
               return decay / lr_init  # as LambdaLR multiplies by lr_init

     return LambdaLR(__A    ,      __A    ,      __A)


lowercase_									=    {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}




def lowerCAmelCase    (__A    ,      __A    ,      __A = None    ,      __A = None    ,      __A = None    ,      __A = 1    ,      __A = 1.0    ,      __A = -1    ,      ):



     """simple docstring"""



     _a           =    SchedulerType(__A)
     _a           =    TYPE_TO_SCHEDULER_FUNCTION[name]
     if name == SchedulerType.CONSTANT:
          return schedule_func(__A    ,      last_epoch=__A)

     if name == SchedulerType.PIECEWISE_CONSTANT:
          return schedule_func(__A    ,      step_rules=__A    ,      last_epoch=__A)

     # All other schedulers require `num_warmup_steps`
     if num_warmup_steps is None:
          raise ValueError(F'''{name} requires `num_warmup_steps`, please provide that argument.''')

     if name == SchedulerType.CONSTANT_WITH_WARMUP:
          return schedule_func(__A    ,      num_warmup_steps=__A    ,      last_epoch=__A)

     # All other schedulers require `num_training_steps`
     if num_training_steps is None:
          raise ValueError(F'''{name} requires `num_training_steps`, please provide that argument.''')

     if name == SchedulerType.COSINE_WITH_RESTARTS:
          return schedule_func(
              __A    ,      num_warmup_steps=__A    ,      num_training_steps=__A    ,      num_cycles=__A    ,      last_epoch=__A    ,      )

     if name == SchedulerType.POLYNOMIAL:
          return schedule_func(
              __A    ,      num_warmup_steps=__A    ,      num_training_steps=__A    ,      power=__A    ,      last_epoch=__A    ,      )

     return schedule_func(
         __A    ,      num_warmup_steps=__A    ,      num_training_steps=__A    ,      last_epoch=__A)








'''simple docstring'''





from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame




def lowerCAmelCase    (__A = "laptop"):



     """simple docstring"""



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



