




from dataclasses import dataclass, field
from typing import Optional







@dataclass
class   A:



 '''simple docstring'''


 UpperCamelCase						   =						field(
     default='''codeparrot/codeparrot'''							,      metadata={'''help''': '''Model name or path of model to be trained.'''}   )
 UpperCamelCase						   =						field(
     default='''./'''							,      metadata={'''help''': '''Save dir where model repo is cloned and models updates are saved to.'''}   )
 UpperCamelCase						   =						field(
     default='''codeparrot/codeparrot-clean-train'''							,      metadata={'''help''': '''Name or path of training dataset.'''}   )
 UpperCamelCase						   =						field(
     default='''codeparrot/codeparrot-clean-valid'''							,      metadata={'''help''': '''Name or path of validation dataset.'''}   )
 UpperCamelCase						   =						field(default=2							,      metadata={'''help''': '''Batch size for training.'''}   )
 UpperCamelCase						   =						field(default=2							,      metadata={'''help''': '''Batch size for evaluation.'''}   )
 UpperCamelCase						   =						field(default=0.1							,      metadata={'''help''': '''Value of weight decay.'''}   )
 UpperCamelCase						   =						field(
     default=1_0000							,      metadata={'''help''': '''Size of buffer used to shuffle streaming dataset.'''}   )
 UpperCamelCase						   =						field(default=2e-4							,      metadata={'''help''': '''Learning rate fo training.'''}   )
 UpperCamelCase						   =						field(default='''cosine'''							,      metadata={'''help''': '''Learning rate.'''}   )
 UpperCamelCase						   =						field(
     default=750							,      metadata={'''help''': '''Number of warmup steps in the learning rate schedule.'''}   )
 UpperCamelCase						   =						field(
     default=16							,      metadata={'''help''': '''Number of gradient accumulation steps.'''}   )
 UpperCamelCase						   =						field(
     default=UpperCamelCase							,      metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''}   )
 UpperCamelCase						   =						field(default=5_0000							,      metadata={'''help''': '''Maximum number of training steps.'''}   )
 UpperCamelCase						   =						field(
     default=-1							,      metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}   )
 UpperCamelCase						   =						field(default=1024							,      metadata={'''help''': '''Sequence lengths used for training.'''}   )
 UpperCamelCase						   =						field(default=1							,      metadata={'''help''': '''Training seed.'''}   )
 UpperCamelCase						   =						field(
     default=1024							,      metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}							,      )
 UpperCamelCase						   =						field(
     default=UpperCamelCase							,      metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''}   )
 UpperCamelCase						   =						field(default=UpperCamelCase							,      metadata={'''help''': '''If True the data is pretokenized.'''}   )







@dataclass
class   A:



 '''simple docstring'''


 UpperCamelCase						   =						field(
     default='''codeparrot/codeparrot'''							,      metadata={'''help''': '''Model name or path of model to be evaluated.'''}   )
 UpperCamelCase						   =						field(
     default='''codeparrot/codeparrot-clean-valid'''							,      metadata={'''help''': '''Name or path of validation dataset.'''}   )
 UpperCamelCase						   =						field(default=2							,      metadata={'''help''': '''Batch size used for evaluation.'''}   )
 UpperCamelCase						   =						field(
     default=-1							,      metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}   )
 UpperCamelCase						   =						field(default=1024							,      metadata={'''help''': '''Length of sequences to be evaluated.'''}   )
 UpperCamelCase						   =						field(default=1							,      metadata={'''help''': '''Random seed used for evaluation.'''}   )







@dataclass
class   A:



 '''simple docstring'''


 UpperCamelCase						   =						field(
     default='''codeparrot/codeparrot'''							,      metadata={'''help''': '''Model name or path of model to be evaluated.'''}   )
 UpperCamelCase						   =						field(default=UpperCamelCase							,      metadata={'''help''': '''Number of workers used for code evaluation.'''}   )
 UpperCamelCase						   =						field(
     default=UpperCamelCase							,      metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}							,      )
 UpperCamelCase						   =						field(
     default=UpperCamelCase							,      metadata={'''help''': '''Sample from the language model\'s output distribution.'''}   )
 UpperCamelCase						   =						field(default=0.2							,      metadata={'''help''': '''Sampling temperature used for generation.'''}   )
 UpperCamelCase						   =						field(default=256							,      metadata={'''help''': '''Maximum number of newly generated tokens.'''}   )
 UpperCamelCase						   =						field(default=0							,      metadata={'''help''': '''Top-k parameter used for generation.'''}   )
 UpperCamelCase						   =						field(default=0.95							,      metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''}   )
 UpperCamelCase						   =						field(default=10							,      metadata={'''help''': '''Number of generations to run in parallel.'''}   )
 UpperCamelCase						   =						field(
     default=200							,      metadata={'''help''': '''Number of completions to generate for each sample.'''}   )
 UpperCamelCase						   =						field(default=1							,      metadata={'''help''': '''Random seed used for evaluation.'''}   )
 UpperCamelCase						   =						field(
     default='''eval_results.json'''							,      metadata={'''help''': '''Random seed used for evaluation.'''}   )
 UpperCamelCase						   =						field(
     default='''0'''							,      metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''}   )
 UpperCamelCase						   =						field(
     default=-1							,      metadata={
         '''help''': (
             '''Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive'''
             ''' number corresponds to which GPU device id to run on.'''
         )
     }							,      )







@dataclass
class   A:



 '''simple docstring'''


 UpperCamelCase						   =						field(
     default=UpperCamelCase							,      metadata={
         '''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
     }							,      )
 UpperCamelCase						   =						field(
     default='''transformersbook/codeparrot'''							,      metadata={'''help''': '''Folder or name of dataset to process.'''}   )
 UpperCamelCase						   =						field(
     default='''codeparrot-clean'''							,      metadata={'''help''': '''Folder to save processed processed dataset.'''}   )
 UpperCamelCase						   =						field(
     default=10_0000							,      metadata={'''help''': '''Number of files to save per JSON output file.'''}   )
 UpperCamelCase						   =						field(default='''content'''							,      metadata={'''help''': '''Column containing text data to process.'''}   )
 UpperCamelCase						   =						field(
     default=1000							,      metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}   )
 UpperCamelCase						   =						field(
     default=100							,      metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''}   )
 UpperCamelCase						   =						field(
     default=0.25							,      metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''}   )
 UpperCamelCase						   =						field(
     default=1.5							,      metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''}   )
 UpperCamelCase						   =						field(
     default=0.7							,      metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''}   )
 UpperCamelCase						   =						field(
     default='''codeparrot/codeparrot'''							,      metadata={'''help''': '''Name or path to the tokenizer.'''}							,      )
 UpperCamelCase						   =						field(
     default=UpperCamelCase							,      metadata={'''help''': '''If True, near-duplicate samples are removed.'''}   )
 UpperCamelCase						   =						field(
     default=0.85							,      metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}   )







@dataclass
class   A:



 '''simple docstring'''


 UpperCamelCase						   =						field(
     default='''gpt2'''							,      metadata={'''help''': '''Base tokenizer to build new tokenizer from.'''}   )
 UpperCamelCase						   =						field(
     default='''transformersbook/codeparrot-train'''							,      metadata={'''help''': '''Dataset to train tokenizer on.'''}   )
 UpperCamelCase						   =						field(default='''content'''							,      metadata={'''help''': '''Column containing text data to process.'''}   )
 UpperCamelCase						   =						field(default=20_0000							,      metadata={'''help''': '''Number of examples to train tokenizer on.'''}   )
 UpperCamelCase						   =						field(
     default=3_2768							,      metadata={'''help''': '''Number of examples to train the tokenizer on.'''}   )
 UpperCamelCase						   =						field(default='''codeparrot'''							,      metadata={'''help''': '''Name of new tokenizer.'''}   )
 UpperCamelCase						   =						field(default=UpperCamelCase							,      metadata={'''help''': '''Push saved tokenizer to the hub.'''}   )







@dataclass
class   A:



 '''simple docstring'''


 UpperCamelCase						   =						field(
     default='''codeparrot/codeparrot'''							,      metadata={'''help''': '''Name or path to the tokenizer.'''}   )
 UpperCamelCase						   =						field(
     default='''codeparrot/codeparrot-clean-train'''							,      metadata={'''help''': '''Name or path to the dataset to pretokenize.'''}   )
 UpperCamelCase						   =						field(
     default='''tokenized-codeparrot-train'''							,      metadata={'''help''': '''Repo name of the pretokenized data.'''}   )
 UpperCamelCase						   =						field(default=UpperCamelCase							,      metadata={'''help''': '''Number of workers used for code evaluation.'''}   )







@dataclass
class   A:



 '''simple docstring'''


 UpperCamelCase						   =						field(
     default='''gpt2-large'''							,      metadata={'''help''': '''Configuration to use for model initialization.'''}   )
 UpperCamelCase						   =						field(
     default='''codeparrot/codeparrot'''							,      metadata={'''help''': '''Tokenizer attached to model.'''}   )
 UpperCamelCase						   =						field(default='''codeparrot'''							,      metadata={'''help''': '''Name of the created model.'''}   )
 UpperCamelCase						   =						field(default=UpperCamelCase							,      metadata={'''help''': '''Push saved tokenizer to the hub.'''}   )









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""")



