
import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
__A				=logging.getLogger(__name__)





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			git.Repo(search_parent_directories=lowerCamelCase__    )
				lowerCamelCase_						=			{
				    "repo_id": str(lowerCamelCase__    ),
				    "repo_sha": str(repo.head.object.hexsha    ),
				    "repo_branch": str(repo.active_branch    ),
				}

				with open(os.path.join(lowerCamelCase__	, "git_log.json"    )	, "w"    ) as f:
								json.dump(lowerCamelCase__	, lowerCamelCase__	, indent=4    )





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				if params.n_gpu <= 0:
								lowerCamelCase_						=			0
								lowerCamelCase_						=			-1
								lowerCamelCase_						=			True
								lowerCamelCase_						=			False
								return

				assert torch.cuda.is_available()

				logger.info("Initializing GPUs"    )
				if params.n_gpu > 1:
								assert params.local_rank != -1

								lowerCamelCase_						=			int(os.environ["WORLD_SIZE"]    )
								lowerCamelCase_						=			int(os.environ["N_GPU_NODE"]    )
								lowerCamelCase_						=			int(os.environ["RANK"]    )

								# number of nodes / node ID
								lowerCamelCase_						=			params.world_size // params.n_gpu_per_node
								lowerCamelCase_						=			params.global_rank // params.n_gpu_per_node
								lowerCamelCase_						=			True

								assert params.n_nodes == int(os.environ["N_NODES"]    )
								assert params.node_id == int(os.environ["NODE_RANK"]    )

				# local job (single GPU)
				else:
								assert params.local_rank == -1

								lowerCamelCase_						=			1
								lowerCamelCase_						=			0
								lowerCamelCase_						=			0
								lowerCamelCase_						=			0
								lowerCamelCase_						=			1
								lowerCamelCase_						=			1
								lowerCamelCase_						=			False

				# sanity checks
				assert params.n_nodes >= 1
				assert 0 <= params.node_id < params.n_nodes
				assert 0 <= params.local_rank <= params.global_rank < params.world_size
				assert params.world_size == params.n_nodes * params.n_gpu_per_node

				# define whether this is the master process / if we are in multi-node distributed mode
				lowerCamelCase_						=			params.node_id == 0 and params.local_rank == 0
				lowerCamelCase_						=			params.n_nodes > 1

				# summary
				lowerCamelCase_						=			F'--- Global rank: {params.global_rank} - '
				logger.info(PREFIX + "Number of nodes: %i" % params.n_nodes    )
				logger.info(PREFIX + "Node ID        : %i" % params.node_id    )
				logger.info(PREFIX + "Local rank     : %i" % params.local_rank    )
				logger.info(PREFIX + "World size     : %i" % params.world_size    )
				logger.info(PREFIX + "GPUs per node  : %i" % params.n_gpu_per_node    )
				logger.info(PREFIX + "Master         : %s" % str(params.is_master    )    )
				logger.info(PREFIX + "Multi-node     : %s" % str(params.multi_node    )    )
				logger.info(PREFIX + "Multi-GPU      : %s" % str(params.multi_gpu    )    )
				logger.info(PREFIX + "Hostname       : %s" % socket.gethostname()    )

				# set GPU device
				torch.cuda.set_device(params.local_rank    )

				# initialize multi-GPU
				if params.multi_gpu:
								logger.info("Initializing PyTorch distributed"    )
								torch.distributed.init_process_group(
								    init_method="env://"	, backend="nccl"	, )





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				np.random.seed(args.seed    )
				torch.manual_seed(args.seed    )
				if args.n_gpu > 0:
								torch.cuda.manual_seed_all(args.seed    )








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""")



