




from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame







def SCREAMING_SNAKE_CASE				(       lowercase_ = "laptop"						)   ->       str:



       """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\n        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36",
           "Accept-Language": "en-US, en;q=0.5",
       }
       A__       =    BeautifulSoup(requests.get(__lowerCAmelCase ,				headers=__lowerCAmelCase						).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__":
  _lowerCamelCase	:		Optional[int]							       =   """headphones"""
  get_amazon_product_data(product).to_csv(F'''Amazon Product Data for {product}.csv''')






'''simple docstring'''


#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist






def        __lowerCAmelCase       (*__lowerCAmelCase      ):
		with open(__lowerCAmelCase					,       "r"      ) as fh:
				fcntl.flock(__lowerCAmelCase					,       fcntl.LOCK_EX      )
				try:
						print(*__lowerCAmelCase      )
				finally:
						fcntl.flock(__lowerCAmelCase					,       fcntl.LOCK_UN      )


lowerCamelCase__					=   int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
lowerCamelCase__					=   torch.device('cuda', local_rank)
lowerCamelCase__					=   socket.gethostname()

lowerCamelCase__					=   F'''[{hostname}-{local_rank}]'''

try:
						# test distributed
						dist.init_process_group('nccl')
						dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
						dist.barrier()

						# test cuda is available and can allocate memory
						torch.cuda.is_available()
						torch.ones(1).cuda(local_rank)

						# global rank
						lowerCamelCase__					=   dist.get_rank()
						lowerCamelCase__					=   dist.get_world_size()

						printflock(F'''{gpu} is OK (global rank: {rank}/{world_size})''')

						dist.barrier()
						if rank == 0:
												printflock(F'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
						printflock(F'''{gpu} is broken''')
						raise



