import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__UpperCamelCase		:       Union[str, Any]									=     getLogger(__name__)
def       A	(    _lowercase		,  _lowercase		,  _lowercase		,  _lowercase = 8		,  _lowercase = 1_024		,  _lowercase="val"		,  _lowercase=None		,  _lowercase=False		,  _lowercase="summarization"		,  _lowercase=None		,  _lowercase=1		,  _lowercase = None		,  _lowercase=""		,  **_lowercase		,  ):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							str(_lowercase						)
						assert local_rank is not None
						torch.distributed.init_process_group(backend='''nccl'''		,  rank=_lowercase						)

						SCREAMING_SNAKE_CASE							:    Tuple							=							Path(_lowercase						)
						SCREAMING_SNAKE_CASE							:    int							=							save_dir.joinpath(f"""rank_{local_rank}_output.json"""						)
						torch.cuda.set_device(_lowercase						)
						SCREAMING_SNAKE_CASE							:    List[str]							=							AutoModelForSeqaSeqLM.from_pretrained(_lowercase						).cuda()
						if fpaa:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							model.half()
						# determine if we need to increase num_beams
						use_task_specific_params(_lowercase		,  _lowercase						)  # update config with task specific params
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							generate_kwargs.pop('''num_beams'''		,  model.config.num_beams						)  # AttributeError risk?
						if num_return_sequences > num_beams:
												SCREAMING_SNAKE_CASE							:    int							=							num_return_sequences

						SCREAMING_SNAKE_CASE							:    Tuple							=							AutoTokenizer.from_pretrained(_lowercase						)
						logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""						)  # if this is wrong, check config.model_type.

						if max_source_length is None:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							tokenizer.model_max_length
						if prefix is None:
												SCREAMING_SNAKE_CASE							:    Tuple							=							prefix or getattr(model.config		,  '''prefix'''		,  ''''''						) or ''''''
						SCREAMING_SNAKE_CASE							:    Any							=							SeqaSeqDataset(
						    _lowercase		,  _lowercase		,  _lowercase		,  max_target_length=1_024		,  type_path=_lowercase		,  n_obs=_lowercase		,  prefix=_lowercase		,  **_lowercase		,  )
						# I set shuffle=True for a more accurate progress bar.
						# If all the longest samples are first, the prog bar estimate is too high at the beginning.
						SCREAMING_SNAKE_CASE							:    str							=							ds.make_sortish_sampler(_lowercase		,  distributed=_lowercase		,  add_extra_examples=_lowercase		,  shuffle=_lowercase						)
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							DataLoader(_lowercase		,  sampler=_lowercase		,  batch_size=_lowercase		,  collate_fn=ds.collate_fn						)
						SCREAMING_SNAKE_CASE							:    List[str]							=							[]
						for batch in tqdm(_lowercase						):
												SCREAMING_SNAKE_CASE							:    Tuple							=							model.generate(
												    input_ids=batch['''input_ids'''].to(model.device						)		,  attention_mask=batch['''attention_mask'''].to(model.device						)		,  num_return_sequences=_lowercase		,  num_beams=_lowercase		,  **_lowercase		,  )
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tokenizer.batch_decode(_lowercase		,  skip_special_tokens=_lowercase		,  clean_up_tokenization_spaces=_lowercase						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							batch['''ids''']
												if num_return_sequences > 1:
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							chunks(_lowercase		,  _lowercase						)  # batch size chunks, each of size num_return_seq
												for i, pred in enumerate(_lowercase						):
																		results.append({'''pred''': pred, '''id''': ids[i].item()}						)
						save_json(_lowercase		,  _lowercase						)
						return results, sampler.num_replicas
def       A	(    ):
						SCREAMING_SNAKE_CASE							:    str							=							argparse.ArgumentParser(
						    epilog='''Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'''						)
						parser.add_argument('''--data_dir'''		,  type=_lowercase		,  help='''like cnn_dm/test.source'''						)
						parser.add_argument(
						    '''--model_name'''		,  type=_lowercase		,  help='''like facebook/bart-large-cnn,t5-base, etc.'''		,  default='''sshleifer/distilbart-xsum-12-3'''		,  )
						parser.add_argument('''--save_dir'''		,  type=_lowercase		,  help='''where to save'''		,  default='''tmp_gen'''						)
						parser.add_argument('''--max_source_length'''		,  type=_lowercase		,  default=_lowercase						)
						parser.add_argument(
						    '''--type_path'''		,  type=_lowercase		,  default='''test'''		,  help='''which subset to evaluate typically train/val/test'''						)
						parser.add_argument('''--task'''		,  type=_lowercase		,  default='''summarization'''		,  help='''used for task_specific_params + metrics'''						)
						parser.add_argument('''--bs'''		,  type=_lowercase		,  default=8		,  required=_lowercase		,  help='''batch size'''						)
						parser.add_argument(
						    '''--local_rank'''		,  type=_lowercase		,  default=-1		,  required=_lowercase		,  help='''should be passed by distributed.launch'''						)

						parser.add_argument(
						    '''--n_obs'''		,  type=_lowercase		,  default=_lowercase		,  required=_lowercase		,  help='''How many observations. Defaults to all.'''						)
						parser.add_argument(
						    '''--num_return_sequences'''		,  type=_lowercase		,  default=1		,  required=_lowercase		,  help='''How many sequences to return'''						)
						parser.add_argument(
						    '''--sync_timeout'''		,  type=_lowercase		,  default=600		,  required=_lowercase		,  help='''How long should master process wait for other processes to finish.'''		,  )
						parser.add_argument('''--src_lang'''		,  type=_lowercase		,  default=_lowercase		,  required=_lowercase						)
						parser.add_argument('''--tgt_lang'''		,  type=_lowercase		,  default=_lowercase		,  required=_lowercase						)
						parser.add_argument(
						    '''--prefix'''		,  type=_lowercase		,  required=_lowercase		,  default=_lowercase		,  help='''will be added to the begininng of src examples'''						)
						parser.add_argument('''--fp16'''		,  action='''store_true'''						)
						parser.add_argument('''--debug'''		,  action='''store_true'''						)
						SCREAMING_SNAKE_CASE							:    str							=							time.time()
						SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Tuple							=							parser.parse_known_args()
						SCREAMING_SNAKE_CASE							:    List[str]							=							parse_numeric_n_bool_cl_kwargs(_lowercase						)
						if generate_kwargs and args.local_rank <= 0:
												print(f"""parsed the following generate kwargs: {generate_kwargs}"""						)
						SCREAMING_SNAKE_CASE							:    Dict							=							Path(args.save_dir + '''_tmp'''						)
						Path(_lowercase						).mkdir(exist_ok=_lowercase						)  # this handles locking.
						SCREAMING_SNAKE_CASE							:    Dict							=							list(json_save_dir.glob('''rank_*.json'''						)						)
						if intermediate_files:
												raise ValueError(f"""Found files at {json_save_dir} please move or remove them."""						)
												# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
						SCREAMING_SNAKE_CASE							:    str							=							{}
						if args.src_lang is not None:
												SCREAMING_SNAKE_CASE							:    Dict							=							args.src_lang
						if args.tgt_lang is not None:
												SCREAMING_SNAKE_CASE							:    int							=							args.tgt_lang

						Path(args.save_dir						).mkdir(exist_ok=_lowercase						)
						SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[Any]							=							eval_data_dir(
						    args.data_dir		,  _lowercase		,  args.model_name		,  type_path=args.type_path		,  bs=args.bs		,  fpaa=args.fpaa		,  task=args.task		,  local_rank=args.local_rank		,  n_obs=args.n_obs		,  max_source_length=args.max_source_length		,  num_return_sequences=args.num_return_sequences		,  prefix=args.prefix		,  dataset_kwargs=_lowercase		,  **_lowercase		,  )

						if args.local_rank <= 0:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							Path(args.save_dir						)
												save_dir.mkdir(exist_ok=_lowercase						)
												SCREAMING_SNAKE_CASE							:    int							=							gather_results_from_each_node(_lowercase		,  _lowercase		,  args.sync_timeout						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							combine_partial_results(_lowercase						)
												if args.num_return_sequences > 1:
																		SCREAMING_SNAKE_CASE							:    Dict							=							save_dir.joinpath('''pseudolabel_results.json'''						)
																		print(f"""Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"""						)
																		save_json(_lowercase		,  _lowercase						)
																		return
												SCREAMING_SNAKE_CASE							:    int							=							Path(args.data_dir						).joinpath(args.type_path + '''.target'''						)
												with open(_lowercase						) as f:
																		SCREAMING_SNAKE_CASE							:    Any							=							[x.rstrip() for x in f.readlines()][: len(_lowercase						)]

												# Calculate metrics, save metrics,  and save _generations.txt
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							'''translation''' in args.task
												SCREAMING_SNAKE_CASE							:    List[Any]							=							calculate_bleu if calc_bleu else calculate_rouge
												SCREAMING_SNAKE_CASE							:    int							=							'''bleu''' if calc_bleu else '''rouge'''
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							score_fn(_lowercase		,  _lowercase						)
												SCREAMING_SNAKE_CASE							:    int							=							len(_lowercase						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							time.time() - start_time
												SCREAMING_SNAKE_CASE							:    List[str]							=							round(runtime / metrics['''n_obs''']		,  4						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							num_replicas
												# TODO(@stas00): add whatever metadata to metrics
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							save_dir.joinpath(f"""{args.type_path}_{metric_name}.json"""						)
												save_json(_lowercase		,  _lowercase		,  indent=_lowercase						)
												print(_lowercase						)
												write_txt_file(_lowercase		,  save_dir.joinpath(f"""{args.type_path}_generations.txt"""						)						)
												if args.debug:
																		write_txt_file(_lowercase		,  save_dir.joinpath(f"""{args.type_path}.target"""						)						)
												else:
																		shutil.rmtree(_lowercase						)
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							[]
						for partial_result in partial_results:
												records.extend(_lowercase						)
						SCREAMING_SNAKE_CASE							:    List[str]							=							sorted(_lowercase		,  key=lambda _lowercase						: x["id"]						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							[x['''pred'''] for x in records]
						return preds
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						# WAIT FOR lots of .json files
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							time.time()
						logger.info('''waiting for all nodes to finish'''						)
						SCREAMING_SNAKE_CASE							:    List[str]							=							None
						while (time.time() - start_wait) < timeout:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							list(save_dir.glob('''rank_*.json'''						)						)
												if len(_lowercase						) < num_replicas:
																		continue
												try:
																		# make sure all json files are fully saved
																		SCREAMING_SNAKE_CASE							:    Any							=							lmap(_lowercase		,  _lowercase						)
																		return json_data
												except JSONDecodeError:
																		continue
						else:
												raise TimeoutError('''Rank 0 gave up on waiting for other processes'''						)
						# Unreachable


if __name__ == "__main__":
					# Usage for MT:
					run_generate()












"""simple docstring"""
from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame






def 					A__				(			UpperCamelCase = "laptop"       ):
							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(UpperCamelCase					,  headers=UpperCamelCase       ).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__":
		_snake_case     :		Optional[int]       						= 'headphones'
		get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")


