





"""simple docstring"""
import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  args.log_outputs
							A		=  "_".join(args.dataset.split("/"       ) + [args.config, args.split]       )

							# load metric
							A		=  load_metric("wer"       )
							A		=  load_metric("cer"       )

							# compute metrics
							A		=  wer.compute(references=result["target"]					,  predictions=result["prediction"]       )
							A		=  cer.compute(references=result["target"]					,  predictions=result["prediction"]       )

							# print & log results
							A		=  F"WER: {wer_result}\nCER: {cer_result}"
							print(UpperCamelCase       )

							with open(F"{dataset_id}_eval_results.txt"					,  "w"       ) as f:
														f.write(UpperCamelCase       )

							# log all results in text file. Possibly interesting for analysis
							if log_outputs is not None:
														A		=  F"log_{dataset_id}_predictions.txt"
														A		=  F"log_{dataset_id}_targets.txt"

														with open(UpperCamelCase					,  "w"       ) as p, open(UpperCamelCase					,  "w"       ) as t:
																					# mapping function to write output
																					def write_to_file(UpperCamelCase					,  UpperCamelCase       ):
																												p.write(F"{i}" + "\n"       )
																												p.write(batch["prediction"] + "\n"       )
																												t.write(F"{i}" + "\n"       )
																												t.write(batch["target"] + "\n"       )

																					result.map(UpperCamelCase					,  with_indices=UpperCamelCase       )






def 					A__				(			UpperCamelCase       ):
							A		=  "[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

							A		=  re.sub(UpperCamelCase					,  ""					,  text.lower()       )

							# In addition, we can normalize the target text, e.g. removing new lines characters etc...
							# note that order is important here!
							A		=  ["\n\n", "\n", "   ", "  "]

							for t in token_sequences_to_ignore:
														A		=  " ".join(text.split(UpperCamelCase       )       )

							return text






def 					A__				(			UpperCamelCase       ):
							# load dataset
							A		=  load_dataset(args.dataset					,  args.config					,  split=args.split					,  use_auth_token=UpperCamelCase       )

							# for testing: only process the first two examples as a test
							# dataset = dataset.select(range(10))

							# load processor
							A		=  AutoFeatureExtractor.from_pretrained(args.model_id       )
							A		=  feature_extractor.sampling_rate

							# resample audio
							A		=  dataset.cast_column("audio"					,  Audio(sampling_rate=UpperCamelCase       )       )

							# load eval pipeline
							if args.device is None:
														A		=  0 if torch.cuda.is_available() else -1
							A		=  pipeline("automatic-speech-recognition"					,  model=args.model_id					,  device=args.device       )

							# map function to decode audio
							def map_to_pred(UpperCamelCase       ):
														A		=  asr(
														    batch["audio"]["array"]					,  chunk_length_s=args.chunk_length_s					,  stride_length_s=args.stride_length_s       )

														A		=  prediction["text"]
														A		=  normalize_text(batch["sentence"]       )
														return batch

							# run inference on all examples
							A		=  dataset.map(UpperCamelCase					,  remove_columns=dataset.column_names       )

							# compute and log_results
							# do not change function below
							log_results(UpperCamelCase					,  UpperCamelCase       )


if __name__ == "__main__":
		_snake_case     :		Optional[int]       						= argparse.ArgumentParser()

		parser.add_argument(
		    '--model_id', type=str, required=True, help='Model identifier. Should be loadable with 🤗 Transformers'
		)
		parser.add_argument(
		    '--dataset',
		    type=str,
		    required=True,
		    help='Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets',
		)
		parser.add_argument(
		    '--config', type=str, required=True, help='Config of the dataset. *E.g.* `\'en\'`  for Common Voice'
		)
		parser.add_argument('--split', type=str, required=True, help='Split of the dataset. *E.g.* `\'test\'`')
		parser.add_argument(
		    '--chunk_length_s', type=float, default=None, help='Chunk length in seconds. Defaults to 5 seconds.'
		)
		parser.add_argument(
		    '--stride_length_s', type=float, default=None, help='Stride of the audio chunks. Defaults to 1 second.'
		)
		parser.add_argument(
		    '--log_outputs', action='store_true', help='If defined, write outputs to log file for analysis.'
		)
		parser.add_argument(
		    '--device',
		    type=int,
		    default=None,
		    help='The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.',
		)
		_snake_case     :		Tuple       						= parser.parse_args()

		main(args)









"""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""")


