

import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score

import datasets


a		="""\
    @inproceedings{kakwani2020indicnlpsuite,
    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
    year={2020},
    booktitle={Findings of EMNLP},
}
"""

a		="""\
    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide
    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.
"""

a		="""
Compute IndicGLUE evaluation metric associated to each IndicGLUE dataset.
Args:
    predictions: list of predictions to score (as int64),
        except for 'cvit-mkb-clsr' where each prediction is a vector (of float32).
    references: list of ground truth labels corresponding to the predictions (as int64),
        except for 'cvit-mkb-clsr' where each reference is a vector (of float32).
Returns: depending on the IndicGLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"precision\": Precision@10
Examples:

    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wnli')  # 'wnli' or any of [\"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wiki-ner')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'cvit-mkb-clsr')
    >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
    >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'precision@10': 1.0}

"""






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__			)  ->     List[str]:
	return float((preds == labels).mean()			)






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__			)  ->     List[str]:
	__lowerCamelCase				:				Optional[Any]					       = simple_accuracy(lowerCamelCase__	, lowerCamelCase__			)
	__lowerCamelCase				:				Tuple					       = float(fa_score(y_true=lowerCamelCase__	, y_pred=lowerCamelCase__			)			)
	return {
	    "accuracy": acc,
	    "f1": fa,
	}






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__			)  ->     Optional[Any]:
	__lowerCamelCase				:				Any					       = np.array(lowerCamelCase__			)
	__lowerCamelCase				:				List[Any]					       = np.array(lowerCamelCase__			)
	__lowerCamelCase				:				Any					       = en_sentvecs.shape[0]

	# mean centering
	__lowerCamelCase				:				Union[str, Any]					       = en_sentvecs - np.mean(lowerCamelCase__	, axis=0			)
	__lowerCamelCase				:				Dict					       = in_sentvecs - np.mean(lowerCamelCase__	, axis=0			)

	__lowerCamelCase				:				Optional[int]					       = cdist(lowerCamelCase__	, lowerCamelCase__	, 'cosine'			)
	__lowerCamelCase				:				Optional[Any]					       = np.array(range(lowerCamelCase__			)			)
	__lowerCamelCase				:				Dict					       = sim.argsort(axis=1			)[:, :1_0]
	__lowerCamelCase				:				Optional[int]					       = np.any(preds == actual[:, None]	, axis=1			)
	return float(matches.mean()			)






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,							_KWARGS_DESCRIPTION				)
class        A_		(       datasets.Metric				):
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								if self.config_name not in [
								    "wnli",
								    "copa",
								    "sna",
								    "csqa",
								    "wstp",
								    "inltkh",
								    "bbca",
								    "cvit-mkb-clsr",
								    "iitp-mr",
								    "iitp-pr",
								    "actsa-sc",
								    "md",
								    "wiki-ner",
								]:
									raise KeyError(
									    'You should supply a configuration name selected in '
									    '["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
									    '"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
									    '"wiki-ner"]')
								return datasets.MetricInfo(
								    description=_DESCRIPTION   ,citation=_CITATION   ,inputs_description=_KWARGS_DESCRIPTION   ,features=datasets.Features(
								        {
								            'predictions': datasets.Value('int64')
								            if self.config_name != 'cvit-mkb-clsr'
								            else datasets.Sequence(datasets.Value('float32')),
								            'references': datasets.Value('int64')
								            if self.config_name != 'cvit-mkb-clsr'
								            else datasets.Sequence(datasets.Value('float32')),
								        })   ,codebase_urls=[]   ,reference_urls=[]   ,format='numpy' if self.config_name != 'cvit-mkb-clsr' else None   ,)


							def     lowerCAmelCase     (		self		:			Any   ,SCREAMING_SNAKE_CASE__		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]):
								if self.config_name == "cvit-mkb-clsr":
									return {"precision@10": precision_at_aa(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)}
								elif self.config_name in ["wiki-ner"]:
									return acc_and_fa(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)
								elif self.config_name in [
								    "wnli",
								    "copa",
								    "sna",
								    "csqa",
								    "wstp",
								    "inltkh",
								    "bbca",
								    "iitp-mr",
								    "iitp-pr",
								    "actsa-sc",
								    "md",
								]:
									return {"accuracy": simple_accuracy(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)}
								else:
									raise KeyError(
									    'You should supply a configuration name selected in '
									    '["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
									    '"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
									    '"wiki-ner"]')











"""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""")


