
import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

_UpperCAmelCase      :				Any          = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
    " (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"
}


def    A (      lowercase = "dhaka"					, lowercase = 5				)     ->						int:
		'''simple docstring'''

		UpperCamelCase						=  min(lowercase					, 50				)  # Prevent abuse!
		UpperCamelCase						=  {
		    'q': query,
		    'tbm': 'isch',
		    'hl': 'en',
		    'ijn': '0',
		}

		UpperCamelCase						=  requests.get('https://www.google.com/search'					, params=lowercase					, headers=lowercase				)
		UpperCamelCase						=  BeautifulSoup(html.text					, 'html.parser'				)
		UpperCamelCase						=  ''.join(
		    re.findall(R'AF_initDataCallback\(([^<]+)\);'					, str(soup.select('script'				)				)				)				)

		UpperCamelCase						=  json.dumps(lowercase				)
		UpperCamelCase						=  json.loads(lowercase				)

		UpperCamelCase						=  re.findall(
		    R'\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",'					, lowercase					, )
		if not matched_google_image_data:
				return 0

		UpperCamelCase						=  re.sub(
		    R'\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]'					, ''					, str(lowercase				)					, )

		UpperCamelCase						=  re.findall(
		    R'(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]'					, lowercase					, )
		for index, fixed_full_res_image in enumerate(lowercase				):
				if index >= max_images:
						return index
				UpperCamelCase						=  bytes(lowercase					, 'ascii'				).decode(
				    'unicode-escape'				)
				UpperCamelCase						=  bytes(lowercase					, 'ascii'				).decode(
				    'unicode-escape'				)
				UpperCamelCase						=  urllib.request.build_opener()
				UpperCamelCase						=  [
				    (
				        'User-Agent',
				        'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
				        ' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582',
				    )
				]
				urllib.request.install_opener(lowercase				)
				UpperCamelCase						=  f'''query_{query.replace(' '					, '_'				)}'''
				if not os.path.exists(lowercase				):
						os.makedirs(lowercase				)
				urllib.request.urlretrieve(  # noqa: S310
				    lowercase					, f'''{path_name}/original_size_img_{index}.jpg'''				)
		return index


if __name__ == "__main__":
					try:
										_UpperCAmelCase      :				str          = download_images_from_google_query(sys.argv[1])
										print(F'''{image_count} images were downloaded to disk.''')
					except IndexError:
										print("Please provide a search term.")
										raise


from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


_UpperCAmelCase      :				Any          = "\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n"

_UpperCAmelCase      :				str          = "\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n"

_UpperCAmelCase      :				List[str]          = "\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"pearson\": Pearson Correlation\n    \"spearmanr\": Spearman Correlation\n    \"matthews_correlation\": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'stsb')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n    {'pearson': 1.0, 'spearmanr': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'cola')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"


def    A (      lowercase					, lowercase				)     ->						List[str]:
		'''simple docstring'''

		return float((preds == labels).mean()				)


def    A (      lowercase					, lowercase				)     ->						Tuple:
		'''simple docstring'''

		UpperCamelCase						=  simple_accuracy(lowercase					, lowercase				)
		UpperCamelCase						=  float(fa_score(y_true=lowercase					, y_pred=lowercase				)				)
		return {
		    "accuracy": acc,
		    "f1": fa,
		}


def    A (      lowercase					, lowercase				)     ->						Optional[int]:
		'''simple docstring'''

		UpperCamelCase						=  float(pearsonr(lowercase					, lowercase				)[0]				)
		UpperCamelCase						=  float(spearmanr(lowercase					, lowercase				)[0]				)
		return {
		    "pearson": pearson_corr,
		    "spearmanr": spearman_corr,
		}


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,     _KWARGS_DESCRIPTION     )
class        lowercase      (	datasets.Metric     ):



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				if self.config_name not in [
				    "sst2",
				    "mnli",
				    "mnli_mismatched",
				    "mnli_matched",
				    "cola",
				    "stsb",
				    "mrpc",
				    "qqp",
				    "qnli",
				    "rte",
				    "wnli",
				    "hans",
				]:
						raise KeyError(
						    'You should supply a configuration name selected in '
						    '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
						    '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]' )
				return datasets.MetricInfo(
				    description=_DESCRIPTION       ,   citation=_CITATION       ,   inputs_description=_KWARGS_DESCRIPTION       ,   features=datasets.Features(
				        {
				            'predictions': datasets.Value('int64' if self.config_name != 'stsb' else 'float32' ),
				            'references': datasets.Value('int64' if self.config_name != 'stsb' else 'float32' ),
				        } )       ,   codebase_urls=[]       ,   reference_urls=[]       ,   format='numpy'       ,   )



		def __UpperCamelCase				(				self       ,   A_       ,   A_ )						->      Any:
				"""simple docstring"""
				if self.config_name == "cola":
						return {"matthews_correlation": matthews_corrcoef(A_       ,   A_ )}
				elif self.config_name == "stsb":
						return pearson_and_spearman(A_       ,   A_ )
				elif self.config_name in ["mrpc", "qqp"]:
						return acc_and_fa(A_       ,   A_ )
				elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
						return {"accuracy": simple_accuracy(A_       ,   A_ )}
				else:
						raise KeyError(
						    'You should supply a configuration name selected in '
						    '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
						    '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]' )
