

'''simple docstring'''
import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

_lowerCamelCase      					=     {
    """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__       (    _SCREAMING_SNAKE_CASE   :       str = "dhaka"	,				_SCREAMING_SNAKE_CASE   :       int = 5				)     ->					int:


	"""simple docstring"""





	UpperCAmelCase_     :      Any						=  min(_SCREAMING_SNAKE_CASE	,				50				)  # Prevent abuse!
	UpperCAmelCase_     :      Optional[int]						=  {
	    "q": query,
	    "tbm": "isch",
	    "hl": "en",
	    "ijn": "0",
	}

	UpperCAmelCase_     :      int						=  requests.get("https://www.google.com/search"	,				params=_SCREAMING_SNAKE_CASE	,				headers=_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      Optional[Any]						=  BeautifulSoup(html.text	,				"html.parser"				)
	UpperCAmelCase_     :      Optional[int]						=  "".join(
	    re.findall(r"AF_initDataCallback\(([^<]+)\);"	,				str(soup.select("script"				)				)				)				)

	UpperCAmelCase_     :      List[str]						=  json.dumps(_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      Optional[int]						=  json.loads(_SCREAMING_SNAKE_CASE				)

	UpperCAmelCase_     :      Dict						=  re.findall(
	    r"\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","	,				_SCREAMING_SNAKE_CASE	,				)
	if not matched_google_image_data:
		return 0

	UpperCAmelCase_     :      Optional[Any]						=  re.sub(
	    r"\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]"	,				""	,				str(_SCREAMING_SNAKE_CASE				)	,				)

	UpperCAmelCase_     :      Dict						=  re.findall(
	    r"(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]"	,				_SCREAMING_SNAKE_CASE	,				)
	for index, fixed_full_res_image in enumerate(_SCREAMING_SNAKE_CASE				):
		if index >= max_images:
			return index
		UpperCAmelCase_     :      str						=  bytes(_SCREAMING_SNAKE_CASE	,				"ascii"				).decode(
		    "unicode-escape"				)
		UpperCAmelCase_     :      Any						=  bytes(_SCREAMING_SNAKE_CASE	,				"ascii"				).decode(
		    "unicode-escape"				)
		UpperCAmelCase_     :      int						=  urllib.request.build_opener()
		UpperCAmelCase_     :      Dict						=  [
		    (
		        "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(_SCREAMING_SNAKE_CASE				)
		UpperCAmelCase_     :      Union[str, Any]						=  F'''query_{query.replace(" "	,				"_"				)}'''
		if not os.path.exists(_SCREAMING_SNAKE_CASE				):
			os.makedirs(_SCREAMING_SNAKE_CASE				)
		urllib.request.urlretrieve(  # noqa: S310
		    _SCREAMING_SNAKE_CASE	,				F'''{path_name}/original_size_img_{index}.jpg'''				)
	return index


if __name__ == "__main__":
					try:
										_lowerCamelCase      					=     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









'''simple docstring'''





from sklearn.metrics import mean_squared_error

import datasets


SCREAMING_SNAKE_CASE__          =  "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"

SCREAMING_SNAKE_CASE__          =  "\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"


SCREAMING_SNAKE_CASE__          =  "\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric(\"mse\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {'mse': 0.6123724356957945}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mse': array([0.41666667, 1. ])}\n"


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



       def 				_a   (					self   )	->				Optional[int]:
           return datasets.MetricInfo(
               description=_DESCRIPTION  ,citation=_CITATION  ,inputs_description=_KWARGS_DESCRIPTION  ,features=datasets.Features(self._get_feature_types()   )  ,reference_urls=[
                   "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
               ]  ,)



       def 				_a   (					self   )	->				Tuple:
           if self.config_name == "multilist":
               return {
                   "predictions": datasets.Sequence(datasets.Value("float"   )   ),
                   "references": datasets.Sequence(datasets.Value("float"   )   ),
               }
           else:
               return {
                   "predictions": datasets.Value("float"   ),
                   "references": datasets.Value("float"   ),
               }







       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_=None  ,UpperCAmelCase_="uniform_average"  ,UpperCAmelCase_=True   )	->				Tuple:
           lowercase__									=    mean_squared_error(
               UpperCAmelCase_  ,UpperCAmelCase_  ,sample_weight=UpperCAmelCase_  ,multioutput=UpperCAmelCase_  ,squared=UpperCAmelCase_   )

           return {"mse": mse}




