




"""simple docstring"""






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

import requests
from bsa import BeautifulSoup

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'''
}

def 							UpperCAmelCase		(		snake_case			:					str = "dhaka"      ,       snake_case			:					int = 5    ):
					_lowerCAmelCase:Any									=  min(snake_case      ,       50    )  # Prevent abuse!
					_lowerCAmelCase:List[Any]									=  {
					    '''q''': query,
					    '''tbm''': '''isch''',
					    '''hl''': '''en''',
					    '''ijn''': '''0''',
					}

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

					_lowerCAmelCase:Dict									=  json.dumps(snake_case    )
					_lowerCAmelCase:Union[str, Any]									=  json.loads(snake_case    )

					_lowerCAmelCase:Optional[int]									=  re.findall(
					    R'''\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",'''      ,       snake_case      ,       )
					if not matched_google_image_data:
										return 0

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

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


if __name__ == "__main__":
				try:
								UpperCamelCase__						=			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"""






import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class   a__			(       UpperCamelCase_  ):
						snake_case__             = (KDPMaDiscreteScheduler,)
						snake_case__             = 1_0





						def       __UpperCamelCase     (							self      :	Dict     ,**a__      :	str)						->       int:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  {
											    '''num_train_timesteps''': 1100,
											    '''beta_start''': 0.0001,
											    '''beta_end''': 0.02,
											    '''beta_schedule''': '''linear''',
											}

											config.update(**a__)
											return config





						def       __UpperCamelCase     (							self      :	Optional[int])						->       Optional[int]:




											"""simple docstring"""







											for timesteps in [10, 50, 100, 1000]:
																self.check_over_configs(num_train_timesteps=a__)





						def       __UpperCamelCase     (							self      :	Union[str, Any])						->       Optional[int]:




											"""simple docstring"""







											for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]     ,[0.0002, 0.002, 0.02]):
																self.check_over_configs(beta_start=a__     ,beta_end=a__)





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       Union[str, Any]:




											"""simple docstring"""







											for schedule in ["linear", "scaled_linear"]:
																self.check_over_configs(beta_schedule=a__)





						def       __UpperCamelCase     (							self      :	str)						->       str:




											"""simple docstring"""







											for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(prediction_type=a__)





						def       __UpperCamelCase     (							self      :	Tuple)						->       Tuple:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  self.scheduler_classes[0]
											_lowerCAmelCase:Union[str, Any]									=  self.get_scheduler_config(prediction_type='''v_prediction''')
											_lowerCAmelCase:Tuple									=  scheduler_class(**a__)

											scheduler.set_timesteps(self.num_inference_steps)

											_lowerCAmelCase:Optional[Any]									=  self.dummy_model()
											_lowerCAmelCase:List[Any]									=  self.dummy_sample_deter * scheduler.init_noise_sigma
											_lowerCAmelCase:Optional[Any]									=  sample.to(a__)

											for i, t in enumerate(scheduler.timesteps):
																_lowerCAmelCase:Tuple									=  scheduler.scale_model_input(a__     ,a__)

																_lowerCAmelCase:Any									=  model(a__     ,a__)

																_lowerCAmelCase:Union[str, Any]									=  scheduler.step(a__     ,a__     ,a__)
																_lowerCAmelCase:Optional[int]									=  output.prev_sample

											_lowerCAmelCase:str									=  torch.sum(torch.abs(a__))
											_lowerCAmelCase:Union[str, Any]									=  torch.mean(torch.abs(a__))

											if torch_device in ["cpu", "mps"]:
																assert abs(result_sum.item() - 4.6_934E-07) < 1E-2
																assert abs(result_mean.item() - 6.1_112E-10) < 1E-3
											else:
																# CUDA
																assert abs(result_sum.item() - 4.693_428_650_170_972E-07) < 1E-2
																assert abs(result_mean.item() - 0.0002) < 1E-3





						def       __UpperCamelCase     (							self      :	Tuple)						->       Dict:




											"""simple docstring"""







											if torch_device == "mps":
																return
											_lowerCAmelCase:Tuple									=  self.scheduler_classes[0]
											_lowerCAmelCase:List[str]									=  self.get_scheduler_config()
											_lowerCAmelCase:Union[str, Any]									=  scheduler_class(**a__)

											scheduler.set_timesteps(self.num_inference_steps)

											_lowerCAmelCase:Any									=  self.dummy_model()
											_lowerCAmelCase:str									=  self.dummy_sample_deter * scheduler.init_noise_sigma
											_lowerCAmelCase:List[str]									=  sample.to(a__)

											for i, t in enumerate(scheduler.timesteps):
																_lowerCAmelCase:Tuple									=  scheduler.scale_model_input(a__     ,a__)

																_lowerCAmelCase:List[str]									=  model(a__     ,a__)

																_lowerCAmelCase:int									=  scheduler.step(a__     ,a__     ,a__)
																_lowerCAmelCase:List[Any]									=  output.prev_sample

											_lowerCAmelCase:Tuple									=  torch.sum(torch.abs(a__))
											_lowerCAmelCase:Dict									=  torch.mean(torch.abs(a__))

											if torch_device in ["cpu", "mps"]:
																assert abs(result_sum.item() - 20.4125) < 1E-2
																assert abs(result_mean.item() - 0.0266) < 1E-3
											else:
																# CUDA
																assert abs(result_sum.item() - 20.4125) < 1E-2
																assert abs(result_mean.item() - 0.0266) < 1E-3







						def       __UpperCamelCase     (							self      :	Union[str, Any])						->       List[Any]:




											"""simple docstring"""







											if torch_device == "mps":
																return
											_lowerCAmelCase:str									=  self.scheduler_classes[0]
											_lowerCAmelCase:Optional[Any]									=  self.get_scheduler_config()
											_lowerCAmelCase:Any									=  scheduler_class(**a__)

											scheduler.set_timesteps(self.num_inference_steps     ,device=a__)

											_lowerCAmelCase:Optional[int]									=  self.dummy_model()
											_lowerCAmelCase:Optional[Any]									=  self.dummy_sample_deter.to(a__) * scheduler.init_noise_sigma

											for t in scheduler.timesteps:
																_lowerCAmelCase:Union[str, Any]									=  scheduler.scale_model_input(a__     ,a__)

																_lowerCAmelCase:Tuple									=  model(a__     ,a__)

																_lowerCAmelCase:str									=  scheduler.step(a__     ,a__     ,a__)
																_lowerCAmelCase:Dict									=  output.prev_sample

											_lowerCAmelCase:Any									=  torch.sum(torch.abs(a__))
											_lowerCAmelCase:str									=  torch.mean(torch.abs(a__))

											if str(a__).startswith('''cpu'''):
																# The following sum varies between 148 and 156 on mps. Why?
																assert abs(result_sum.item() - 20.4125) < 1E-2
																assert abs(result_mean.item() - 0.0266) < 1E-3
											else:
																# CUDA
																assert abs(result_sum.item() - 20.4125) < 1E-2
																assert abs(result_mean.item() - 0.0266) < 1E-3






