


"""simple docstring"""


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					Optional[int]          =(DEISMultistepScheduler,)
							__UpperCAmelCase		:					Tuple          =(("""num_inference_steps""", 2_5),)

							def     snake_case       (  self					,     **__a			):
										__lowerCAmelCase       					=    {
										    "num_train_timesteps": 10_00,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "beta_schedule": "linear",
										    "solver_order": 2,
										}

										config.update(**__a			)
										return config

							def     snake_case       (  self					,     __a=0					,     **__a			):
										__lowerCAmelCase       					=    dict(self.forward_default_kwargs			)
										__lowerCAmelCase       					=    kwargs.pop("num_inference_steps"					,     __a			)
										__lowerCAmelCase       					=    self.dummy_sample
										__lowerCAmelCase       					=    0.1 * sample
										__lowerCAmelCase       					=    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

										for scheduler_class in self.scheduler_classes:
													__lowerCAmelCase       					=    self.get_scheduler_config(**__a			)
													__lowerCAmelCase       					=    scheduler_class(**__a			)
													scheduler.set_timesteps(__a			)
													# copy over dummy past residuals
													__lowerCAmelCase       					=    dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(__a			)
																__lowerCAmelCase       					=    scheduler_class.from_pretrained(__a			)
																new_scheduler.set_timesteps(__a			)
																# copy over dummy past residuals
																__lowerCAmelCase       					=    dummy_past_residuals[: new_scheduler.config.solver_order]

													__lowerCAmelCase					,			__lowerCAmelCase       					=    sample, sample
													for t in range(__a					,     time_step + scheduler.config.solver_order + 1			):
																__lowerCAmelCase       					=    scheduler.step(__a					,     __a					,     __a					,     **__a			).prev_sample
																__lowerCAmelCase       					=    new_scheduler.step(__a					,     __a					,     __a					,     **__a			).prev_sample

																assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"

							def     snake_case       (  self			):
										pass

							def     snake_case       (  self					,     __a=0					,     **__a			):
										__lowerCAmelCase       					=    dict(self.forward_default_kwargs			)
										__lowerCAmelCase       					=    kwargs.pop("num_inference_steps"					,     __a			)
										__lowerCAmelCase       					=    self.dummy_sample
										__lowerCAmelCase       					=    0.1 * sample
										__lowerCAmelCase       					=    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

										for scheduler_class in self.scheduler_classes:
													__lowerCAmelCase       					=    self.get_scheduler_config()
													__lowerCAmelCase       					=    scheduler_class(**__a			)
													scheduler.set_timesteps(__a			)

													# copy over dummy past residuals (must be after setting timesteps)
													__lowerCAmelCase       					=    dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(__a			)
																__lowerCAmelCase       					=    scheduler_class.from_pretrained(__a			)
																# copy over dummy past residuals
																new_scheduler.set_timesteps(__a			)

																# copy over dummy past residual (must be after setting timesteps)
																__lowerCAmelCase       					=    dummy_past_residuals[: new_scheduler.config.solver_order]

													__lowerCAmelCase       					=    scheduler.step(__a					,     __a					,     __a					,     **__a			).prev_sample
													__lowerCAmelCase       					=    new_scheduler.step(__a					,     __a					,     __a					,     **__a			).prev_sample

													assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"

							def     snake_case       (  self					,     __a=None					,     **__a			):
										if scheduler is None:
													__lowerCAmelCase       					=    self.scheduler_classes[0]
													__lowerCAmelCase       					=    self.get_scheduler_config(**__a			)
													__lowerCAmelCase       					=    scheduler_class(**__a			)

										__lowerCAmelCase       					=    self.scheduler_classes[0]
										__lowerCAmelCase       					=    self.get_scheduler_config(**__a			)
										__lowerCAmelCase       					=    scheduler_class(**__a			)

										__lowerCAmelCase       					=    10
										__lowerCAmelCase       					=    self.dummy_model()
										__lowerCAmelCase       					=    self.dummy_sample_deter
										scheduler.set_timesteps(__a			)

										for i, t in enumerate(scheduler.timesteps			):
													__lowerCAmelCase       					=    model(__a					,     __a			)
													__lowerCAmelCase       					=    scheduler.step(__a					,     __a					,     __a			).prev_sample

										return sample

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    dict(self.forward_default_kwargs			)

										__lowerCAmelCase       					=    kwargs.pop("num_inference_steps"					,     __a			)

										for scheduler_class in self.scheduler_classes:
													__lowerCAmelCase       					=    self.get_scheduler_config()
													__lowerCAmelCase       					=    scheduler_class(**__a			)

													__lowerCAmelCase       					=    self.dummy_sample
													__lowerCAmelCase       					=    0.1 * sample

													if num_inference_steps is not None and hasattr(__a					,     "set_timesteps"			):
																scheduler.set_timesteps(__a			)
													elif num_inference_steps is not None and not hasattr(__a					,     "set_timesteps"			):
																__lowerCAmelCase       					=    num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													__lowerCAmelCase       					=    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
													__lowerCAmelCase       					=    dummy_past_residuals[: scheduler.config.solver_order]

													__lowerCAmelCase       					=    scheduler.timesteps[5]
													__lowerCAmelCase       					=    scheduler.timesteps[6]

													__lowerCAmelCase       					=    scheduler.step(__a					,     __a					,     __a					,     **__a			).prev_sample
													__lowerCAmelCase       					=    scheduler.step(__a					,     __a					,     __a					,     **__a			).prev_sample

													self.assertEqual(output_a.shape					,     sample.shape			)
													self.assertEqual(output_a.shape					,     output_a.shape			)

							def     snake_case       (  self			):
										# make sure that iterating over schedulers with same config names gives same results
										# for defaults
										__lowerCAmelCase       					=    DEISMultistepScheduler(**self.get_scheduler_config()			)
										__lowerCAmelCase       					=    self.full_loop(scheduler=__a			)
										__lowerCAmelCase       					=    torch.mean(torch.abs(__a			)			)

										assert abs(result_mean.item() - 0.2_3_9_1_6			) < 1e-3

										__lowerCAmelCase       					=    DPMSolverSinglestepScheduler.from_config(scheduler.config			)
										__lowerCAmelCase       					=    DPMSolverMultistepScheduler.from_config(scheduler.config			)
										__lowerCAmelCase       					=    UniPCMultistepScheduler.from_config(scheduler.config			)
										__lowerCAmelCase       					=    DEISMultistepScheduler.from_config(scheduler.config			)

										__lowerCAmelCase       					=    self.full_loop(scheduler=__a			)
										__lowerCAmelCase       					=    torch.mean(torch.abs(__a			)			)

										assert abs(result_mean.item() - 0.2_3_9_1_6			) < 1e-3

							def     snake_case       (  self			):
										for timesteps in [25, 50, 1_00, 9_99, 10_00]:
													self.check_over_configs(num_train_timesteps=__a			)

							def     snake_case       (  self			):
										self.check_over_configs(thresholding=__a			)
										for order in [1, 2, 3]:
													for solver_type in ["logrho"]:
																for threshold in [0.5, 1.0, 2.0]:
																			for prediction_type in ["epsilon", "sample"]:
																						self.check_over_configs(
																						    thresholding=__a					,     prediction_type=__a					,     sample_max_value=__a					,     algorithm_type="deis"					,     solver_order=__a					,     solver_type=__a					,     )

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

							def     snake_case       (  self			):
										for algorithm_type in ["deis"]:
													for solver_type in ["logrho"]:
																for order in [1, 2, 3]:
																			for prediction_type in ["epsilon", "sample"]:
																						self.check_over_configs(
																						    solver_order=__a					,     solver_type=__a					,     prediction_type=__a					,     algorithm_type=__a					,     )
																						__lowerCAmelCase       					=    self.full_loop(
																						    solver_order=__a					,     solver_type=__a					,     prediction_type=__a					,     algorithm_type=__a					,     )
																						assert not torch.isnan(__a			).any(), "Samples have nan numbers"

							def     snake_case       (  self			):
										self.check_over_configs(lower_order_final=__a			)
										self.check_over_configs(lower_order_final=__a			)

							def     snake_case       (  self			):
										for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
													self.check_over_forward(num_inference_steps=__a					,     time_step=0			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.full_loop()
										__lowerCAmelCase       					=    torch.mean(torch.abs(__a			)			)

										assert abs(result_mean.item() - 0.2_3_9_1_6			) < 1e-3

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.full_loop(prediction_type="v_prediction"			)
										__lowerCAmelCase       					=    torch.mean(torch.abs(__a			)			)

										assert abs(result_mean.item() - 0.0_9_1			) < 1e-3



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.scheduler_classes[0]
										__lowerCAmelCase       					=    self.get_scheduler_config(thresholding=__a					,     dynamic_thresholding_ratio=0			)
										__lowerCAmelCase       					=    scheduler_class(**__a			)

										__lowerCAmelCase       					=    10
										__lowerCAmelCase       					=    self.dummy_model()
										__lowerCAmelCase       					=    self.dummy_sample_deter.half()
										scheduler.set_timesteps(__a			)

										for i, t in enumerate(scheduler.timesteps			):
													__lowerCAmelCase       					=    model(__a					,     __a			)
													__lowerCAmelCase       					=    scheduler.step(__a					,     __a					,     __a			).prev_sample

										assert sample.dtype == torch.floataa








"""simple docstring"""


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

import requests
from bsa import BeautifulSoup

A  :     Union[str, 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 _lowerCamelCase			(	_UpperCamelCase = "dhaka"					,				_UpperCamelCase = 5   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    min(_UpperCamelCase					,				50   )  # Prevent abuse!
			__lowerCAmelCase       					=    {
			    "q": query,
			    "tbm": "isch",
			    "hl": "en",
			    "ijn": "0",
			}

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

			__lowerCAmelCase       					=    json.dumps(_UpperCamelCase   )
			__lowerCAmelCase       					=    json.loads(_UpperCamelCase   )

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

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

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


if __name__ == "__main__":
	try:
		A  :     Any    					=    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




