

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  (DDIMParallelScheduler,)
			__A						  =  (("""eta""", 0.0), ("""num_inference_steps""", 5_0))


			def 			__lowerCAmelCase (    self ,				**__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= {
				    'num_train_timesteps': 10_00,
				    'beta_start': 0.0001,
				    'beta_end': 0.02,
				    'beta_schedule': 'linear',
				    'clip_sample': True,
				}

				config.update(**__UpperCamelCase							)
				return config


			def 			__lowerCAmelCase (    self ,				**__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.scheduler_classes[0]
				snake_case_      		= self.get_scheduler_config(**__UpperCamelCase							)
				snake_case_      		= scheduler_class(**__UpperCamelCase							)

				snake_case_		,       snake_case_      		= 10, 0.0

				snake_case_      		= self.dummy_model()
				snake_case_      		= self.dummy_sample_deter

				scheduler.set_timesteps(__UpperCamelCase							)

				for t in scheduler.timesteps:
					snake_case_      		= model(__UpperCamelCase ,				__UpperCamelCase							)
					snake_case_      		= scheduler.step(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							).prev_sample

				return sample


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for timesteps in [1_00, 5_00, 10_00]:
					self.check_over_configs(num_train_timesteps=__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for steps_offset in [0, 1]:
					self.check_over_configs(steps_offset=__UpperCamelCase							)

				snake_case_      		= self.scheduler_classes[0]
				snake_case_      		= self.get_scheduler_config(steps_offset=1							)
				snake_case_      		= scheduler_class(**__UpperCamelCase							)
				scheduler.set_timesteps(5							)
				assert torch.equal(scheduler.timesteps ,				torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]							)							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] ,				[0.002, 0.02, 0.2, 2]							):
					self.check_over_configs(beta_start=__UpperCamelCase ,				beta_end=__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for schedule in ["linear", "squaredcos_cap_v2"]:
					self.check_over_configs(beta_schedule=__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for clip_sample in [True, False]:
					self.check_over_configs(clip_sample=__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for timestep_spacing in ["trailing", "leading"]:
					self.check_over_configs(timestep_spacing=__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for rescale_betas_zero_snr in [True, False]:
					self.check_over_configs(rescale_betas_zero_snr=__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				self.check_over_configs(thresholding=__UpperCamelCase							)
				for threshold in [0.5, 1.0, 2.0]:
					for prediction_type in ["epsilon", "v_prediction"]:
						self.check_over_configs(
						    thresholding=__UpperCamelCase ,				prediction_type=__UpperCamelCase ,				sample_max_value=__UpperCamelCase ,				)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for t in [1, 10, 49]:
					self.check_over_forward(time_step=__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for t, num_inference_steps in zip([1, 10, 50] ,				[10, 50, 5_00]							):
					self.check_over_forward(time_step=__UpperCamelCase ,				num_inference_steps=__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for t, eta in zip([1, 10, 49] ,				[0.0, 0.5, 1.0]							):
					self.check_over_forward(time_step=__UpperCamelCase ,				eta=__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.scheduler_classes[0]
				snake_case_      		= self.get_scheduler_config()
				snake_case_      		= scheduler_class(**__UpperCamelCase							)

				assert torch.sum(torch.abs(scheduler._get_variance(0 ,				0							) - 0.0							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(4_20 ,				4_00							) - 0.1_4771							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(9_80 ,				9_60							) - 0.3_2460							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(0 ,				0							) - 0.0							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(4_87 ,				4_86							) - 0.0_0979							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(9_99 ,				9_98							) - 0.02							)							) < 1E-5


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.scheduler_classes[0]
				snake_case_      		= self.get_scheduler_config()
				snake_case_      		= scheduler_class(**__UpperCamelCase							)

				snake_case_		,       snake_case_      		= 10, 0.0
				scheduler.set_timesteps(__UpperCamelCase							)

				snake_case_      		= self.dummy_model()
				snake_case_      		= self.dummy_sample_deter
				snake_case_      		= self.dummy_sample_deter + 0.1
				snake_case_      		= self.dummy_sample_deter - 0.1

				snake_case_      		= samplea.shape[0]
				snake_case_      		= torch.stack([samplea, samplea, samplea] ,				dim=0							)
				snake_case_      		= torch.arange(__UpperCamelCase							)[0:3, None].repeat(1 ,				__UpperCamelCase							)

				snake_case_      		= model(samples.flatten(0 ,				1							) ,				timesteps.flatten(0 ,				1							)							)
				snake_case_      		= scheduler.batch_step_no_noise(__UpperCamelCase ,				timesteps.flatten(0 ,				1							) ,				samples.flatten(0 ,				1							) ,				__UpperCamelCase							)

				snake_case_      		= torch.sum(torch.abs(__UpperCamelCase							)							)
				snake_case_      		= torch.mean(torch.abs(__UpperCamelCase							)							)

				assert abs(result_sum.item() - 1147.7904							) < 1E-2
				assert abs(result_mean.item() - 0.4982							) < 1E-3


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.full_loop()

				snake_case_      		= torch.sum(torch.abs(__UpperCamelCase							)							)
				snake_case_      		= torch.mean(torch.abs(__UpperCamelCase							)							)

				assert abs(result_sum.item() - 172.0067							) < 1E-2
				assert abs(result_mean.item() - 0.22_3967							) < 1E-3


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.full_loop(prediction_type='v_prediction'							)

				snake_case_      		= torch.sum(torch.abs(__UpperCamelCase							)							)
				snake_case_      		= torch.mean(torch.abs(__UpperCamelCase							)							)

				assert abs(result_sum.item() - 52.5302							) < 1E-2
				assert abs(result_mean.item() - 0.0684							) < 1E-3


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.full_loop(set_alpha_to_one=__UpperCamelCase ,				beta_start=0.01							)
				snake_case_      		= torch.sum(torch.abs(__UpperCamelCase							)							)
				snake_case_      		= torch.mean(torch.abs(__UpperCamelCase							)							)

				assert abs(result_sum.item() - 149.8295							) < 1E-2
				assert abs(result_mean.item() - 0.1951							) < 1E-3







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.full_loop(set_alpha_to_one=__UpperCamelCase ,				beta_start=0.01							)
				snake_case_      		= torch.sum(torch.abs(__UpperCamelCase							)							)
				snake_case_      		= torch.mean(torch.abs(__UpperCamelCase							)							)

				assert abs(result_sum.item() - 149.0784							) < 1E-2
				assert abs(result_mean.item() - 0.1941							) < 1E-3



import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm





def a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= 1.5
	snake_case_      		= int(factor * num_class_images	)
	snake_case_      		= ClipClient(
	    url='https://knn.laion.ai/knn-service'			, indice_name='laion_400m'			, num_images=lowercase__			, aesthetic_weight=0.1	)

	os.makedirs(f"""{class_data_dir}/images"""			, exist_ok=lowercase__	)
	if len(list(Path(f"""{class_data_dir}/images"""	).iterdir()	)	) >= num_class_images:
		return

	while True:
		snake_case_      		= client.query(text=lowercase__	)
		if len(lowercase__	) >= factor * num_class_images or num_images > 1e4:
			break
		else:
			snake_case_      		= int(factor * num_images	)
			snake_case_      		= ClipClient(
			    url='https://knn.laion.ai/knn-service'			, indice_name='laion_400m'			, num_images=lowercase__			, aesthetic_weight=0.1			, )

	snake_case_      		= 0
	snake_case_      		= 0
	snake_case_      		= tqdm(desc='downloading real regularization images'			, total=lowercase__	)

	with open(f"""{class_data_dir}/caption.txt"""			, 'w'	) as fa, open(f"""{class_data_dir}/urls.txt"""			, 'w'	) as fa, open(
	    f"""{class_data_dir}/images.txt"""			, 'w'	) as fa:
		while total < num_class_images:
			snake_case_      		= class_images[count]
			count += 1
			try:
				snake_case_      		= requests.get(images['url']	)
				if img.status_code == 200:
					snake_case_      		= Image.open(BytesIO(img.content	)	)
					with open(f"""{class_data_dir}/images/{total}.jpg"""			, 'wb'	) as f:
						f.write(img.content	)
					fa.write(images['caption'] + '\n'	)
					fa.write(images['url'] + '\n'	)
					fa.write(f"""{class_data_dir}/images/{total}.jpg""" + '\n'	)
					total += 1
					pbar.update(1	)
				else:
					continue
			except Exception:
				continue
	return





def a():





	'''simple docstring'''
	snake_case_      		= argparse.ArgumentParser(''			, add_help=lowercase__	)
	parser.add_argument('--class_prompt'			, help='text prompt to retrieve images'			, required=lowercase__			, type=lowercase__	)
	parser.add_argument('--class_data_dir'			, help='path to save images'			, required=lowercase__			, type=lowercase__	)
	parser.add_argument('--num_class_images'			, help='number of images to download'			, default=200			, type=lowercase__	)
	return parser.parse_args()


if __name__ == "__main__":
		A											=     parse_args()
		retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
