






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class     __SCREAMING_SNAKE_CASE					(   __lowercase):
						_SCREAMING_SNAKE_CASE	:       Dict   =					(DDIMParallelScheduler,)
						_SCREAMING_SNAKE_CASE	:       str   =					(('''eta''', 0.0), ('''num_inference_steps''', 50))






						def       UpperCamelCase__      (	self     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						{
												    'num_train_timesteps': 10_00,
												    'beta_start': 0.00_01,
												    'beta_end': 0.02,
												    'beta_schedule': 'linear',
												    'clip_sample': True,
												}

												config.update(**_UpperCamelCase   )
												return config






						def       UpperCamelCase__      (	self     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.scheduler_classes[0]
												lowerCAmelCase__    						=						self.get_scheduler_config(**_UpperCamelCase   )
												lowerCAmelCase__    						=						scheduler_class(**_UpperCamelCase   )

												lowerCAmelCase__						,       lowerCAmelCase__    						=						10, 0.0

												lowerCAmelCase__    						=						self.dummy_model()
												lowerCAmelCase__    						=						self.dummy_sample_deter

												scheduler.set_timesteps(_UpperCamelCase   )

												for t in scheduler.timesteps:
																		lowerCAmelCase__    						=						model(_UpperCamelCase     ,						_UpperCamelCase   )
																		lowerCAmelCase__    						=						scheduler.step(_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ).prev_sample

												return sample






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												for timesteps in [1_00, 5_00, 10_00]:
																		self.check_over_configs(num_train_timesteps=_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												for steps_offset in [0, 1]:
																		self.check_over_configs(steps_offset=_UpperCamelCase   )

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






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]     ,						[0.0_02, 0.02, 0.2, 2]   ):
																		self.check_over_configs(beta_start=_UpperCamelCase     ,						beta_end=_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






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






						def       UpperCamelCase__      (	self   ):






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






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






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






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												for rescale_betas_zero_snr in [True, False]:
																		self.check_over_configs(rescale_betas_zero_snr=_UpperCamelCase   )






						def       UpperCamelCase__      (	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       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												for t in [1, 10, 49]:
																		self.check_over_forward(time_step=_UpperCamelCase   )






						def       UpperCamelCase__      (	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       UpperCamelCase__      (	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       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.scheduler_classes[0]
												lowerCAmelCase__    						=						self.get_scheduler_config()
												lowerCAmelCase__    						=						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_47_71   )   ) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(9_80     ,						9_60   ) - 0.3_24_60   )   ) < 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_09_79   )   ) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(9_99     ,						9_98   ) - 0.02   )   ) < 1E-5






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.scheduler_classes[0]
												lowerCAmelCase__    						=						self.get_scheduler_config()
												lowerCAmelCase__    						=						scheduler_class(**_UpperCamelCase   )

												lowerCAmelCase__						,       lowerCAmelCase__    						=						10, 0.0
												scheduler.set_timesteps(_UpperCamelCase   )

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

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

												lowerCAmelCase__    						=						model(samples.flatten(0     ,						1   )     ,						timesteps.flatten(0     ,						1   )   )
												lowerCAmelCase__    						=						scheduler.batch_step_no_noise(_UpperCamelCase     ,						timesteps.flatten(0     ,						1   )     ,						samples.flatten(0     ,						1   )     ,						_UpperCamelCase   )

												lowerCAmelCase__    						=						torch.sum(torch.abs(_UpperCamelCase   )   )
												lowerCAmelCase__    						=						torch.mean(torch.abs(_UpperCamelCase   )   )

												assert abs(result_sum.item() - 11_47.79_04   ) < 1E-2
												assert abs(result_mean.item() - 0.49_82   ) < 1E-3






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.full_loop()

												lowerCAmelCase__    						=						torch.sum(torch.abs(_UpperCamelCase   )   )
												lowerCAmelCase__    						=						torch.mean(torch.abs(_UpperCamelCase   )   )

												assert abs(result_sum.item() - 1_72.00_67   ) < 1E-2
												assert abs(result_mean.item() - 0.22_39_67   ) < 1E-3






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.full_loop(prediction_type='v_prediction'   )

												lowerCAmelCase__    						=						torch.sum(torch.abs(_UpperCamelCase   )   )
												lowerCAmelCase__    						=						torch.mean(torch.abs(_UpperCamelCase   )   )

												assert abs(result_sum.item() - 52.53_02   ) < 1E-2
												assert abs(result_mean.item() - 0.06_84   ) < 1E-3






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												# We specify different beta, so that the first alpha is 0.99
												lowerCAmelCase__    						=						self.full_loop(set_alpha_to_one=_UpperCamelCase     ,						beta_start=0.01   )
												lowerCAmelCase__    						=						torch.sum(torch.abs(_UpperCamelCase   )   )
												lowerCAmelCase__    						=						torch.mean(torch.abs(_UpperCamelCase   )   )

												assert abs(result_sum.item() - 1_49.82_95   ) < 1E-2
												assert abs(result_mean.item() - 0.19_51   ) < 1E-3







						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												# We specify different beta, so that the first alpha is 0.99
												lowerCAmelCase__    						=						self.full_loop(set_alpha_to_one=_UpperCamelCase     ,						beta_start=0.01   )
												lowerCAmelCase__    						=						torch.sum(torch.abs(_UpperCamelCase   )   )
												lowerCAmelCase__    						=						torch.mean(torch.abs(_UpperCamelCase   )   )

												assert abs(result_sum.item() - 1_49.07_84   ) < 1E-2
												assert abs(result_mean.item() - 0.19_41   ) < 1E-3













import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
__snake_case    :    str         =     (7_20, 12_80)  # Height, Width
__snake_case    :    Dict         =     (0.4, 0.6)  # if height or width lower than this scale, drop it.
__snake_case    :    Dict         =     1 / 1_00
__snake_case    :    Optional[int]         =     """"""
__snake_case    :    Union[str, Any]         =     """"""
__snake_case    :    List[str]         =     """"""
__snake_case    :    List[str]         =     2_50







def 	_UpperCamelCase   (    )  ->   None:

						"""simple docstring"""



						lowerCAmelCase__						,       lowerCAmelCase__    						=						get_dataset(UpperCamelCase_    ,       UpperCamelCase_		)
						for index in range(UpperCamelCase_		):
												lowerCAmelCase__    						=						random.sample(range(len(UpperCamelCase_		)		)    ,       4		)
												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						update_image_and_anno(
												    UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       filter_scale=UpperCamelCase_    ,       )

												# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
												lowerCAmelCase__    						=						random_chars(32		)
												lowerCAmelCase__    						=						path.split(os.sep		)[-1].rsplit('.'    ,       1		)[0]
												lowerCAmelCase__    						=						F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
												cva.imwrite(F"{file_root}.jpg"    ,       UpperCamelCase_    ,       [cva.IMWRITE_JPEG_QUALITY, 85]		)
												print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"		)
												lowerCAmelCase__    						=						[]
												for anno in new_annos:
																		lowerCAmelCase__    						=						anno[3] - anno[1]
																		lowerCAmelCase__    						=						anno[4] - anno[2]
																		lowerCAmelCase__    						=						anno[1] + width / 2
																		lowerCAmelCase__    						=						anno[2] + height / 2
																		lowerCAmelCase__    						=						F"{anno[0]} {x_center} {y_center} {width} {height}"
																		annos_list.append(UpperCamelCase_		)
												with open(F"{file_root}.txt"    ,       'w'		) as outfile:
																		outfile.write('\n'.join(line for line in annos_list		)		)







def 	_UpperCamelCase   (    UpperCamelCase_       :					str    ,       UpperCamelCase_       :					str		)  ->   tuple[list, list]:

						"""simple docstring"""



						lowerCAmelCase__    						=						[]
						lowerCAmelCase__    						=						[]
						for label_file in glob.glob(os.path.join(UpperCamelCase_    ,       '*.txt'		)		):
												lowerCAmelCase__    						=						label_file.split(os.sep		)[-1].rsplit('.'    ,       1		)[0]
												with open(UpperCamelCase_		) as in_file:
																		lowerCAmelCase__    						=						in_file.readlines()
												lowerCAmelCase__    						=						os.path.join(UpperCamelCase_    ,       F"{label_name}.jpg"		)

												lowerCAmelCase__    						=						[]
												for obj_list in obj_lists:
																		lowerCAmelCase__    						=						obj_list.rstrip('\n'		).split(' '		)
																		lowerCAmelCase__    						=						float(obj[1]		) - float(obj[3]		) / 2
																		lowerCAmelCase__    						=						float(obj[2]		) - float(obj[4]		) / 2
																		lowerCAmelCase__    						=						float(obj[1]		) + float(obj[3]		) / 2
																		lowerCAmelCase__    						=						float(obj[2]		) + float(obj[4]		) / 2

																		boxes.append([int(obj[0]		), xmin, ymin, xmax, ymax]		)
												if not boxes:
																		continue
												img_paths.append(UpperCamelCase_		)
												labels.append(UpperCamelCase_		)
						return img_paths, labels







def 	_UpperCamelCase   (    UpperCamelCase_       :					list    ,       UpperCamelCase_       :					list    ,       UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					tuple[int, int]    ,       UpperCamelCase_       :					tuple[float, float]    ,       UpperCamelCase_       :					float = 0.0    ,       )  ->   tuple[list, list, str]:

						"""simple docstring"""



						lowerCAmelCase__    						=						np.zeros([output_size[0], output_size[1], 3]    ,       dtype=np.uinta		)
						lowerCAmelCase__    						=						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						lowerCAmelCase__    						=						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						lowerCAmelCase__    						=						int(scale_x * output_size[1]		)
						lowerCAmelCase__    						=						int(scale_y * output_size[0]		)

						lowerCAmelCase__    						=						[]
						lowerCAmelCase__    						=						[]
						for i, index in enumerate(UpperCamelCase_		):
												lowerCAmelCase__    						=						all_img_list[index]
												path_list.append(UpperCamelCase_		)
												lowerCAmelCase__    						=						all_annos[index]
												lowerCAmelCase__    						=						cva.imread(UpperCamelCase_		)
												if i == 0:  # top-left
																		lowerCAmelCase__    						=						cva.resize(UpperCamelCase_    ,       (divid_point_x, divid_point_y)		)
																		lowerCAmelCase__    						=						img
																		for bbox in img_annos:
																								lowerCAmelCase__    						=						bbox[1] * scale_x
																								lowerCAmelCase__    						=						bbox[2] * scale_y
																								lowerCAmelCase__    						=						bbox[3] * scale_x
																								lowerCAmelCase__    						=						bbox[4] * scale_y
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
												elif i == 1:  # top-right
																		lowerCAmelCase__    						=						cva.resize(UpperCamelCase_    ,       (output_size[1] - divid_point_x, divid_point_y)		)
																		lowerCAmelCase__    						=						img
																		for bbox in img_annos:
																								lowerCAmelCase__    						=						scale_x + bbox[1] * (1 - scale_x)
																								lowerCAmelCase__    						=						bbox[2] * scale_y
																								lowerCAmelCase__    						=						scale_x + bbox[3] * (1 - scale_x)
																								lowerCAmelCase__    						=						bbox[4] * scale_y
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
												elif i == 2:  # bottom-left
																		lowerCAmelCase__    						=						cva.resize(UpperCamelCase_    ,       (divid_point_x, output_size[0] - divid_point_y)		)
																		lowerCAmelCase__    						=						img
																		for bbox in img_annos:
																								lowerCAmelCase__    						=						bbox[1] * scale_x
																								lowerCAmelCase__    						=						scale_y + bbox[2] * (1 - scale_y)
																								lowerCAmelCase__    						=						bbox[3] * scale_x
																								lowerCAmelCase__    						=						scale_y + bbox[4] * (1 - scale_y)
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
												else:  # bottom-right
																		lowerCAmelCase__    						=						cva.resize(
																		    UpperCamelCase_    ,       (output_size[1] - divid_point_x, output_size[0] - divid_point_y)		)
																		lowerCAmelCase__    						=						img
																		for bbox in img_annos:
																								lowerCAmelCase__    						=						scale_x + bbox[1] * (1 - scale_x)
																								lowerCAmelCase__    						=						scale_y + bbox[2] * (1 - scale_y)
																								lowerCAmelCase__    						=						scale_x + bbox[3] * (1 - scale_x)
																								lowerCAmelCase__    						=						scale_y + bbox[4] * (1 - scale_y)
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)

    # Remove bounding box small than scale of filter
						if filter_scale > 0:
												lowerCAmelCase__    						=						[
												    anno
												    for anno in new_anno
												    if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
												]

						return output_img, new_anno, path_list[0]







def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   str:

						"""simple docstring"""



						assert number_char > 1, "The number of character should greater than 1"
						lowerCAmelCase__    						=						ascii_lowercase + digits
						return "".join(random.choice(UpperCamelCase_		) for _ in range(UpperCamelCase_		)		)


if __name__ == "__main__":
					main()
					print("""DONE ✅""")





