




'''simple docstring'''

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		(PNDMScheduler,)
				snake_case_      =		(("num_inference_steps", 50),)


				def   A_ (		self    :					Tuple       ,				**lowercase_    :					Tuple					):
									snake_case_									=				{
									    '''num_train_timesteps''': 1000,
									    '''beta_start''': 0.0001,
									    '''beta_end''': 0.02,
									    '''beta_schedule''': '''linear''',
									}

									config.update(**lowercase_					)
									return config


				def   A_ (		self    :					Any       ,				lowercase_    :					Optional[int]=0       ,				**lowercase_    :					int					):
									snake_case_									=				dict(self.forward_default_kwargs					)
									snake_case_									=				kwargs.pop('''num_inference_steps'''       ,				lowercase_					)
									snake_case_									=				self.dummy_sample
									snake_case_									=				0.1 * sample
									snake_case_									=				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
														snake_case_									=				self.get_scheduler_config(**lowercase_					)
														snake_case_									=				scheduler_class(**lowercase_					)
														scheduler.set_timesteps(lowercase_					)
														# copy over dummy past residuals
														snake_case_									=				dummy_past_residuals[:]

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

														snake_case_									=				scheduler.step_prk(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample
														snake_case_									=				new_scheduler.step_prk(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample

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

														snake_case_									=				scheduler.step_plms(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample
														snake_case_									=				new_scheduler.step_plms(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample

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


				def   A_ (		self    :					Any					):
									pass


				def   A_ (		self    :					Any       ,				lowercase_    :					Dict=0       ,				**lowercase_    :					Optional[int]					):
									snake_case_									=				dict(self.forward_default_kwargs					)
									snake_case_									=				kwargs.pop('''num_inference_steps'''       ,				lowercase_					)
									snake_case_									=				self.dummy_sample
									snake_case_									=				0.1 * sample
									snake_case_									=				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
														snake_case_									=				self.get_scheduler_config()
														snake_case_									=				scheduler_class(**lowercase_					)
														scheduler.set_timesteps(lowercase_					)

														# copy over dummy past residuals (must be after setting timesteps)
														snake_case_									=				dummy_past_residuals[:]

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

																			# copy over dummy past residual (must be after setting timesteps)
																			snake_case_									=				dummy_past_residuals[:]

														snake_case_									=				scheduler.step_prk(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample
														snake_case_									=				new_scheduler.step_prk(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample

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

														snake_case_									=				scheduler.step_plms(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample
														snake_case_									=				new_scheduler.step_plms(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample

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


				def   A_ (		self    :					Dict       ,				**lowercase_    :					str					):
									snake_case_									=				self.scheduler_classes[0]
									snake_case_									=				self.get_scheduler_config(**lowercase_					)
									snake_case_									=				scheduler_class(**lowercase_					)

									snake_case_									=				10
									snake_case_									=				self.dummy_model()
									snake_case_									=				self.dummy_sample_deter
									scheduler.set_timesteps(lowercase_					)

									for i, t in enumerate(scheduler.prk_timesteps					):
														snake_case_									=				model(lowercase_       ,				lowercase_					)
														snake_case_									=				scheduler.step_prk(lowercase_       ,				lowercase_       ,				lowercase_					).prev_sample

									for i, t in enumerate(scheduler.plms_timesteps					):
														snake_case_									=				model(lowercase_       ,				lowercase_					)
														snake_case_									=				scheduler.step_plms(lowercase_       ,				lowercase_       ,				lowercase_					).prev_sample

									return sample


				def   A_ (		self    :					Dict					):
									snake_case_									=				dict(self.forward_default_kwargs					)

									snake_case_									=				kwargs.pop('''num_inference_steps'''       ,				lowercase_					)

									for scheduler_class in self.scheduler_classes:
														snake_case_									=				self.get_scheduler_config()
														snake_case_									=				scheduler_class(**lowercase_					)

														snake_case_									=				self.dummy_sample
														snake_case_									=				0.1 * sample

														if num_inference_steps is not None and hasattr(lowercase_       ,				'''set_timesteps'''					):
																			scheduler.set_timesteps(lowercase_					)
														elif num_inference_steps is not None and not hasattr(lowercase_       ,				'''set_timesteps'''					):
																			snake_case_									=				num_inference_steps

														# copy over dummy past residuals (must be done after set_timesteps)
														snake_case_									=				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
														snake_case_									=				dummy_past_residuals[:]

														snake_case_									=				scheduler.step_prk(lowercase_       ,				0       ,				lowercase_       ,				**lowercase_					).prev_sample
														snake_case_									=				scheduler.step_prk(lowercase_       ,				1       ,				lowercase_       ,				**lowercase_					).prev_sample

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

														snake_case_									=				scheduler.step_plms(lowercase_       ,				0       ,				lowercase_       ,				**lowercase_					).prev_sample
														snake_case_									=				scheduler.step_plms(lowercase_       ,				1       ,				lowercase_       ,				**lowercase_					).prev_sample

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


				def   A_ (		self    :					List[Any]					):
									for timesteps in [100, 1000]:
														self.check_over_configs(num_train_timesteps=lowercase_					)


				def   A_ (		self    :					List[str]					):
									for steps_offset in [0, 1]:
														self.check_over_configs(steps_offset=lowercase_					)

									snake_case_									=				self.scheduler_classes[0]
									snake_case_									=				self.get_scheduler_config(steps_offset=1					)
									snake_case_									=				scheduler_class(**lowercase_					)
									scheduler.set_timesteps(10					)
									assert torch.equal(
									    scheduler.timesteps       ,				torch.LongTensor(
									        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]					)       ,				)


				def   A_ (		self    :					Any					):
									for beta_start, beta_end in zip([0.0001, 0.001]       ,				[0.002, 0.02]					):
														self.check_over_configs(beta_start=lowercase_       ,				beta_end=lowercase_					)


				def   A_ (		self    :					List[str]					):
									for schedule in ["linear", "squaredcos_cap_v2"]:
														self.check_over_configs(beta_schedule=lowercase_					)


				def   A_ (		self    :					str					):
									for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=lowercase_					)


				def   A_ (		self    :					str					):
									for t in [1, 5, 10]:
														self.check_over_forward(time_step=lowercase_					)


				def   A_ (		self    :					str					):
									for t, num_inference_steps in zip([1, 5, 10]       ,				[10, 50, 100]					):
														self.check_over_forward(num_inference_steps=lowercase_					)


				def   A_ (		self    :					Any					):
									# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
									snake_case_									=				27

									for scheduler_class in self.scheduler_classes:
														snake_case_									=				self.dummy_sample
														snake_case_									=				0.1 * sample

														snake_case_									=				self.get_scheduler_config()
														snake_case_									=				scheduler_class(**lowercase_					)

														scheduler.set_timesteps(lowercase_					)

														# before power of 3 fix, would error on first step, so we only need to do two
														for i, t in enumerate(scheduler.prk_timesteps[:2]					):
																			snake_case_									=				scheduler.step_prk(lowercase_       ,				lowercase_       ,				lowercase_					).prev_sample


				def   A_ (		self    :					Tuple					):
									with self.assertRaises(lowercase_					):
														snake_case_									=				self.scheduler_classes[0]
														snake_case_									=				self.get_scheduler_config()
														snake_case_									=				scheduler_class(**lowercase_					)

														scheduler.step_plms(self.dummy_sample       ,				1       ,				self.dummy_sample					).prev_sample


				def   A_ (		self    :					str					):
									snake_case_									=				self.full_loop()
									snake_case_									=				torch.sum(torch.abs(lowercase_					)					)
									snake_case_									=				torch.mean(torch.abs(lowercase_					)					)

									assert abs(result_sum.item() - 198.1318					) < 1e-2
									assert abs(result_mean.item() - 0.2580					) < 1e-3


				def   A_ (		self    :					Any					):
									snake_case_									=				self.full_loop(prediction_type='''v_prediction'''					)
									snake_case_									=				torch.sum(torch.abs(lowercase_					)					)
									snake_case_									=				torch.mean(torch.abs(lowercase_					)					)

									assert abs(result_sum.item() - 67.3986					) < 1e-2
									assert abs(result_mean.item() - 0.0878					) < 1e-3


				def   A_ (		self    :					Optional[Any]					):
									# We specify different beta, so that the first alpha is 0.99
									snake_case_									=				self.full_loop(set_alpha_to_one=lowercase_       ,				beta_start=0.01					)
									snake_case_									=				torch.sum(torch.abs(lowercase_					)					)
									snake_case_									=				torch.mean(torch.abs(lowercase_					)					)

									assert abs(result_sum.item() - 230.0399					) < 1e-2
									assert abs(result_mean.item() - 0.2995					) < 1e-3


				def   A_ (		self    :					str					):
									# We specify different beta, so that the first alpha is 0.99
									snake_case_									=				self.full_loop(set_alpha_to_one=lowercase_       ,				beta_start=0.01					)
									snake_case_									=				torch.sum(torch.abs(lowercase_					)					)
									snake_case_									=				torch.mean(torch.abs(lowercase_					)					)

									assert abs(result_sum.item() - 186.9482					) < 1e-2
									assert abs(result_mean.item() - 0.2434					) < 1e-3







'''simple docstring'''

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

import cva
import numpy as np

# Parrameters
a  :				Dict									=  (720, 1280)  # Height, Width
a  :				Tuple									=  (0.4, 0.6)  # if height or width lower than this scale, drop it.
a  :				Dict									=  1 / 100
a  :				str									=  ''
a  :				Any									=  ''
a  :				Optional[int]									=  ''
a  :				List[str]									=  250


def 		__magic_name__    (	)		->					None:

					'''simple docstring'''

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

										# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
										snake_case_									=				random_chars(32	)
										snake_case_									=				path.split(os.sep	)[-1].rsplit('''.''',      1	)[0]
										snake_case_									=				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}"	)
										snake_case_									=				[]
										for anno in new_annos:
															snake_case_									=				anno[3] - anno[1]
															snake_case_									=				anno[4] - anno[2]
															snake_case_									=				anno[1] + width / 2
															snake_case_									=				anno[2] + height / 2
															snake_case_									=				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 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					tuple[list, list]:

					'''simple docstring'''

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

										snake_case_									=				[]
										for obj_list in obj_lists:
															snake_case_									=				obj_list.rstrip('''\n'''	).split(''' '''	)
															snake_case_									=				float(obj[1]	) - float(obj[3]	) / 2
															snake_case_									=				float(obj[2]	) - float(obj[4]	) / 2
															snake_case_									=				float(obj[1]	) + float(obj[3]	) / 2
															snake_case_									=				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 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase = 0.0,      )		->					tuple[list, list, str]:

					'''simple docstring'''

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

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

					'''simple docstring'''

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


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

