
'''simple docstring'''







import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     (IPNDMScheduler,)
					lowerCAmelCase_        =     (('''num_inference_steps''', 5_0),)






					def 	_snake_case    (   self				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       {'''num_train_timesteps''': 10_00}
											config.update(**__SCREAMING_SNAKE_CASE		)
											return config






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE=0				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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

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

																	if time_step is None:
																							lowercase_	:     List[Any]													=       scheduler.timesteps[len(scheduler.timesteps		) // 2]

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

																	lowercase_	:     Optional[int]													=       scheduler.step(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		).prev_sample
																	lowercase_	:     List[Any]													=       new_scheduler.step(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		).prev_sample

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

																	lowercase_	:     List[str]													=       scheduler.step(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		).prev_sample
																	lowercase_	:     Dict													=       new_scheduler.step(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		).prev_sample

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






					def 	_snake_case    (   self		):


											"""simple docstring"""





											pass






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE=0				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       dict(self.forward_default_kwargs		)
											lowercase_	:     Tuple													=       kwargs.pop('''num_inference_steps'''				,							__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Tuple													=       self.dummy_sample
											lowercase_	:     Dict													=       0.1 * sample
											lowercase_	:     str													=       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

											for scheduler_class in self.scheduler_classes:
																	lowercase_	:     Optional[int]													=       self.get_scheduler_config()
																	lowercase_	:     Any													=       scheduler_class(**__SCREAMING_SNAKE_CASE		)
																	scheduler.set_timesteps(__SCREAMING_SNAKE_CASE		)

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

																	if time_step is None:
																							lowercase_	:     Dict													=       scheduler.timesteps[len(scheduler.timesteps		) // 2]

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

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

																	lowercase_	:     Optional[Any]													=       scheduler.step(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		).prev_sample
																	lowercase_	:     Dict													=       new_scheduler.step(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		).prev_sample

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

																	lowercase_	:     int													=       scheduler.step(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		).prev_sample
																	lowercase_	:     int													=       new_scheduler.step(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		).prev_sample

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






					def 	_snake_case    (   self				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     str													=       self.scheduler_classes[0]
											lowercase_	:     Optional[Any]													=       self.get_scheduler_config(**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Dict													=       scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Dict													=       10
											lowercase_	:     List[Any]													=       self.dummy_model()
											lowercase_	:     Tuple													=       self.dummy_sample_deter
											scheduler.set_timesteps(__SCREAMING_SNAKE_CASE		)

											for i, t in enumerate(scheduler.timesteps		):
																	lowercase_	:     int													=       model(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     int													=       scheduler.step(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		).prev_sample

											for i, t in enumerate(scheduler.timesteps		):
																	lowercase_	:     List[Any]													=       model(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     Union[str, Any]													=       scheduler.step(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		).prev_sample

											return sample






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     List[str]													=       dict(self.forward_default_kwargs		)

											lowercase_	:     List[str]													=       kwargs.pop('''num_inference_steps'''				,							__SCREAMING_SNAKE_CASE		)

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

																	lowercase_	:     Union[str, Any]													=       self.dummy_sample
																	lowercase_	:     str													=       0.1 * sample

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

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

																	lowercase_	:     List[str]													=       scheduler.timesteps[5]
																	lowercase_	:     Optional[int]													=       scheduler.timesteps[6]

																	lowercase_	:     int													=       scheduler.step(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		).prev_sample
																	lowercase_	:     int													=       scheduler.step(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		).prev_sample

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

																	lowercase_	:     Optional[int]													=       scheduler.step(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		).prev_sample
																	lowercase_	:     Tuple													=       scheduler.step(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		).prev_sample

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






					def 	_snake_case    (   self		):


											"""simple docstring"""





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






					def 	_snake_case    (   self		):


											"""simple docstring"""





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






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Union[str, Any]													=       self.full_loop()
											lowercase_	:     Optional[Any]													=       torch.mean(torch.abs(__SCREAMING_SNAKE_CASE		)		)

											assert abs(result_mean.item() - 2_54_05_29		) < 10





'''simple docstring'''







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

import cva
import numpy as np

# Parrameters
_lowercase :  Any    							=       (7_2_0, 1_2_8_0)  # Height, Width
_lowercase :  List[Any]    							=       (0.4, 0.6)  # if height or width lower than this scale, drop it.
_lowercase :  str    							=       1 / 1_0_0
_lowercase :  Any    							=       ""
_lowercase :  Union[str, Any]    							=       ""
_lowercase :  Optional[int]    							=       ""
_lowercase :  List[Any]    							=       2_5_0
def 					snake_case_		(				):



						"""simple docstring"""





						lowercase_						, lowercase_	:     Any													=       get_dataset(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)
						for index in range(__SCREAMING_SNAKE_CASE							):
												lowercase_	:     str													=       random.sample(range(len(__SCREAMING_SNAKE_CASE							)							)      ,      4							)
												lowercase_						, lowercase_						, lowercase_	:     Any													=       update_image_and_anno(
												    __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      filter_scale=__SCREAMING_SNAKE_CASE      ,      )

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



						"""simple docstring"""





						lowercase_	:     Optional[Any]													=       []
						lowercase_	:     Optional[Any]													=       []
						for label_file in glob.glob(os.path.join(__SCREAMING_SNAKE_CASE      ,      '''*.txt'''							)							):
												lowercase_	:     int													=       label_file.split(os.sep							)[-1].rsplit('''.'''      ,      1							)[0]
												with open(__SCREAMING_SNAKE_CASE							) as in_file:
																		lowercase_	:     List[str]													=       in_file.readlines()
												lowercase_	:     Optional[Any]													=       os.path.join(__SCREAMING_SNAKE_CASE      ,      F'''{label_name}.jpg'''							)

												lowercase_	:     Optional[int]													=       []
												for obj_list in obj_lists:
																		lowercase_	:     List[str]													=       obj_list.rstrip('''\n'''							).split(''' '''							)
																		lowercase_	:     Optional[int]													=       float(obj[1]							) - float(obj[3]							) / 2
																		lowercase_	:     Any													=       float(obj[2]							) - float(obj[4]							) / 2
																		lowercase_	:     str													=       float(obj[1]							) + float(obj[3]							) / 2
																		lowercase_	:     List[str]													=       float(obj[2]							) + float(obj[4]							) / 2

																		boxes.append([int(obj[0]							), xmin, ymin, xmax, ymax]							)
												if not boxes:
																		continue
												img_paths.append(__SCREAMING_SNAKE_CASE							)
												labels.append(__SCREAMING_SNAKE_CASE							)
						return img_paths, labels
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: list      ,      __SCREAMING_SNAKE_CASE					: list      ,      __SCREAMING_SNAKE_CASE					: list[int]      ,      __SCREAMING_SNAKE_CASE					: tuple[int, int]      ,      __SCREAMING_SNAKE_CASE					: tuple[float, float]      ,      __SCREAMING_SNAKE_CASE					: float = 0.0      ,      ):



						"""simple docstring"""





						lowercase_	:     List[Any]													=       np.zeros([output_size[0], output_size[1], 3]      ,      dtype=np.uinta							)
						lowercase_	:     Tuple													=       scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						lowercase_	:     List[Any]													=       scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						lowercase_	:     Optional[int]													=       int(scale_x * output_size[1]							)
						lowercase_	:     Dict													=       int(scale_y * output_size[0]							)

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



						"""simple docstring"""





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


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



