




'''simple docstring'''

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

import cva
import numpy as np

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


def 		__magic_name__    (	)		->					List[Any]:

					'''simple docstring'''

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

										# 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",      lowercase__,      [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(lowercase__	)
										with open(F"{file_root}.txt",      '''w'''	) as outfile:
															outfile.write('''\n'''.join(line for line in annos_list	)	)


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					List[Any]:

					'''simple docstring'''

					snake_case_									=				[]
					snake_case_									=				[]
					for label_file in glob.glob(os.path.join(lowercase__,      '''*.txt'''	)	):
										snake_case_									=				label_file.split(os.sep	)[-1].rsplit('''.''',      1	)[0]
										with open(lowercase__	) as in_file:
															snake_case_									=				in_file.readlines()
										snake_case_									=				os.path.join(lowercase__,      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(lowercase__	)
										labels.append(lowercase__	)
					return img_paths, labels


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase = 0.0,      )		->					Optional[int]:

					'''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(lowercase__	):
										snake_case_									=				all_img_list[index]
										path_list.append(lowercase__	)
										snake_case_									=				all_annos[index]
										snake_case_									=				cva.imread(lowercase__	)
										if i == 0:  # top-left
															snake_case_									=				cva.resize(lowercase__,      (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(lowercase__,      (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(lowercase__,      (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(
															    lowercase__,      (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	)		->					List[Any]:

					'''simple docstring'''

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


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







"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class lowerCAmelCase__	(     lowercase,	unittest.TestCase						):






			'''simple docstring'''



			lowerCamelCase__      =			DDIMPipeline
			lowerCamelCase__      =			UNCONDITIONAL_IMAGE_GENERATION_PARAMS
			lowerCamelCase__      =			PipelineTesterMixin.required_optional_params - {
			    """num_images_per_prompt""",
			    """latents""",
			    """callback""",
			    """callback_steps""",
			}
			lowerCamelCase__      =			UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
			lowerCamelCase__      =			False
			def 					A_    (  self  ):
							torch.manual_seed(0  )
							_lowerCamelCase     :			List[Any]    =    UNetaDModel(
							    block_out_channels=(32, 64)  ,      layers_per_block=2  ,      sample_size=32  ,      in_channels=3  ,      out_channels=3  ,      down_block_types=('DownBlock2D', 'AttnDownBlock2D')  ,      up_block_types=('AttnUpBlock2D', 'UpBlock2D')  ,      )
							_lowerCamelCase     :			List[str]    =    DDIMScheduler()
							_lowerCamelCase     :			Optional[int]    =    {'unet': unet, 'scheduler': scheduler}
							return components
			def 					A_    (  self  ,      lowercase  ,      lowercase=0  ):
							if str(lowercase  ).startswith('mps'  ):
											_lowerCamelCase     :			Dict    =    torch.manual_seed(lowercase  )
							else:
											_lowerCamelCase     :			List[str]    =    torch.Generator(device=lowercase  ).manual_seed(lowercase  )
							_lowerCamelCase     :			Tuple    =    {
							    'batch_size': 1,
							    'generator': generator,
							    'num_inference_steps': 2,
							    'output_type': 'numpy',
							}
							return inputs
			def 					A_    (  self  ):
							_lowerCamelCase     :			Any    =    'cpu'

							_lowerCamelCase     :			Tuple    =    self.get_dummy_components()
							_lowerCamelCase     :			Optional[Any]    =    self.pipeline_class(**lowercase  )
							pipe.to(lowercase  )
							pipe.set_progress_bar_config(disable=lowercase  )

							_lowerCamelCase     :			str    =    self.get_dummy_inputs(lowercase  )
							_lowerCamelCase     :			int    =    pipe(**lowercase  ).images
							_lowerCamelCase     :			Any    =    image[0, -3:, -3:, -1]

							self.assertEqual(image.shape  ,      (1, 32, 32, 3)  )
							_lowerCamelCase     :			Tuple    =    np.array(
							    [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]  )
							_lowerCamelCase     :			str    =    np.abs(image_slice.flatten() - expected_slice  ).max()
							self.assertLessEqual(lowercase  ,      1E-3  )
			def 					A_    (  self  ):
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3  )
			def 					A_    (  self  ):
							super().test_save_load_local(expected_max_difference=3E-3  )
			def 					A_    (  self  ):
							super().test_save_load_optional_components(expected_max_difference=3E-3  )






			def 					A_    (  self  ):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3  )






@slow
@require_torch_gpu
class lowerCAmelCase__	(     unittest.TestCase						):






			'''simple docstring'''



			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[Any]    =    'google/ddpm-cifar10-32'

							_lowerCamelCase     :			Optional[Any]    =    UNetaDModel.from_pretrained(lowercase  )
							_lowerCamelCase     :			Dict    =    DDIMScheduler()

							_lowerCamelCase     :			Dict    =    DDIMPipeline(unet=lowercase  ,      scheduler=lowercase  )
							ddim.to(lowercase  )
							ddim.set_progress_bar_config(disable=lowercase  )

							_lowerCamelCase     :			List[str]    =    torch.manual_seed(0  )
							_lowerCamelCase     :			str    =    ddim(generator=lowercase  ,      eta=0.0  ,      output_type='numpy'  ).images

							_lowerCamelCase     :			Dict    =    image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							_lowerCamelCase     :			List[Any]    =    np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53]  )

							assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2






			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[int]    =    'google/ddpm-ema-bedroom-256'

							_lowerCamelCase     :			str    =    UNetaDModel.from_pretrained(lowercase  )
							_lowerCamelCase     :			str    =    DDIMScheduler.from_pretrained(lowercase  )

							_lowerCamelCase     :			Optional[int]    =    DDIMPipeline(unet=lowercase  ,      scheduler=lowercase  )
							ddpm.to(lowercase  )
							ddpm.set_progress_bar_config(disable=lowercase  )

							_lowerCamelCase     :			Tuple    =    torch.manual_seed(0  )
							_lowerCamelCase     :			int    =    ddpm(generator=lowercase  ,      output_type='numpy'  ).images

							_lowerCamelCase     :			Dict    =    image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)
							_lowerCamelCase     :			str    =    np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69]  )

							assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2