

'''simple docstring'''







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

import cva
import numpy as np

# Parrameters
__lowerCamelCase									=     (720, 1280)  # Height, Width
__lowerCamelCase									=     (0.4, 0.6)  # if height or width lower than this scale, drop it.
__lowerCamelCase									=     1 / 100
__lowerCamelCase									=     ''''''
__lowerCamelCase									=     ''''''
__lowerCamelCase									=     ''''''
__lowerCamelCase									=     250
def    UpperCAmelCase__     (   )      ->      None:
       A_       ,      A_						       =   get_dataset(UpperCAmelCase__,							UpperCAmelCase__					)
       for index in range(UpperCAmelCase__					):
              A_						       =   random.sample(range(len(UpperCAmelCase__					)					),							4					)
              A_       ,      A_       ,      A_						       =   update_image_and_anno(
                  UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							filter_scale=UpperCAmelCase__,							)

              # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
              A_						       =   random_chars(32					)
              A_						       =   path.split(os.sep					)[-1].rsplit(""".""",							1					)[0]
              A_						       =   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}'''					)
              A_						       =   []
              for anno in new_annos:
                     A_						       =   anno[3] - anno[1]
                     A_						       =   anno[4] - anno[2]
                     A_						       =   anno[1] + width / 2
                     A_						       =   anno[2] + height / 2
                     A_						       =   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__,							UpperCAmelCase__					)      ->      tuple[list, list]:
       A_						       =   []
       A_						       =   []
       for label_file in glob.glob(os.path.join(UpperCAmelCase__,							"""*.txt"""					)					):
              A_						       =   label_file.split(os.sep					)[-1].rsplit(""".""",							1					)[0]
              with open(UpperCAmelCase__					) as in_file:
                     A_						       =   in_file.readlines()
              A_						       =   os.path.join(UpperCAmelCase__,							F'''{label_name}.jpg'''					)

              A_						       =   []
              for obj_list in obj_lists:
                     A_						       =   obj_list.rstrip("""\n"""					).split(""" """					)
                     A_						       =   float(obj[1]					) - float(obj[3]					) / 2
                     A_						       =   float(obj[2]					) - float(obj[4]					) / 2
                     A_						       =   float(obj[1]					) + float(obj[3]					) / 2
                     A_						       =   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__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__ = 0.0,							)      ->      tuple[list, list, str]:
       A_						       =   np.zeros([output_size[0], output_size[1], 3],							dtype=np.uinta					)
       A_						       =   scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       A_						       =   scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       A_						       =   int(scale_x * output_size[1]					)
       A_						       =   int(scale_y * output_size[0]					)

       A_						       =   []
       A_						       =   []
       for i, index in enumerate(UpperCAmelCase__					):
              A_						       =   all_img_list[index]
              path_list.append(UpperCAmelCase__					)
              A_						       =   all_annos[index]
              A_						       =   cva.imread(UpperCAmelCase__					)
              if i == 0:  # top-left
                     A_						       =   cva.resize(UpperCAmelCase__,							(divid_point_x, divid_point_y)					)
                     A_						       =   img
                     for bbox in img_annos:
                            A_						       =   bbox[1] * scale_x
                            A_						       =   bbox[2] * scale_y
                            A_						       =   bbox[3] * scale_x
                            A_						       =   bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]					)
              elif i == 1:  # top-right
                     A_						       =   cva.resize(UpperCAmelCase__,							(output_size[1] - divid_point_x, divid_point_y)					)
                     A_						       =   img
                     for bbox in img_annos:
                            A_						       =   scale_x + bbox[1] * (1 - scale_x)
                            A_						       =   bbox[2] * scale_y
                            A_						       =   scale_x + bbox[3] * (1 - scale_x)
                            A_						       =   bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]					)
              elif i == 2:  # bottom-left
                     A_						       =   cva.resize(UpperCAmelCase__,							(divid_point_x, output_size[0] - divid_point_y)					)
                     A_						       =   img
                     for bbox in img_annos:
                            A_						       =   bbox[1] * scale_x
                            A_						       =   scale_y + bbox[2] * (1 - scale_y)
                            A_						       =   bbox[3] * scale_x
                            A_						       =   scale_y + bbox[4] * (1 - scale_y)
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]					)
              else:  # bottom-right
                     A_						       =   cva.resize(
                         UpperCAmelCase__,							(output_size[1] - divid_point_x, output_size[0] - divid_point_y)					)
                     A_						       =   img
                     for bbox in img_annos:
                            A_						       =   scale_x + bbox[1] * (1 - scale_x)
                            A_						       =   scale_y + bbox[2] * (1 - scale_y)
                            A_						       =   scale_x + bbox[3] * (1 - scale_x)
                            A_						       =   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:
              A_						       =   [
                  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__					)      ->      str:
       assert number_char > 1, "The number of character should greater than 1"
       A_						       =   ascii_lowercase + digits
       return "".join(random.choice(UpperCAmelCase__					) for _ in range(UpperCAmelCase__					)					)


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



'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class        A__   (   unittest.TestCase      ):



    @property
    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           torch.manual_seed(0							)
           A_						       =   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""")		,					)
           return model







    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           A_						       =   self.dummy_uncond_unet
           A_						       =   PNDMScheduler()

           A_						       =   PNDMPipeline(unet=UpperCamelCase__		,					scheduler=UpperCamelCase__							)
           pndm.to(UpperCamelCase__							)
           pndm.set_progress_bar_config(disable=UpperCamelCase__							)

           A_						       =   torch.manual_seed(0							)
           A_						       =   pndm(generator=UpperCamelCase__		,					num_inference_steps=20		,					output_type="""numpy"""							).images

           A_						       =   torch.manual_seed(0							)
           A_						       =   pndm(generator=UpperCamelCase__		,					num_inference_steps=20		,					output_type="""numpy"""		,					return_dict=UpperCamelCase__							)[0]

           A_						       =   image[0, -3:, -3:, -1]
           A_						       =   image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           A_						       =   np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

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


@slow
@require_torch
class        A__   (   unittest.TestCase      ):



    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           A_						       =   """google/ddpm-cifar10-32"""

           A_						       =   UNetaDModel.from_pretrained(UpperCamelCase__							)
           A_						       =   PNDMScheduler()

           A_						       =   PNDMPipeline(unet=UpperCamelCase__		,					scheduler=UpperCamelCase__							)
           pndm.to(UpperCamelCase__							)
           pndm.set_progress_bar_config(disable=UpperCamelCase__							)
           A_						       =   torch.manual_seed(0							)
           A_						       =   pndm(generator=UpperCamelCase__		,					output_type="""numpy"""							).images

           A_						       =   image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           A_						       =   np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]							)

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