

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

import cva
import numpy as np

# Parrameters
lowercase_									=   (7_2_0, 1_2_8_0)  # Height, Width
lowercase_									=   (0.4, 0.6)  # if height or width lower than this scale, drop it.
lowercase_									=   1 / 1_0_0
lowercase_									=   """"""
lowercase_									=   """"""
lowercase_									=   """"""
lowercase_									=   2_5_0
def      a      (			)    ->      Dict:





      """simple docstring"""



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

            # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
            _lowercase	       =random_chars(32     )
            _lowercase	       =path.split(os.sep     )[-1].rsplit('.'      ,   1     )[0]
            _lowercase	       =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}'''     )
            _lowercase	       =[]
            for anno in new_annos:
                  _lowercase	       =anno[3] - anno[1]
                  _lowercase	       =anno[4] - anno[2]
                  _lowercase	       =anno[1] + width / 2
                  _lowercase	       =anno[2] + height / 2
                  _lowercase	       =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      a      (			A__	:   Optional[int]      ,   A__	:   Any     )    ->      Any:





      """simple docstring"""



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

            _lowercase	       =[]
            for obj_list in obj_lists:
                  _lowercase	       =obj_list.rstrip('\n'     ).split(' '     )
                  _lowercase	       =float(obj[1]     ) - float(obj[3]     ) / 2
                  _lowercase	       =float(obj[2]     ) - float(obj[4]     ) / 2
                  _lowercase	       =float(obj[1]     ) + float(obj[3]     ) / 2
                  _lowercase	       =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      a      (			A__	:   Tuple      ,   A__	:   List[str]      ,   A__	:   Optional[int]      ,   A__	:   Union[str, Any]      ,   A__	:   List[Any]      ,   A__	:   Optional[Any] = 0.0      ,   )    ->      int:





      """simple docstring"""



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

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





      """simple docstring"""



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


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





import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
					import jax
					import jax.numpy as jnp
					from flax.jax_utils import replicate
					from flax.training.common_utils import shard







@slow
@require_flax
class  lowercase				(       unittest.TestCase			):




				def     a							(					self       ):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()




				def     a							(					self       ):
											snake_case_							=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/sd2-inpaint/init_image.png'       )
											snake_case_							=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'       )

											snake_case_							=    'xvjiarui/stable-diffusion-2-inpainting'
											snake_case_				,    snake_case_							=    FlaxStableDiffusionInpaintPipeline.from_pretrained(snake_case      ,   safety_checker=snake_case       )

											snake_case_							=    'Face of a yellow cat, high resolution, sitting on a park bench'

											snake_case_							=    jax.random.PRNGKey(0       )
											snake_case_							=    50

											snake_case_							=    jax.device_count()
											snake_case_							=    num_samples * [prompt]
											snake_case_							=    num_samples * [init_image]
											snake_case_							=    num_samples * [mask_image]
											snake_case_				,    snake_case_				,    snake_case_							=    pipeline.prepare_inputs(snake_case      ,   snake_case      ,   snake_case       )

											# shard inputs and rng
											snake_case_							=    replicate(snake_case       )
											snake_case_							=    jax.random.split(snake_case      ,   jax.device_count()       )
											snake_case_							=    shard(snake_case       )
											snake_case_							=    shard(snake_case       )
											snake_case_							=    shard(snake_case       )

											snake_case_							=    pipeline(
											    snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   jit=snake_case       )

											snake_case_							=    output.images.reshape(snake_case      ,   512      ,   512      ,   3       )

											snake_case_							=    images[0, 253:256, 253:256, -1]

											snake_case_							=    jnp.asarray(jax.device_get(image_slice.flatten()       )       )
											snake_case_							=    jnp.array(
											    [0.3_61_13_07, 0.37_64_97_36, 0.3_75_74_08, 0.38_21_39_53, 0.39_29_51_67, 0.3_84_16_31, 0.41_55_49_78, 0.4_13_74_75, 0.4_21_70_84]       )
											print(F'''output_slice: {output_slice}'''       )
											assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2




