

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

import cva
import numpy as np

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





def 					__lowerCamelCase	( ):







							'''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 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''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 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__ = 0.0   ,							):







							'''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 					__lowerCamelCase	( UpperCamelCase__      ):







							'''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 ✅""")







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




