





'''simple docstring'''





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

import cva
__lowercase       :Any       =  ""
__lowercase       :Optional[Any]       =  ""
__lowercase       :Tuple       =  ""
__lowercase       :List[Any]       =  1  # (0 is vertical, 1 is horizontal)







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					get_dataset(_lowerCamelCase			,   _lowerCamelCase     )
						print("Processing..."     )
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					update_image_and_anno(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase     )

						for index, image in enumerate(_lowerCamelCase     ):
												# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
												SCREAMING_SNAKE_CASE__     :				Dict   =					random_chars(32     )
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					paths[index].split(os.sep     )[-1].rsplit("."			,   1     )[0]
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					f"""{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"""
												cva.imwrite(f"""/{file_root}.jpg"""			,   _lowerCamelCase			,   [cva.IMWRITE_JPEG_QUALITY, 85]     )
												print(f"""Success {index+1}/{len(_lowerCamelCase     )} with {file_name}"""     )
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					[]
												for anno in new_annos[index]:
																		SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					f"""{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"""
																		annos_list.append(_lowerCamelCase     )
												with open(f"""/{file_root}.txt"""			,   "w"     ) as outfile:
																		outfile.write("\n".join(line for line in annos_list     )     )







def       UpperCAmelCase							(     _lowerCamelCase      :  str			,   _lowerCamelCase      :  str     ):





						'''simple docstring'''


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

												SCREAMING_SNAKE_CASE__     :				List[str]   =					[]
												for obj_list in obj_lists:
																		SCREAMING_SNAKE_CASE__     :				Optional[int]   =					obj_list.rstrip("\n"     ).split(" "     )
																		boxes.append(
																		    [
																		        int(obj[0]     ),
																		        float(obj[1]     ),
																		        float(obj[2]     ),
																		        float(obj[3]     ),
																		        float(obj[4]     ),
																		    ]     )
												if not boxes:
																		continue
												img_paths.append(_lowerCamelCase     )
												labels.append(_lowerCamelCase     )
						return img_paths, labels







def       UpperCAmelCase							(     _lowerCamelCase      :  list			,   _lowerCamelCase      :  list			,   _lowerCamelCase      :  int = 1     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Dict   =					[]
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					[]
						SCREAMING_SNAKE_CASE__     :				str   =					[]
						for idx in range(len(_lowerCamelCase     )     ):
												SCREAMING_SNAKE_CASE__     :				Optional[int]   =					[]
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					img_list[idx]
												path_list.append(_lowerCamelCase     )
												SCREAMING_SNAKE_CASE__     :				int   =					anno_list[idx]
												SCREAMING_SNAKE_CASE__     :				List[str]   =					cva.imread(_lowerCamelCase     )
												if flip_type == 1:
																		SCREAMING_SNAKE_CASE__     :				str   =					cva.flip(_lowerCamelCase			,   _lowerCamelCase     )
																		for bbox in img_annos:
																								SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					1 - bbox[1]
																								new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]     )
												elif flip_type == 0:
																		SCREAMING_SNAKE_CASE__     :				int   =					cva.flip(_lowerCamelCase			,   _lowerCamelCase     )
																		for bbox in img_annos:
																								SCREAMING_SNAKE_CASE__     :				Optional[int]   =					1 - bbox[2]
																								new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]     )
												new_annos_lists.append(_lowerCamelCase     )
												new_imgs_list.append(_lowerCamelCase     )
						return new_imgs_list, new_annos_lists, path_list







def       UpperCAmelCase							(     _lowerCamelCase      :  int = 32     ):





						'''simple docstring'''


						assert number_char > 1, "The number of character should greater than 1"
						SCREAMING_SNAKE_CASE__     :				List[str]   =					ascii_lowercase + digits
						return "".join(random.choice(_lowerCamelCase     ) for _ in range(_lowerCamelCase     )     )


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






import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
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    _a     (				unittest.TestCase							):






							"""simple docstring"""

							def      A_  (      self				:     Dict    )	->List[Any]:
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()

							def      A_  (      self				:     Dict    )	->Tuple:
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					FlaxControlNetModel.from_pretrained(
													    "lllyasviel/sd-controlnet-canny"							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					FlaxStableDiffusionControlNetPipeline.from_pretrained(
													    "runwayml/stable-diffusion-v1-5"							,	controlnet=a							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					controlnet_params

													SCREAMING_SNAKE_CASE__     :				Dict   =					"bird"
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					jax.device_count()
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					pipe.prepare_text_inputs([prompts] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					pipe.prepare_image_inputs([canny_image] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					jax.random.PRNGKey(0    )
													SCREAMING_SNAKE_CASE__     :				int   =					jax.random.split(a							,	jax.device_count()    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					replicate(a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					shard(a    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					shard(a    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					pipe(
													    prompt_ids=a							,	image=a							,	params=a							,	prng_seed=a							,	num_inference_steps=50							,	jit=a							,	).images
													assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					images[0, 2_53:2_56, 2_53:2_56, -1]

													SCREAMING_SNAKE_CASE__     :				Tuple   =					jnp.asarray(jax.device_get(image_slice.flatten()    )    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					jnp.array(
													    [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]    )
													print(f"""output_slice: {output_slice}"""    )
													assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2






							def      A_  (      self				:     List[Any]    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				int   =					FlaxControlNetModel.from_pretrained(
													    "lllyasviel/sd-controlnet-openpose"							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					FlaxStableDiffusionControlNetPipeline.from_pretrained(
													    "runwayml/stable-diffusion-v1-5"							,	controlnet=a							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					controlnet_params

													SCREAMING_SNAKE_CASE__     :				Any   =					"Chef in the kitchen"
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jax.device_count()
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					pipe.prepare_text_inputs([prompts] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"    )
													SCREAMING_SNAKE_CASE__     :				str   =					pipe.prepare_image_inputs([pose_image] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				Any   =					jax.random.PRNGKey(0    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					jax.random.split(a							,	jax.device_count()    )

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					replicate(a    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					shard(a    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					shard(a    )

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					pipe(
													    prompt_ids=a							,	image=a							,	params=a							,	prng_seed=a							,	num_inference_steps=50							,	jit=a							,	).images
													assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
													SCREAMING_SNAKE_CASE__     :				str   =					images[0, 2_53:2_56, 2_53:2_56, -1]

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jnp.asarray(jax.device_get(image_slice.flatten()    )    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jnp.array(
													    [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]    )
													print(f"""output_slice: {output_slice}"""    )
													assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2