





from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)  # pylint: disable=invalid-name

_SCREAMING_SNAKE_CASE      	=		"""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")


        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a=8   ):
	snake_case_		:			Optional[int]											=	height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	snake_case_		:			str											=	width // scale_factor**2
	if width % scale_factor**2 != 0:
		new_width += 1
	return new_height * scale_factor, new_width * scale_factor


class SCREAMING_SNAKE_CASE_						(					snake_case_					):






						def __init__(      self      :      Tuple		,   _A      :      UNetaDConditionModel		,   _A      :      DDPMScheduler		,   _A      :      VQModel		,   )							->    List[str]:







							"""simple docstring"""


							super().__init__()

							self.register_modules(
							    unet=_A		,   scheduler=_A		,   movq=_A		,   )
							snake_case_		:			Tuple											=	2 ** (len(self.movq.config.block_out_channels	) - 1)






						def UpperCAmelCase_				(      self      :      Any		,   _A      :      Dict		,   _A      :      Optional[Any]		,   _A      :      Union[str, Any]		,   _A      :      Union[str, Any]		,   _A      :      Dict		,   _A      :      List[Any]	)							->    List[str]:







							"""simple docstring"""


							if latents is None:
								snake_case_		:			Optional[int]											=	randn_tensor(_A		,   generator=_A		,   device=_A		,   dtype=_A	)
							else:
								if latents.shape != shape:
									raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""	)
								snake_case_		:			List[str]											=	latents.to(_A	)

							snake_case_		:			Union[str, Any]											=	latents * scheduler.init_noise_sigma
							return latents






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      List[str]=0	)							->    Optional[int]:







							"""simple docstring"""


							if is_accelerate_available():
								from accelerate import cpu_offload
							else:
								raise ImportError('Please install accelerate via `pip install accelerate`'	)

							snake_case_		:			Optional[int]											=	torch.device(F"""cuda:{gpu_id}"""	)

							snake_case_		:			Any											=	[
							    self.unet,
							    self.movq,
							]
							for cpu_offloaded_model in models:
								if cpu_offloaded_model is not None:
									cpu_offload(_A		,   _A	)






						def UpperCAmelCase_				(      self      :      int		,   _A      :      List[str]=0	)							->    int:







							"""simple docstring"""


							if is_accelerate_available() and is_accelerate_version('>='		,   '0.17.0.dev0'	):
								from accelerate import cpu_offload_with_hook
							else:
								raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'	)

							snake_case_		:			Union[str, Any]											=	torch.device(F"""cuda:{gpu_id}"""	)

							if self.device.type != "cpu":
								self.to('cpu'		,   silence_dtype_warnings=_A	)
								torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							snake_case_		:			str											=	None
							for cpu_offloaded_model in [self.unet, self.movq]:
								snake_case_						,snake_case_		:			Optional[int]											=	cpu_offload_with_hook(_A		,   _A		,   prev_module_hook=_A	)

							# We'll offload the last model manually.
							snake_case_		:			Tuple											=	hook






						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    List[str]:







							"""simple docstring"""


							if not hasattr(self.unet		,   '_hf_hook'	):
								return self.device
							for module in self.unet.modules():
								if (
								    hasattr(_A		,   '_hf_hook'	)
								    and hasattr(module._hf_hook		,   'execution_device'	)
								    and module._hf_hook.execution_device is not None
								):
									return torch.device(module._hf_hook.execution_device	)
							return self.device






						@torch.no_grad()
						@replace_example_docstring(_A	)
						def __call__(      self      :      str		,   _A      :      Union[torch.FloatTensor, List[torch.FloatTensor]]		,   _A      :      Union[torch.FloatTensor, List[torch.FloatTensor]]		,   _A      :      torch.FloatTensor		,   _A      :      int = 512		,   _A      :      int = 512		,   _A      :      int = 100		,   _A      :      float = 4.0		,   _A      :      int = 1		,   _A      :      Optional[Union[torch.Generator, List[torch.Generator]]] = None		,   _A      :      Optional[torch.FloatTensor] = None		,   _A      :      Optional[str] = "pil"		,   _A      :      bool = True		,   )							->    str:







							"""simple docstring"""


							snake_case_		:			Any											=	self._execution_device

							snake_case_		:			Tuple											=	guidance_scale > 1.0

							if isinstance(_A		,   _A	):
								snake_case_		:			int											=	torch.cat(_A		,   dim=0	)
							if isinstance(_A		,   _A	):
								snake_case_		:			Any											=	torch.cat(_A		,   dim=0	)
							if isinstance(_A		,   _A	):
								snake_case_		:			int											=	torch.cat(_A		,   dim=0	)

							snake_case_		:			Dict											=	image_embeds.shape[0] * num_images_per_prompt

							if do_classifier_free_guidance:
								snake_case_		:			int											=	image_embeds.repeat_interleave(_A		,   dim=0	)
								snake_case_		:			Optional[int]											=	negative_image_embeds.repeat_interleave(_A		,   dim=0	)
								snake_case_		:			int											=	hint.repeat_interleave(_A		,   dim=0	)

							snake_case_		:			Optional[Any]											=	torch.cat([negative_image_embeds, image_embeds]		,   dim=0	).to(dtype=self.unet.dtype		,   device=_A	)
							snake_case_		:			List[str]											=	torch.cat([hint, hint]		,   dim=0	).to(dtype=self.unet.dtype		,   device=_A	)
							self.scheduler.set_timesteps(_A		,   device=_A	)
							snake_case_		:			Tuple											=	self.scheduler.timesteps

							snake_case_		:			Union[str, Any]											=	self.movq.config.latent_channels

							snake_case_						,snake_case_		:			Union[str, Any]											=	downscale_height_and_width(_A		,   _A		,   self.movq_scale_factor	)

							# create initial latent
							snake_case_		:			Union[str, Any]											=	self.prepare_latents(
							    (batch_size, num_channels_latents, height, width)		,   image_embeds.dtype		,   _A		,   _A		,   _A		,   self.scheduler		,   )

							for i, t in enumerate(self.progress_bar(_A	)	):
								# expand the latents if we are doing classifier free guidance
								snake_case_		:			List[Any]											=	torch.cat([latents] * 2	) if do_classifier_free_guidance else latents

								snake_case_		:			Optional[Any]											=	{'image_embeds': image_embeds, 'hint': hint}
								snake_case_		:			int											=	self.unet(
								    sample=_A		,   timestep=_A		,   encoder_hidden_states=_A		,   added_cond_kwargs=_A		,   return_dict=_A		,   )[0]

								if do_classifier_free_guidance:
									snake_case_						,snake_case_		:			Optional[int]											=	noise_pred.split(latents.shape[1]		,   dim=1	)
									snake_case_						,snake_case_		:			Dict											=	noise_pred.chunk(2	)
									snake_case_						,snake_case_		:			Tuple											=	variance_pred.chunk(2	)
									snake_case_		:			Tuple											=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
									snake_case_		:			Optional[Any]											=	torch.cat([noise_pred, variance_pred_text]		,   dim=1	)

								if not (
								    hasattr(self.scheduler.config		,   'variance_type'	)
								    and self.scheduler.config.variance_type in ["learned", "learned_range"]
								):
									snake_case_						,snake_case_		:			Any											=	noise_pred.split(latents.shape[1]		,   dim=1	)

								# compute the previous noisy sample x_t -> x_t-1
								snake_case_		:			Any											=	self.scheduler.step(
								    _A		,   _A		,   _A		,   generator=_A		,   )[0]
							# post-processing
							snake_case_		:			List[Any]											=	self.movq.decode(_A		,   force_not_quantize=_A	)['sample']

							if output_type not in ["pt", "np", "pil"]:
								raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""	)

							if output_type in ["np", "pil"]:
								snake_case_		:			int											=	image * 0.5 + 0.5
								snake_case_		:			Any											=	image.clamp(0		,   1	)
								snake_case_		:			Optional[Any]											=	image.cpu().permute(0		,   2		,   3		,   1	).float().numpy()

							if output_type == "pil":
								snake_case_		:			List[Any]											=	self.numpy_to_pil(_A	)

							if not return_dict:
								return (image,)

							return ImagePipelineOutput(images=_A	)








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

import cva
import numpy as np

# Parrameters
_SCREAMING_SNAKE_CASE      	=		(7_20, 12_80)  # Height, Width
_SCREAMING_SNAKE_CASE      	=		(0.4, 0.6)  # if height or width lower than this scale, drop it.
_SCREAMING_SNAKE_CASE      	=		1 / 1_00
_SCREAMING_SNAKE_CASE      	=		""""""
_SCREAMING_SNAKE_CASE      	=		""""""
_SCREAMING_SNAKE_CASE      	=		""""""
_SCREAMING_SNAKE_CASE      	=		2_50


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_						,snake_case_		:			Any											=	get_dataset(__a   , __a   )
	for index in range(__a   ):
		snake_case_		:			Dict											=	random.sample(range(len(__a   )   )   , 4   )
		snake_case_						,snake_case_						,snake_case_		:			Optional[int]											=	update_image_and_anno(
		    __a   , __a   , __a   , __a   , __a   , filter_scale=__a   , )

		# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
		snake_case_		:			List[str]											=	random_chars(32   )
		snake_case_		:			Dict											=	path.split(os.sep   )[-1].rsplit('.'   , 1   )[0]
		snake_case_		:			Any											=	f"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
		cva.imwrite(f"""{file_root}.jpg"""   , __a   , [cva.IMWRITE_JPEG_QUALITY, 85]   )
		print(f"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"""   )
		snake_case_		:			Dict											=	[]
		for anno in new_annos:
			snake_case_		:			int											=	anno[3] - anno[1]
			snake_case_		:			Dict											=	anno[4] - anno[2]
			snake_case_		:			int											=	anno[1] + width / 2
			snake_case_		:			str											=	anno[2] + height / 2
			snake_case_		:			Union[str, Any]											=	f"""{anno[0]} {x_center} {y_center} {width} {height}"""
			annos_list.append(__a   )
		with open(f"""{file_root}.txt"""   , 'w'   ) as outfile:
			outfile.write('\n'.join(line for line in annos_list   )   )


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

		snake_case_		:			int											=	[]
		for obj_list in obj_lists:
			snake_case_		:			str											=	obj_list.rstrip('\n'   ).split(' '   )
			snake_case_		:			Tuple											=	float(obj[1]   ) - float(obj[3]   ) / 2
			snake_case_		:			str											=	float(obj[2]   ) - float(obj[4]   ) / 2
			snake_case_		:			Optional[Any]											=	float(obj[1]   ) + float(obj[3]   ) / 2
			snake_case_		:			List[Any]											=	float(obj[2]   ) + float(obj[4]   ) / 2

			boxes.append([int(obj[0]   ), xmin, ymin, xmax, ymax]   )
		if not boxes:
			continue
		img_paths.append(__a   )
		labels.append(__a   )
	return img_paths, labels


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   , __a   , __a = 0.0   , ):
	snake_case_		:			str											=	np.zeros([output_size[0], output_size[1], 3]   , dtype=np.uinta   )
	snake_case_		:			Tuple											=	scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
	snake_case_		:			int											=	scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
	snake_case_		:			Optional[Any]											=	int(scale_x * output_size[1]   )
	snake_case_		:			int											=	int(scale_y * output_size[0]   )

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


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

