


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,
)


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

lowerCAmelCase	=							'\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> 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\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n'
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=8					):


		"""simple docstring"""




		lowercase__						=				height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		lowercase__						=				width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor


class 	_a							(      UpperCamelCase__      ):

	def __init__(							self:     str   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     Optional[int]   ,						)      ->       Optional[int]:





			"""simple docstring"""







			super().__init__()

			self.register_modules(
			    unet=_SCREAMING_SNAKE_CASE   ,						scheduler=_SCREAMING_SNAKE_CASE   ,						movq=_SCREAMING_SNAKE_CASE   ,						)
			lowercase__						=				2 ** (len(self.movq.config.block_out_channels							) - 1)

	def 				lowerCamelCase_			(							self:     int   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     List[Any]   ,						UpperCamelCase_:     Any   ,						UpperCamelCase_:     Optional[Any]							)      ->       int:





			"""simple docstring"""







			if latents is None:
					lowercase__						=				randn_tensor(_SCREAMING_SNAKE_CASE   ,						generator=_SCREAMING_SNAKE_CASE   ,						device=_SCREAMING_SNAKE_CASE   ,						dtype=_SCREAMING_SNAKE_CASE							)
			else:
					if latents.shape != shape:
							raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'							)
					lowercase__						=				latents.to(_SCREAMING_SNAKE_CASE							)

			lowercase__						=				latents * scheduler.init_noise_sigma
			return latents

	def 				lowerCamelCase_			(							self:     Any   ,						UpperCamelCase_:     Optional[Any]=0							)      ->       int:





			"""simple docstring"""







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

			lowercase__						=				torch.device(f'cuda:{gpu_id}'							)

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

	def 				lowerCamelCase_			(							self:     Dict   ,						UpperCamelCase_:     Optional[int]=0							)      ->       Dict:





			"""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.'''							)

			lowercase__						=				torch.device(f'cuda:{gpu_id}'							)

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

			lowercase__						=				None
			for cpu_offloaded_model in [self.unet, self.movq]:
					lowercase__						=				cpu_offload_with_hook(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						prev_module_hook=_SCREAMING_SNAKE_CASE							)

			# We'll offload the last model manually.
			lowercase__						=				hook

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





			"""simple docstring"""







			if not hasattr(self.unet   ,						'''_hf_hook'''							):
					return self.device
			for module in self.unet.modules():
					if (
					    hasattr(_SCREAMING_SNAKE_CASE   ,						'''_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(_SCREAMING_SNAKE_CASE							)
	def __call__(							self:     Union[str, Any]   ,						UpperCamelCase_:     Optional[Any]   ,						UpperCamelCase_:     Union[str, Any]   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     Dict = 512   ,						UpperCamelCase_:     Dict = 512   ,						UpperCamelCase_:     str = 100   ,						UpperCamelCase_:     Dict = 4.0   ,						UpperCamelCase_:     int = 1   ,						UpperCamelCase_:     Optional[Any] = None   ,						UpperCamelCase_:     Dict = None   ,						UpperCamelCase_:     Any = "pil"   ,						UpperCamelCase_:     Any = True   ,						)      ->       List[Any]:





			"""simple docstring"""







			lowercase__						=				self._execution_device

			lowercase__						=				guidance_scale > 1.0

			if isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							):
					lowercase__						=				torch.cat(_SCREAMING_SNAKE_CASE   ,						dim=0							)
			if isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							):
					lowercase__						=				torch.cat(_SCREAMING_SNAKE_CASE   ,						dim=0							)
			if isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							):
					lowercase__						=				torch.cat(_SCREAMING_SNAKE_CASE   ,						dim=0							)

			lowercase__						=				image_embeds.shape[0] * num_images_per_prompt

			if do_classifier_free_guidance:
					lowercase__						=				image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE   ,						dim=0							)
					lowercase__						=				negative_image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE   ,						dim=0							)
					lowercase__						=				hint.repeat_interleave(_SCREAMING_SNAKE_CASE   ,						dim=0							)

			lowercase__						=				torch.cat([negative_image_embeds, image_embeds]   ,						dim=0							).to(dtype=self.unet.dtype   ,						device=_SCREAMING_SNAKE_CASE							)
			lowercase__						=				torch.cat([hint, hint]   ,						dim=0							).to(dtype=self.unet.dtype   ,						device=_SCREAMING_SNAKE_CASE							)
			self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE   ,						device=_SCREAMING_SNAKE_CASE							)
			lowercase__						=				self.scheduler.timesteps

			lowercase__						=				self.movq.config.latent_channels

			lowercase__						=				downscale_height_and_width(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						self.movq_scale_factor							)

			# create initial latent
			lowercase__						=				self.prepare_latents(
			    (batch_size, num_channels_latents, height, width)   ,						image_embeds.dtype   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						self.scheduler   ,						)

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

					lowercase__						=				{'''image_embeds''': image_embeds, '''hint''': hint}
					lowercase__						=				self.unet(
					    sample=_SCREAMING_SNAKE_CASE   ,						timestep=_SCREAMING_SNAKE_CASE   ,						encoder_hidden_states=_SCREAMING_SNAKE_CASE   ,						added_cond_kwargs=_SCREAMING_SNAKE_CASE   ,						return_dict=_SCREAMING_SNAKE_CASE   ,						)[0]

					if do_classifier_free_guidance:
							lowercase__						=				noise_pred.split(latents.shape[1]   ,						dim=1							)
							lowercase__						=				noise_pred.chunk(2							)
							lowercase__						=				variance_pred.chunk(2							)
							lowercase__						=				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
							lowercase__						=				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"]
					):
							lowercase__						=				noise_pred.split(latents.shape[1]   ,						dim=1							)

					# compute the previous noisy sample x_t -> x_t-1
					lowercase__						=				self.scheduler.step(
					    _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						generator=_SCREAMING_SNAKE_CASE   ,						)[0]
			# post-processing
			lowercase__						=				self.movq.decode(_SCREAMING_SNAKE_CASE   ,						force_not_quantize=_SCREAMING_SNAKE_CASE							)['''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"]:
					lowercase__						=				image * 0.5 + 0.5
					lowercase__						=				image.clamp(0   ,						1							)
					lowercase__						=				image.cpu().permute(0   ,						2   ,						3   ,						1							).float().numpy()

			if output_type == "pil":
					lowercase__						=				self.numpy_to_pil(_SCREAMING_SNAKE_CASE							)

			if not return_dict:
					return (image,)

			return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE							)






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


lowerCAmelCase	=							'Create a default config file for Accelerate with only a few flags set.'
def       _a			(				SCREAMING_SNAKE_CASE="no"						,    SCREAMING_SNAKE_CASE = default_json_config_file						,    SCREAMING_SNAKE_CASE = False					):


		"""simple docstring"""




		lowercase__						=				Path(SCREAMING_SNAKE_CASE					)
		path.parent.mkdir(parents=SCREAMING_SNAKE_CASE						,    exist_ok=SCREAMING_SNAKE_CASE					)
		if path.exists():
				print(
				    f'Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'					)
				return False
		lowercase__						=				mixed_precision.lower()
		if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
				raise ValueError(
				    f'`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'					)
		lowercase__						=				{
		    '''compute_environment''': '''LOCAL_MACHINE''',
		    '''mixed_precision''': mixed_precision,
		}
		if torch.cuda.is_available():
				lowercase__						=				torch.cuda.device_count()
				lowercase__						=				num_gpus
				lowercase__						=				False
				if num_gpus > 1:
						lowercase__						=				'''MULTI_GPU'''
				else:
						lowercase__						=				'''NO'''
		elif is_xpu_available() and use_xpu:
				lowercase__						=				torch.xpu.device_count()
				lowercase__						=				num_xpus
				lowercase__						=				False
				if num_xpus > 1:
						lowercase__						=				'''MULTI_XPU'''
				else:
						lowercase__						=				'''NO'''
		elif is_npu_available():
				lowercase__						=				torch.npu.device_count()
				lowercase__						=				num_npus
				lowercase__						=				False
				if num_npus > 1:
						lowercase__						=				'''MULTI_NPU'''
				else:
						lowercase__						=				'''NO'''
		else:
				lowercase__						=				0
				lowercase__						=				True
				lowercase__						=				1
				lowercase__						=				'''NO'''
		lowercase__						=				ClusterConfig(**SCREAMING_SNAKE_CASE					)
		config.to_json_file(SCREAMING_SNAKE_CASE					)
		return path
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				parser.add_parser('''default'''						,    parents=SCREAMING_SNAKE_CASE						,    help=SCREAMING_SNAKE_CASE						,    formatter_class=SCREAMING_SNAKE_CASE					)
		parser.add_argument(
		    '''--config_file'''						,    default=SCREAMING_SNAKE_CASE						,    help=(
		        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
		        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
		        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
		        '''with \'huggingface\'.'''
		    )						,    dest='''save_location'''						,    )

		parser.add_argument(
		    '''--mixed_precision'''						,    choices=['''no''', '''fp16''', '''bf16''']						,    type=SCREAMING_SNAKE_CASE						,    help='''Whether or not to use mixed precision training. '''
		    '''Choose between FP16 and BF16 (bfloat16) training. '''
		    '''BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'''						,    default='''no'''						,    )
		parser.set_defaults(func=SCREAMING_SNAKE_CASE					)
		return parser
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				write_basic_config(args.mixed_precision						,    args.save_location					)
		if config_file:
				print(f'accelerate configuration saved at {config_file}'					)


