






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase  							=					{
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							=					["DebertaTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							=					[
	    "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "DebertaForMaskedLM",
	    "DebertaForQuestionAnswering",
	    "DebertaForSequenceClassification",
	    "DebertaForTokenClassification",
	    "DebertaModel",
	    "DebertaPreTrainedModel",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							=					[
	    "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFDebertaForMaskedLM",
	    "TFDebertaForQuestionAnswering",
	    "TFDebertaForSequenceClassification",
	    "TFDebertaForTokenClassification",
	    "TFDebertaModel",
	    "TFDebertaPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
	from .tokenization_deberta import DebertaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_deberta_fast import DebertaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deberta import (
		    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DebertaForMaskedLM,
		    DebertaForQuestionAnswering,
		    DebertaForSequenceClassification,
		    DebertaForTokenClassification,
		    DebertaModel,
		    DebertaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_deberta import (
		    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDebertaForMaskedLM,
		    TFDebertaForQuestionAnswering,
		    TFDebertaForSequenceClassification,
		    TFDebertaForTokenClassification,
		    TFDebertaModel,
		    TFDebertaPreTrainedModel,
		)


else:
	import sys

	lowercase  							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)













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,
)


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

lowercase  							=					"\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 						__UpperCAmelCase			(     a_							,     a_							,     a_=8):
							snake_case_				=							height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							snake_case_				=							width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a       ,			a       ,			)       ->    Tuple:
								super().__init__()

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




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a					)       ->    Any:
								if latents is None:
															snake_case_				=							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_				=							latents.to(a					)

								snake_case_				=							latents * scheduler.init_noise_sigma
								return latents




	def 							_UpperCamelCase			(       self       ,			a=0					)       ->    str:
								if is_accelerate_available():
															from accelerate import cpu_offload
								else:
															raise ImportError('Please install accelerate via `pip install accelerate`'					)

								snake_case_				=							torch.device(F'''cuda:{gpu_id}'''					)

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




	def 							_UpperCamelCase			(       self       ,			a=0					)       ->    List[str]:
								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_				=							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_				=							None
								for cpu_offloaded_model in [self.unet, self.movq]:
															snake_case_			,		snake_case_				=							cpu_offload_with_hook(a       ,			a       ,			prev_module_hook=a					)

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




	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def 							_UpperCamelCase			(       self					)       ->    Any:
								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       ,			a       ,			a       ,			a       ,			a = 5_12       ,			a = 5_12       ,			a = 1_00       ,			a = 4.0       ,			a = 1       ,			a = None       ,			a = None       ,			a = "pil"       ,			a = True       ,			)       ->    List[str]:
								snake_case_				=							self._execution_device

								snake_case_				=							guidance_scale > 1.0

								if isinstance(a       ,			a					):
															snake_case_				=							torch.cat(a       ,			dim=0					)
								if isinstance(a       ,			a					):
															snake_case_				=							torch.cat(a       ,			dim=0					)
								if isinstance(a       ,			a					):
															snake_case_				=							torch.cat(a       ,			dim=0					)

								snake_case_				=							image_embeds.shape[0] * num_images_per_prompt

								if do_classifier_free_guidance:
															snake_case_				=							image_embeds.repeat_interleave(a       ,			dim=0					)
															snake_case_				=							negative_image_embeds.repeat_interleave(a       ,			dim=0					)
															snake_case_				=							hint.repeat_interleave(a       ,			dim=0					)

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

								snake_case_				=							self.movq.config.latent_channels

								snake_case_			,		snake_case_				=							downscale_height_and_width(a       ,			a       ,			self.movq_scale_factor					)

								# create initial latent
								snake_case_				=							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_				=							torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

															snake_case_				=							{'image_embeds': image_embeds, 'hint': hint}
															snake_case_				=							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_				=							noise_pred.split(latents.shape[1]       ,			dim=1					)
																						snake_case_			,		snake_case_				=							noise_pred.chunk(2					)
																						snake_case_			,		snake_case_				=							variance_pred.chunk(2					)
																						snake_case_				=							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																						snake_case_				=							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_				=							noise_pred.split(latents.shape[1]       ,			dim=1					)

															# compute the previous noisy sample x_t -> x_t-1
															snake_case_				=							self.scheduler.step(
															    a       ,			a       ,			a       ,			generator=a       ,			)[0]
								# post-processing
								snake_case_				=							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_				=							image * 0.5 + 0.5
															snake_case_				=							image.clamp(0       ,			1					)
															snake_case_				=							image.cpu().permute(0       ,			2       ,			3       ,			1					).float().numpy()

								if output_type == "pil":
															snake_case_				=							self.numpy_to_pil(a					)

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a					)





