





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,
)


__A				:    Optional[int]       =   logging.get_logger(__name__)  # pylint: disable=invalid-name

__A				:    int       =   "\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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__=8 )  ->				str:







			'''simple docstring'''

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






class 	A_       (a_			):
	def __init__(    self							,					_A							,					_A							,					_A							,					):






				'''simple docstring'''







				super().__init__()

				self.register_modules(
				    unet=_A							,					scheduler=_A							,					movq=_A							,					)
				UpperCAmelCase   		=				2 ** (len(self.movq.config.block_out_channels		) - 1)
	def      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A							,					_A							,					_A		):






				'''simple docstring'''







				if latents is None:
							UpperCAmelCase   		=				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}"""		)
							UpperCAmelCase   		=				latents.to(_A		)

				UpperCAmelCase   		=				latents * scheduler.init_noise_sigma
				return latents
	def      _lowercase				(    self							,					_A=0		):






				'''simple docstring'''







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

				UpperCAmelCase   		=				torch.device(F"""cuda:{gpu_id}"""		)

				UpperCAmelCase   		=				[
				    self.unet,
				    self.movq,
				]
				for cpu_offloaded_model in models:
							if cpu_offloaded_model is not None:
										cpu_offload(_A							,					_A		)
	def      _lowercase				(    self							,					_A=0		):






				'''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.'''		)

				UpperCAmelCase   		=				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)

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

				# We'll offload the last model manually.
				UpperCAmelCase   		=				hook
	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def      _lowercase				(    self		):






				'''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							,					_A							,					_A							,					_A							,					_A = 5_1_2							,					_A = 5_1_2							,					_A = 1_0_0							,					_A = 4.0							,					_A = 1							,					_A = None							,					_A = None							,					_A = "pil"							,					_A = True							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				self._execution_device

				UpperCAmelCase   		=				guidance_scale > 1.0

				if isinstance(_A							,					_A		):
							UpperCAmelCase   		=				torch.cat(_A							,					dim=0		)
				if isinstance(_A							,					_A		):
							UpperCAmelCase   		=				torch.cat(_A							,					dim=0		)
				if isinstance(_A							,					_A		):
							UpperCAmelCase   		=				torch.cat(_A							,					dim=0		)

				UpperCAmelCase   		=				image_embeds.shape[0] * num_images_per_prompt

				if do_classifier_free_guidance:
							UpperCAmelCase   		=				image_embeds.repeat_interleave(_A							,					dim=0		)
							UpperCAmelCase   		=				negative_image_embeds.repeat_interleave(_A							,					dim=0		)
							UpperCAmelCase   		=				hint.repeat_interleave(_A							,					dim=0		)

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

				UpperCAmelCase   		=				self.movq.config.latent_channels

				UpperCAmelCase		,			UpperCAmelCase   		=				downscale_height_and_width(_A							,					_A							,					self.movq_scale_factor		)

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

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

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

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

				if output_type == "pil":
							UpperCAmelCase   		=				self.numpy_to_pil(_A		)

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=_A		)










from typing import TYPE_CHECKING

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


__A				:    Dict       =   {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    Union[str, Any]       =   ["RoFormerTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    str       =   [
				    "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "RoFormerForCausalLM",
				    "RoFormerForMaskedLM",
				    "RoFormerForMultipleChoice",
				    "RoFormerForQuestionAnswering",
				    "RoFormerForSequenceClassification",
				    "RoFormerForTokenClassification",
				    "RoFormerLayer",
				    "RoFormerModel",
				    "RoFormerPreTrainedModel",
				    "load_tf_weights_in_roformer",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    int       =   [
				    "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFRoFormerForCausalLM",
				    "TFRoFormerForMaskedLM",
				    "TFRoFormerForMultipleChoice",
				    "TFRoFormerForQuestionAnswering",
				    "TFRoFormerForSequenceClassification",
				    "TFRoFormerForTokenClassification",
				    "TFRoFormerLayer",
				    "TFRoFormerModel",
				    "TFRoFormerPreTrainedModel",
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    Any       =   [
				    "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "FlaxRoFormerForMaskedLM",
				    "FlaxRoFormerForMultipleChoice",
				    "FlaxRoFormerForQuestionAnswering",
				    "FlaxRoFormerForSequenceClassification",
				    "FlaxRoFormerForTokenClassification",
				    "FlaxRoFormerModel",
				    "FlaxRoFormerPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
				from .tokenization_roformer import RoFormerTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_roformer_fast import RoFormerTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_roformer import (
								    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RoFormerForCausalLM,
								    RoFormerForMaskedLM,
								    RoFormerForMultipleChoice,
								    RoFormerForQuestionAnswering,
								    RoFormerForSequenceClassification,
								    RoFormerForTokenClassification,
								    RoFormerLayer,
								    RoFormerModel,
								    RoFormerPreTrainedModel,
								    load_tf_weights_in_roformer,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_roformer import (
								    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRoFormerForCausalLM,
								    TFRoFormerForMaskedLM,
								    TFRoFormerForMultipleChoice,
								    TFRoFormerForQuestionAnswering,
								    TFRoFormerForSequenceClassification,
								    TFRoFormerForTokenClassification,
								    TFRoFormerLayer,
								    TFRoFormerModel,
								    TFRoFormerPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_roformer import (
								    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FlaxRoFormerForMaskedLM,
								    FlaxRoFormerForMultipleChoice,
								    FlaxRoFormerForQuestionAnswering,
								    FlaxRoFormerForSequenceClassification,
								    FlaxRoFormerForTokenClassification,
								    FlaxRoFormerModel,
								    FlaxRoFormerPreTrainedModel,
								)


else:
				import sys

				__A				:    int       =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



