




from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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


lowercase_		=							'''
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = "cuda"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample.png")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
        ```
'''





class 		A__    (       __SCREAMING_SNAKE_CASE			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase						)				->       None:




						"""simple docstring"""






						super().__init__()

						self.register_modules(
						    unet=lowerCamelCase    ,					scheduler=lowerCamelCase    ,					)

						__magic_name__ :							List[str]		       =							None






	def 			lowercase				(     self    ,					lowerCamelCase=0						)				->       int:




						"""simple docstring"""






						if is_accelerate_available() and is_accelerate_version('''>='''    ,					'''0.14.0'''						):
											from accelerate import cpu_offload
						else:
											raise ImportError('''`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'''						)

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

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

						for cpu_offloaded_model in [self.unet]:
											cpu_offload(lowerCamelCase    ,					lowerCamelCase						)

						if self.safety_checker is not None:
											cpu_offload(self.safety_checker    ,					execution_device=lowerCamelCase    ,					offload_buffers=lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase=0						)				->       Optional[Any]:




						"""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.'''						)

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

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

						__magic_name__ :							Union[str, Any]		       =							None
						for cpu_offloaded_model in [self.unet]:
											__magic_name__       ,			__magic_name__ :							Optional[Any]		       =							cpu_offload_with_hook(lowerCamelCase    ,					lowerCamelCase    ,					prev_module_hook=lowerCamelCase						)

						if self.safety_checker is not None:
											__magic_name__       ,			__magic_name__ :							Optional[int]		       =							cpu_offload_with_hook(self.safety_checker    ,					lowerCamelCase    ,					prev_module_hook=lowerCamelCase						)

						# We'll offload the last model manually.
						__magic_name__ :							List[Any]		       =							hook






	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def 			lowercase				(     self						)				->       Any:




						"""simple docstring"""






						if not hasattr(self.unet    ,					'''_hf_hook'''						):
											return self.device
						for module in self.unet.modules():
											if (
											    hasattr(lowerCamelCase    ,					'''_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






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       Any:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							(batch_size, num_channels, height, width)
						if isinstance(lowerCamelCase    ,					lowerCamelCase						) and len(lowerCamelCase						) != batch_size:
											raise ValueError(
											    F'''You have passed a list of generators of length {len(lowerCamelCase						)}, but requested an effective batch'''
											    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''						)

						if latents is None:
											__magic_name__ :							List[Any]		       =							randn_tensor(lowerCamelCase    ,					generator=lowerCamelCase    ,					device=lowerCamelCase    ,					dtype=lowerCamelCase						)
						else:
											__magic_name__ :							int		       =							latents.to(device=lowerCamelCase    ,					dtype=lowerCamelCase						)

						# scale the initial noise by the standard deviation required by the scheduler
						__magic_name__ :							Tuple		       =							latents * self.scheduler.init_noise_sigma
						return latents






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase = "pil"						)				->       List[Any]:




						"""simple docstring"""






						if output_type not in ["pt", "np", "pil"]:
											raise ValueError(
											    F'''output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'''						)

						# Equivalent to diffusers.VaeImageProcessor.denormalize
						__magic_name__ :							str		       =							(sample / 2 + 0.5).clamp(0    ,					1						)
						if output_type == "pt":
											return sample

						# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
						__magic_name__ :							Dict		       =							sample.cpu().permute(0    ,					2    ,					3    ,					1						).numpy()
						if output_type == "np":
											return sample

						# Output_type must be 'pil'
						__magic_name__ :							Optional[int]		       =							self.numpy_to_pil(lowerCamelCase						)
						return sample






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       int:




						"""simple docstring"""






						if self.unet.config.num_class_embeds is not None:
											if isinstance(lowerCamelCase    ,					lowerCamelCase						):
																__magic_name__ :							List[str]		       =							torch.tensor(lowerCamelCase    ,					dtype=torch.int						)
											elif isinstance(lowerCamelCase    ,					lowerCamelCase						):
																assert batch_size == 1, "Batch size must be 1 if classes is an int"
																__magic_name__ :							Union[str, Any]		       =							torch.tensor([class_labels]    ,					dtype=torch.int						)
											elif class_labels is None:
																# Randomly generate batch_size class labels
																# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
																__magic_name__ :							Any		       =							torch.randint(0    ,					self.unet.config.num_class_embeds    ,					size=(batch_size,)						)
											__magic_name__ :							Tuple		       =							class_labels.to(lowerCamelCase						)
						else:
											__magic_name__ :							Any		       =							None
						return class_labels






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)				->       Any:




						"""simple docstring"""






						if num_inference_steps is None and timesteps is None:
											raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''						)

						if num_inference_steps is not None and timesteps is not None:
											logger.warning(
											    F'''Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'''
											    ''' `timesteps` will be used over `num_inference_steps`.'''						)

						if latents is not None:
											__magic_name__ :							Union[str, Any]		       =							(batch_size, 3, img_size, img_size)
											if latents.shape != expected_shape:
																raise ValueError(F'''The shape of latents is {latents.shape} but is expected to be {expected_shape}.'''						)

						if (callback_steps is None) or (
						    callback_steps is not None and (not isinstance(lowerCamelCase    ,					lowerCamelCase						) or callback_steps <= 0)
						):
											raise ValueError(
											    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
											    F''' {type(lowerCamelCase						)}.'''						)






	@torch.no_grad()
	@replace_example_docstring(lowerCamelCase						)
	def __call__(     self    ,					lowerCamelCase = 1    ,					lowerCamelCase = None    ,					lowerCamelCase = 1    ,					lowerCamelCase = None    ,					lowerCamelCase = None    ,					lowerCamelCase = None    ,					lowerCamelCase = "pil"    ,					lowerCamelCase = True    ,					lowerCamelCase = None    ,					lowerCamelCase = 1    ,					)				->       Any:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							self.unet.config.sample_size
						__magic_name__ :							List[Any]		       =							self._execution_device

						# 1. Check inputs
						self.check_inputs(lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)

						# 2. Prepare image latents
						# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
						__magic_name__ :							List[str]		       =							self.prepare_latents(
						    batch_size=lowerCamelCase    ,					num_channels=self.unet.config.in_channels    ,					height=lowerCamelCase    ,					width=lowerCamelCase    ,					dtype=self.unet.dtype    ,					device=lowerCamelCase    ,					generator=lowerCamelCase    ,					latents=lowerCamelCase    ,					)

						# 3. Handle class_labels for class-conditional models
						__magic_name__ :							Optional[Any]		       =							self.prepare_class_labels(lowerCamelCase    ,					lowerCamelCase    ,					class_labels=lowerCamelCase						)

						# 4. Prepare timesteps
						if timesteps is not None:
											self.scheduler.set_timesteps(timesteps=lowerCamelCase    ,					device=lowerCamelCase						)
											__magic_name__ :							Tuple		       =							self.scheduler.timesteps
											__magic_name__ :							List[Any]		       =							len(lowerCamelCase						)
						else:
											self.scheduler.set_timesteps(lowerCamelCase						)
											__magic_name__ :							Any		       =							self.scheduler.timesteps

						# 5. Denoising loop
						# Multistep sampling: implements Algorithm 1 in the paper
						with self.progress_bar(total=lowerCamelCase						) as progress_bar:
											for i, t in enumerate(lowerCamelCase						):
																__magic_name__ :							str		       =							self.scheduler.scale_model_input(lowerCamelCase    ,					lowerCamelCase						)
																__magic_name__ :							Dict		       =							self.unet(lowerCamelCase    ,					lowerCamelCase    ,					class_labels=lowerCamelCase    ,					return_dict=lowerCamelCase						)[0]

																__magic_name__ :							Union[str, Any]		       =							self.scheduler.step(lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					generator=lowerCamelCase						)[0]

																# call the callback, if provided
																progress_bar.update()
																if callback is not None and i % callback_steps == 0:
																					callback(lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)

        # 6. Post-process image sample
						__magic_name__ :							List[str]		       =							self.postprocess_image(lowerCamelCase    ,					output_type=lowerCamelCase						)

						# Offload last model to CPU
						if hasattr(self    ,					'''final_offload_hook'''						) and self.final_offload_hook is not None:
											self.final_offload_hook.offload()

						if not return_dict:
											return (image,)

						return ImagePipelineOutput(images=lowerCamelCase						)









import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase = False,     UpperCAmelCase = 1,     UpperCAmelCase = 1,     UpperCAmelCase = 1_0000,     )  ->List[Any]:






					"""simple docstring"""






					assert len(timesteps.shape	) == 1, "Timesteps should be a 1d-array"

					__magic_name__ :							List[str]		       =							embedding_dim // 2
					__magic_name__ :							Dict		       =							-math.log(UpperCAmelCase	) * torch.arange(
					    start=0,     end=UpperCAmelCase,     dtype=torch.floataa,     device=timesteps.device	)
					__magic_name__ :							Any		       =							exponent / (half_dim - downscale_freq_shift)

					__magic_name__ :							Tuple		       =							torch.exp(UpperCAmelCase	)
					__magic_name__ :							Tuple		       =							timesteps[:, None].float() * emb[None, :]

					# scale embeddings
					__magic_name__ :							Optional[Any]		       =							scale * emb

					# concat sine and cosine embeddings
					__magic_name__ :							Optional[int]		       =							torch.cat([torch.sin(UpperCAmelCase	), torch.cos(UpperCAmelCase	)],     dim=-1	)

					# flip sine and cosine embeddings
					if flip_sin_to_cos:
										__magic_name__ :							Tuple		       =							torch.cat([emb[:, half_dim:], emb[:, :half_dim]],     dim=-1	)

					# zero pad
					if embedding_dim % 2 == 1:
										__magic_name__ :							List[str]		       =							torch.nn.functional.pad(UpperCAmelCase,     (0, 1, 0, 0)	)
					return emb







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase=False,     UpperCAmelCase=0	)  ->Dict:






					"""simple docstring"""






					__magic_name__ :							Any		       =							np.arange(UpperCAmelCase,     dtype=np.floataa	)
					__magic_name__ :							List[str]		       =							np.arange(UpperCAmelCase,     dtype=np.floataa	)
					__magic_name__ :							List[str]		       =							np.meshgrid(UpperCAmelCase,     UpperCAmelCase	)  # here w goes first
					__magic_name__ :							Tuple		       =							np.stack(UpperCAmelCase,     axis=0	)

					__magic_name__ :							Any		       =							grid.reshape([2, 1, grid_size, grid_size]	)
					__magic_name__ :							Dict		       =							get_ad_sincos_pos_embed_from_grid(UpperCAmelCase,     UpperCAmelCase	)
					if cls_token and extra_tokens > 0:
										__magic_name__ :							List[Any]		       =							np.concatenate([np.zeros([extra_tokens, embed_dim]	), pos_embed],     axis=0	)
					return pos_embed







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase	)  ->List[Any]:






					"""simple docstring"""






					if embed_dim % 2 != 0:
										raise ValueError('''embed_dim must be divisible by 2'''	)

					# use half of dimensions to encode grid_h
					__magic_name__ :							Union[str, Any]		       =							get_ad_sincos_pos_embed_from_grid(embed_dim // 2,     grid[0]	)  # (H*W, D/2)
					__magic_name__ :							Dict		       =							get_ad_sincos_pos_embed_from_grid(embed_dim // 2,     grid[1]	)  # (H*W, D/2)

					__magic_name__ :							Tuple		       =							np.concatenate([emb_h, emb_w],     axis=1	)  # (H*W, D)
					return emb







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase	)  ->List[Any]:






					"""simple docstring"""






					if embed_dim % 2 != 0:
										raise ValueError('''embed_dim must be divisible by 2'''	)

					__magic_name__ :							str		       =							np.arange(embed_dim // 2,     dtype=np.floataa	)
					omega /= embed_dim / 2.0
					__magic_name__ :							str		       =							1.0 / 1_0000**omega  # (D/2,)

					__magic_name__ :							List[str]		       =							pos.reshape(-1	)  # (M,)
					__magic_name__ :							Dict		       =							np.einsum('''m,d->md''',     UpperCAmelCase,     UpperCAmelCase	)  # (M, D/2), outer product

					__magic_name__ :							Union[str, Any]		       =							np.sin(UpperCAmelCase	)  # (M, D/2)
					__magic_name__ :							str		       =							np.cos(UpperCAmelCase	)  # (M, D/2)

					__magic_name__ :							int		       =							np.concatenate([emb_sin, emb_cos],     axis=1	)  # (M, D)
					return emb





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase=224    ,					lowerCamelCase=224    ,					lowerCamelCase=16    ,					lowerCamelCase=3    ,					lowerCamelCase=768    ,					lowerCamelCase=False    ,					lowerCamelCase=True    ,					lowerCamelCase=True    ,					)				->       Any:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							Dict		       =							(height // patch_size) * (width // patch_size)
						__magic_name__ :							Tuple		       =							flatten
						__magic_name__ :							List[Any]		       =							layer_norm

						__magic_name__ :							Optional[Any]		       =							nn.Convad(
						    lowerCamelCase    ,					lowerCamelCase    ,					kernel_size=(patch_size, patch_size)    ,					stride=lowerCamelCase    ,					bias=lowerCamelCase						)
						if layer_norm:
											__magic_name__ :							Optional[int]		       =							nn.LayerNorm(lowerCamelCase    ,					elementwise_affine=lowerCamelCase    ,					eps=1e-6						)
						else:
											__magic_name__ :							int		       =							None

						__magic_name__ :							List[Any]		       =							get_ad_sincos_pos_embed(lowerCamelCase    ,					int(num_patches**0.5						)						)
						self.register_buffer('''pos_embed'''    ,					torch.from_numpy(lowerCamelCase						).float().unsqueeze(0						)    ,					persistent=lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							self.proj(lowerCamelCase						)
						if self.flatten:
											__magic_name__ :							Dict		       =							latent.flatten(2						).transpose(1    ,					2						)  # BCHW -> BNC
						if self.layer_norm:
											__magic_name__ :							Tuple		       =							self.norm(lowerCamelCase						)
						return latent + self.pos_embed





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase = "silu"    ,					lowerCamelCase = None    ,					lowerCamelCase = None    ,					lowerCamelCase=None    ,					)				->       Tuple:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							Union[str, Any]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)

						if cond_proj_dim is not None:
											__magic_name__ :							Any		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase    ,					bias=lowerCamelCase						)
						else:
											__magic_name__ :							Union[str, Any]		       =							None

						__magic_name__ :							Union[str, Any]		       =							get_activation(lowerCamelCase						)

						if out_dim is not None:
											__magic_name__ :							Tuple		       =							out_dim
						else:
											__magic_name__ :							List[str]		       =							time_embed_dim
						__magic_name__ :							List[str]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)

						if post_act_fn is None:
											__magic_name__ :							List[str]		       =							None
						else:
											__magic_name__ :							Dict		       =							get_activation(lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       Tuple:




						"""simple docstring"""






						if condition is not None:
											__magic_name__ :							List[Any]		       =							sample + self.cond_proj(lowerCamelCase						)
						__magic_name__ :							List[str]		       =							self.linear_a(lowerCamelCase						)

						if self.act is not None:
											__magic_name__ :							str		       =							self.act(lowerCamelCase						)

						__magic_name__ :							Optional[int]		       =							self.linear_a(lowerCamelCase						)

						if self.post_act is not None:
											__magic_name__ :							List[str]		       =							self.post_act(lowerCamelCase						)
						return sample





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)				->       str:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							Any		       =							num_channels
						__magic_name__ :							Tuple		       =							flip_sin_to_cos
						__magic_name__ :							List[Any]		       =							downscale_freq_shift






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Any		       =							get_timestep_embedding(
						    lowerCamelCase    ,					self.num_channels    ,					flip_sin_to_cos=self.flip_sin_to_cos    ,					downscale_freq_shift=self.downscale_freq_shift    ,					)
						return t_emb





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 256    ,					lowerCamelCase = 1.0    ,					lowerCamelCase=True    ,					lowerCamelCase=True    ,					lowerCamelCase=False						)				->       int:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							Optional[int]		       =							nn.Parameter(torch.randn(lowerCamelCase						) * scale    ,					requires_grad=lowerCamelCase						)
						__magic_name__ :							List[str]		       =							log
						__magic_name__ :							Optional[int]		       =							flip_sin_to_cos

						if set_W_to_weight:
											# to delete later
											__magic_name__ :							Tuple		       =							nn.Parameter(torch.randn(lowerCamelCase						) * scale    ,					requires_grad=lowerCamelCase						)

											__magic_name__ :							List[str]		       =							self.W






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       List[str]:




						"""simple docstring"""






						if self.log:
											__magic_name__ :							Tuple		       =							torch.log(lowerCamelCase						)

						__magic_name__ :							Optional[int]		       =							x[:, None] * self.weight[None, :] * 2 * np.pi

						if self.flip_sin_to_cos:
											__magic_name__ :							Any		       =							torch.cat([torch.cos(lowerCamelCase						), torch.sin(lowerCamelCase						)]    ,					dim=-1						)
						else:
											__magic_name__ :							Dict		       =							torch.cat([torch.sin(lowerCamelCase						), torch.cos(lowerCamelCase						)]    ,					dim=-1						)
						return out





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					)				->       int:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							Tuple		       =							height
						__magic_name__ :							Dict		       =							width
						__magic_name__ :							Any		       =							num_embed
						__magic_name__ :							List[str]		       =							embed_dim

						__magic_name__ :							Tuple		       =							nn.Embedding(self.num_embed    ,					lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							nn.Embedding(self.height    ,					lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							nn.Embedding(self.width    ,					lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Optional[Any]		       =							self.emb(lowerCamelCase						)

						__magic_name__ :							Dict		       =							self.height_emb(torch.arange(self.height    ,					device=index.device						).view(1    ,					self.height						)						)

						# 1 x H x D -> 1 x H x 1 x D
						__magic_name__ :							Any		       =							height_emb.unsqueeze(2						)

						__magic_name__ :							Optional[int]		       =							self.width_emb(torch.arange(self.width    ,					device=index.device						).view(1    ,					self.width						)						)

						# 1 x W x D -> 1 x 1 x W x D
						__magic_name__ :							str		       =							width_emb.unsqueeze(1						)

						__magic_name__ :							Dict		       =							height_emb + width_emb

						# 1 x H x W x D -> 1 x L xD
						__magic_name__ :							Any		       =							pos_emb.view(1    ,					self.height * self.width    ,					-1						)

						__magic_name__ :							Optional[Any]		       =							emb + pos_emb[:, : emb.shape[1], :]

						return emb





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)				->       Union[str, Any]:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							List[Any]		       =							dropout_prob > 0
						__magic_name__ :							Optional[int]		       =							nn.Embedding(num_classes + use_cfg_embedding    ,					lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							num_classes
						__magic_name__ :							Union[str, Any]		       =							dropout_prob






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       Tuple:




						"""simple docstring"""






						if force_drop_ids is None:
											__magic_name__ :							Dict		       =							torch.rand(labels.shape[0]    ,					device=labels.device						) < self.dropout_prob
						else:
											__magic_name__ :							List[str]		       =							torch.tensor(force_drop_ids == 1						)
						__magic_name__ :							Optional[int]		       =							torch.where(lowerCamelCase    ,					self.num_classes    ,					lowerCamelCase						)
						return labels






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							self.dropout_prob > 0
						if (self.training and use_dropout) or (force_drop_ids is not None):
											__magic_name__ :							Optional[Any]		       =							self.token_drop(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							int		       =							self.embedding_table(lowerCamelCase						)
						return embeddings





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 1024    ,					lowerCamelCase = 768    ,					lowerCamelCase = 768    ,					lowerCamelCase = 10    ,					)				->       str:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							int		       =							num_image_text_embeds
						__magic_name__ :							Dict		       =							nn.Linear(lowerCamelCase    ,					self.num_image_text_embeds * cross_attention_dim						)
						__magic_name__ :							List[str]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Any		       =							text_embeds.shape[0]

						# image
						__magic_name__ :							Optional[Any]		       =							self.image_embeds(lowerCamelCase						)
						__magic_name__ :							Tuple		       =							image_text_embeds.reshape(lowerCamelCase    ,					self.num_image_text_embeds    ,					-1						)

						# text
						__magic_name__ :							Dict		       =							self.text_proj(lowerCamelCase						)

						return torch.cat([image_text_embeds, text_embeds]    ,					dim=1						)





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 768    ,					lowerCamelCase = 768    ,					lowerCamelCase = 32    ,					)				->       Union[str, Any]:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							List[Any]		       =							num_image_text_embeds
						__magic_name__ :							Dict		       =							nn.Linear(lowerCamelCase    ,					self.num_image_text_embeds * cross_attention_dim						)
						__magic_name__ :							List[Any]		       =							nn.LayerNorm(lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Any:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							image_embeds.shape[0]

						# image
						__magic_name__ :							Any		       =							self.image_embeds(lowerCamelCase						)
						__magic_name__ :							Tuple		       =							image_embeds.reshape(lowerCamelCase    ,					self.num_image_text_embeds    ,					-1						)
						__magic_name__ :							Union[str, Any]		       =							self.norm(lowerCamelCase						)
						return image_embeds





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase=0.1						)				->       List[str]:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							Dict		       =							Timesteps(num_channels=256    ,					flip_sin_to_cos=lowerCamelCase    ,					downscale_freq_shift=1						)
						__magic_name__ :							int		       =							TimestepEmbedding(in_channels=256    ,					time_embed_dim=lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							LabelEmbedding(lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							str		       =							self.time_proj(lowerCamelCase						)
						__magic_name__ :							Dict		       =							self.timestep_embedder(timesteps_proj.to(dtype=lowerCamelCase						)						)  # (N, D)

						__magic_name__ :							Optional[Any]		       =							self.class_embedder(lowerCamelCase						)  # (N, D)

						__magic_name__ :							Dict		       =							timesteps_emb + class_labels  # (N, D)

						return conditioning





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase = 64						)				->       str:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							List[Any]		       =							nn.LayerNorm(lowerCamelCase						)
						__magic_name__ :							Union[str, Any]		       =							AttentionPooling(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							Dict		       =							nn.LayerNorm(lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       List[Any]:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							self.norma(lowerCamelCase						)
						__magic_name__ :							Tuple		       =							self.pool(lowerCamelCase						)
						__magic_name__ :							Dict		       =							self.proj(lowerCamelCase						)
						__magic_name__ :							Dict		       =							self.norma(lowerCamelCase						)
						return hidden_states





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 768    ,					lowerCamelCase = 768    ,					lowerCamelCase = 1536						)				->       Tuple:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							Any		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							int		       =							nn.LayerNorm(lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase						)				->       str:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							self.text_proj(lowerCamelCase						)
						__magic_name__ :							Union[str, Any]		       =							self.text_norm(lowerCamelCase						)

						# image
						__magic_name__ :							Tuple		       =							self.image_proj(lowerCamelCase						)

						return time_image_embeds + time_text_embeds





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 768    ,					lowerCamelCase = 1536						)				->       Optional[int]:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							int		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							str		       =							nn.LayerNorm(lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       List[Any]:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							self.image_proj(lowerCamelCase						)
						__magic_name__ :							int		       =							self.image_norm(lowerCamelCase						)
						return time_image_embeds





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 768    ,					lowerCamelCase = 1536						)				->       List[str]:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							Optional[int]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							Optional[Any]		       =							nn.LayerNorm(lowerCamelCase						)
						__magic_name__ :							Any		       =							nn.Sequential(
						    nn.Convad(3    ,					16    ,					3    ,					padding=1						)    ,					nn.SiLU()    ,					nn.Convad(16    ,					16    ,					3    ,					padding=1						)    ,					nn.SiLU()    ,					nn.Convad(16    ,					32    ,					3    ,					padding=1    ,					stride=2						)    ,					nn.SiLU()    ,					nn.Convad(32    ,					32    ,					3    ,					padding=1						)    ,					nn.SiLU()    ,					nn.Convad(32    ,					96    ,					3    ,					padding=1    ,					stride=2						)    ,					nn.SiLU()    ,					nn.Convad(96    ,					96    ,					3    ,					padding=1						)    ,					nn.SiLU()    ,					nn.Convad(96    ,					256    ,					3    ,					padding=1    ,					stride=2						)    ,					nn.SiLU()    ,					nn.Convad(256    ,					4    ,					3    ,					padding=1						)    ,					)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase						)				->       Tuple:




						"""simple docstring"""






						__magic_name__ :							List[str]		       =							self.image_proj(lowerCamelCase						)
						__magic_name__ :							str		       =							self.image_norm(lowerCamelCase						)
						__magic_name__ :							Union[str, Any]		       =							self.input_hint_block(lowerCamelCase						)
						return time_image_embeds, hint





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       int:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							str		       =							dtype
						__magic_name__ :							str		       =							nn.Parameter(torch.randn(1    ,					lowerCamelCase						) / embed_dim**0.5						)
						__magic_name__ :							Tuple		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase    ,					dtype=self.dtype						)
						__magic_name__ :							str		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase    ,					dtype=self.dtype						)
						__magic_name__ :							Dict		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase    ,					dtype=self.dtype						)
						__magic_name__ :							Dict		       =							num_heads
						__magic_name__ :							List[Any]		       =							embed_dim // self.num_heads






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__       ,			__magic_name__       ,			__magic_name__ :							str		       =							x.size()

						def shape(lowerCamelCase						):
											# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
											__magic_name__ :							Union[str, Any]		       =							x.view(lowerCamelCase    ,					-1    ,					self.num_heads    ,					self.dim_per_head						)
											# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
											__magic_name__ :							str		       =							x.transpose(1    ,					2						)
											# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
											__magic_name__ :							List[str]		       =							x.reshape(bs * self.num_heads    ,					-1    ,					self.dim_per_head						)
											# (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
											__magic_name__ :							int		       =							x.transpose(1    ,					2						)
											return x

						__magic_name__ :							List[Any]		       =							x.mean(dim=1    ,					keepdim=lowerCamelCase						) + self.positional_embedding.to(x.dtype						)
						__magic_name__ :							int		       =							torch.cat([class_token, x]    ,					dim=1						)  # (bs, length+1, width)

						# (bs*n_heads, class_token_length, dim_per_head)
						__magic_name__ :							int		       =							shape(self.q_proj(lowerCamelCase						)						)
						# (bs*n_heads, length+class_token_length, dim_per_head)
						__magic_name__ :							str		       =							shape(self.k_proj(lowerCamelCase						)						)
						__magic_name__ :							Any		       =							shape(self.v_proj(lowerCamelCase						)						)

						# (bs*n_heads, class_token_length, length+class_token_length):
						__magic_name__ :							Optional[int]		       =							1 / math.sqrt(math.sqrt(self.dim_per_head						)						)
						__magic_name__ :							Union[str, Any]		       =							torch.einsum('''bct,bcs->bts'''    ,					q * scale    ,					k * scale						)  # More stable with f16 than dividing afterwards
						__magic_name__ :							Union[str, Any]		       =							torch.softmax(weight.float()    ,					dim=-1						).type(weight.dtype						)

						# (bs*n_heads, dim_per_head, class_token_length)
						__magic_name__ :							Dict		       =							torch.einsum('''bts,bcs->bct'''    ,					lowerCamelCase    ,					lowerCamelCase						)

						# (bs, length+1, width)
						__magic_name__ :							Optional[int]		       =							a.reshape(lowerCamelCase    ,					-1    ,					1						).transpose(1    ,					2						)

						return a[:, 0, :]  # cls_token



