
# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput





class     lowerCamelCase      (       __lowerCamelCase					):







	def __init__(     self      :List[Any]   ,     lowercase      :Tuple   ,     lowercase      :Tuple			)		->      List[str]:







				"""simple docstring"""






				super().__init__()
				self.register_modules(unet=lowercase   ,     scheduler=lowercase			)







	@torch.no_grad()
	def __call__(     self      :Any   ,     lowercase      :int = 1   ,     lowercase      :Optional[torch.Generator] = None   ,     lowercase      :int = 5_0   ,     lowercase      :Optional[str] = "pil"   ,     lowercase      :bool = True   ,     **lowercase      :int   ,     )		->      Union[ImagePipelineOutput, Tuple]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= torch.randn(
				    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,     generator=lowercase   ,     )
				SCREAMING_SNAKE_CASE     						= image.to(self.device			)

				# set step values
				self.scheduler.set_timesteps(lowercase			)

				for t in self.progress_bar(self.scheduler.timesteps			):
							# 1. predict noise model_output
							SCREAMING_SNAKE_CASE     						= self.unet(lowercase   ,     lowercase			).sample

							# 2. predict previous mean of image x_t-1 and add variance depending on eta
							# eta corresponds to η in paper and should be between [0, 1]
							# do x_t -> x_t-1
							SCREAMING_SNAKE_CASE     						= self.scheduler.step(lowercase   ,     lowercase   ,     lowercase			).prev_sample

				SCREAMING_SNAKE_CASE     						= (image / 2 + 0.5).clamp(0   ,     1			)
				SCREAMING_SNAKE_CASE     						= image.cpu().permute(0   ,     2   ,     3   ,     1			).numpy()
				if output_type == "pil":
							SCREAMING_SNAKE_CASE     						= self.numpy_to_pil(lowercase			)

				if not return_dict:
							return (image,), "This is a local test"

				return ImagePipelineOutput(images=lowercase			), "This is a local test"

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class     lowerCamelCase      (       __lowerCamelCase ,		__lowerCamelCase					):







	@register_to_config
	def __init__(     self      :Tuple   ,     *,
	    lowercase      :int = 4   ,     lowercase      :int = 7_6_8   ,     lowercase      :int   ,     lowercase      :Any   ,     )		->      Optional[Any]:







				"""simple docstring"""






				super().__init__()

				SCREAMING_SNAKE_CASE     						= nn.Parameter(torch.zeros(lowercase			)			)

				# parameters for additional clip time embeddings
				SCREAMING_SNAKE_CASE     						= nn.Linear(lowercase   ,     lowercase			)
				SCREAMING_SNAKE_CASE     						= nn.Linear(lowercase   ,     lowercase			)

				# parameters for encoder hidden states
				SCREAMING_SNAKE_CASE     						= clip_extra_context_tokens
				SCREAMING_SNAKE_CASE     						= nn.Linear(
				    lowercase   ,     self.clip_extra_context_tokens * cross_attention_dim			)
				SCREAMING_SNAKE_CASE     						= nn.Linear(lowercase   ,     lowercase			)
				SCREAMING_SNAKE_CASE     						= nn.LayerNorm(lowercase			)







	def  snake_case__     (     self      :Union[str, Any]   ,     *, lowercase      :Optional[int]   ,     lowercase      :Union[str, Any]   ,     lowercase      :Union[str, Any]   ,     lowercase      :Optional[Any]			)		->      Union[str, Any]:







				"""simple docstring"""






				if do_classifier_free_guidance:
							# Add the classifier free guidance embeddings to the image embeddings
							SCREAMING_SNAKE_CASE     						= image_embeddings.shape[0]
							SCREAMING_SNAKE_CASE     						= self.learned_classifier_free_guidance_embeddings.unsqueeze(0			)
							SCREAMING_SNAKE_CASE     						= classifier_free_guidance_embeddings.expand(
							    lowercase   ,     -1			)
							SCREAMING_SNAKE_CASE     						= torch.cat([classifier_free_guidance_embeddings, image_embeddings]   ,     dim=0			)

				# The image embeddings batch size and the text embeddings batch size are equal
				assert image_embeddings.shape[0] == prompt_embeds.shape[0]

				SCREAMING_SNAKE_CASE     						= prompt_embeds.shape[0]

				# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
				# adding CLIP embeddings to the existing timestep embedding, ...
				SCREAMING_SNAKE_CASE     						= self.embedding_proj(lowercase			)
				SCREAMING_SNAKE_CASE     						= self.clip_image_embeddings_project_to_time_embeddings(lowercase			)
				SCREAMING_SNAKE_CASE     						= time_projected_image_embeddings + time_projected_prompt_embeds

				# ... and by projecting CLIP embeddings into four
				# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
				SCREAMING_SNAKE_CASE     						= self.clip_extra_context_tokens_proj(lowercase			)
				SCREAMING_SNAKE_CASE     						= clip_extra_context_tokens.reshape(lowercase   ,     -1   ,     self.clip_extra_context_tokens			)
				SCREAMING_SNAKE_CASE     						= clip_extra_context_tokens.permute(0   ,     2   ,     1			)

				SCREAMING_SNAKE_CASE     						= self.encoder_hidden_states_proj(lowercase			)
				SCREAMING_SNAKE_CASE     						= self.text_encoder_hidden_states_norm(lowercase			)
				SCREAMING_SNAKE_CASE     						= torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]   ,     dim=1			)

				return text_encoder_hidden_states, additive_clip_time_embeddings