'''simple docstring'''




import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin




class    a__				(       UpperCAmelCase__							,       UpperCAmelCase__			):
     lowerCamelCase  :					Any							=1






     @register_to_config
     def __init__(			self		:		Dict  ,    a		:		List[str]=20_00  ,    a		:		Optional[Any]=0.1  ,    a		:		str=20  ,    a		:		Dict=1e-3   ):





         """simple docstring"""



         __lowerCamelCase          =  None
         __lowerCamelCase          =  None
         __lowerCamelCase          =  None






     def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    a		:		Dict  ,    a		:		Union[str, torch.device] = None   ):





         """simple docstring"""



         __lowerCamelCase          =  torch.linspace(1  ,    self.config.sampling_eps  ,    a  ,    device=a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict  ,    a		:		str  ,    a		:		List[str]  ,    a		:		str  ,    a		:		Any=None   ):





         """simple docstring"""



         if self.timesteps is None:
             raise ValueError(
                 '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''   )

         # TODO(Patrick) better comments + non-PyTorch
         # postprocess model score
         __lowerCamelCase          =  (
             -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
         )
         __lowerCamelCase          =  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff   )   )
         __lowerCamelCase          =  std.flatten()
         while len(std.shape   ) < len(score.shape   ):
             __lowerCamelCase          =  std.unsqueeze(-1   )
         __lowerCamelCase          =  -score / std

         # compute
         __lowerCamelCase          =  -1.0 / len(self.timesteps   )

         __lowerCamelCase          =  self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
         __lowerCamelCase          =  beta_t.flatten()
         while len(beta_t.shape   ) < len(x.shape   ):
             __lowerCamelCase          =  beta_t.unsqueeze(-1   )
         __lowerCamelCase          =  -0.5 * beta_t * x

         __lowerCamelCase          =  torch.sqrt(a   )
         __lowerCamelCase          =  drift - diffusion**2 * score
         __lowerCamelCase          =  x + drift * dt

         # add noise
         __lowerCamelCase          =  randn_tensor(x.shape  ,    layout=x.layout  ,    generator=a  ,    device=x.device  ,    dtype=x.dtype   )
         __lowerCamelCase          =  x_mean + diffusion * math.sqrt(-dt   ) * noise

         return x, x_mean






     def __len__(			self		:		int   ):





         """simple docstring"""



         return self.config.num_train_timesteps







'''simple docstring'''




import torch
from torch import nn

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




class    a__				(       UpperCAmelCase__							,       UpperCAmelCase__			):






     @register_to_config
     def __init__(			self		:		Any  ,    *,
         a		:		int = 4  ,    a		:		int = 7_68  ,    a		:		int  ,    a		:		Optional[int]  ,    ):





         """simple docstring"""



         super().__init__()

         __lowerCamelCase          =  nn.Parameter(torch.zeros(a   )   )

         # parameters for additional clip time embeddings
         __lowerCamelCase          =  nn.Linear(a  ,    a   )
         __lowerCamelCase          =  nn.Linear(a  ,    a   )

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    *, a		:		Union[str, Any]  ,    a		:		Any  ,    a		:		str  ,    a		:		Any   ):





         """simple docstring"""



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

         __lowerCamelCase          =  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, ...
         __lowerCamelCase          =  self.embedding_proj(a   )
         __lowerCamelCase          =  self.clip_image_embeddings_project_to_time_embeddings(a   )
         __lowerCamelCase          =  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"
         __lowerCamelCase          =  self.clip_extra_context_tokens_proj(a   )
         __lowerCamelCase          =  clip_extra_context_tokens.reshape(a  ,    -1  ,    self.clip_extra_context_tokens   )
         __lowerCamelCase          =  clip_extra_context_tokens.permute(0  ,    2  ,    1   )

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

         return text_encoder_hidden_states, additive_clip_time_embeddings
