

import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   (CMStochasticIterativeScheduler,)
					__magic_name__        =   1_0





					def    _UpperCAmelCase    (				self				,      **lowerCamelCase__						):
						UpperCAmelCase__:	Tuple									= {
						    "num_train_timesteps": 2_0_1,
						    "sigma_min": 0.002,
						    "sigma_max": 80.0,
						}

						config.update(**lowerCamelCase__						)
						return config





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Dict									= 1_0

						UpperCAmelCase__:	Optional[int]									= self.get_scheduler_config()
						UpperCAmelCase__:	Optional[Any]									= self.scheduler_classes[0](**lowerCamelCase__						)

						scheduler.set_timesteps(lowerCamelCase__						)

						UpperCAmelCase__:	Tuple									= scheduler.timesteps[0]
						UpperCAmelCase__:	int									= scheduler.timesteps[1]

						UpperCAmelCase__:	Tuple									= self.dummy_sample
						UpperCAmelCase__:	List[Any]									= 0.1 * sample

						UpperCAmelCase__:	Tuple									= scheduler.step(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						).prev_sample
						UpperCAmelCase__:	Any									= scheduler.step(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						).prev_sample

						self.assertEqual(output_a.shape				,      sample.shape						)
						self.assertEqual(output_a.shape				,      output_a.shape						)





					def    _UpperCAmelCase    (				self						):
						for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						for clip_denoised in [True, False]:
							self.check_over_configs(clip_denoised=lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[Any]									= self.scheduler_classes[0]
						UpperCAmelCase__:	str									= self.get_scheduler_config()
						UpperCAmelCase__:	Dict									= scheduler_class(**lowerCamelCase__						)

						UpperCAmelCase__:	Optional[int]									= 1
						scheduler.set_timesteps(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[Any]									= scheduler.timesteps

						UpperCAmelCase__:	List[Any]									= torch.manual_seed(0						)

						UpperCAmelCase__:	Optional[Any]									= self.dummy_model()
						UpperCAmelCase__:	Any									= self.dummy_sample_deter * scheduler.init_noise_sigma

						for i, t in enumerate(lowerCamelCase__						):
							# 1. scale model input
							UpperCAmelCase__:	Dict									= scheduler.scale_model_input(lowerCamelCase__				,      lowerCamelCase__						)

							# 2. predict noise residual
							UpperCAmelCase__:	Union[str, Any]									= model(lowerCamelCase__				,      lowerCamelCase__						)

							# 3. predict previous sample x_t-1
							UpperCAmelCase__:	Optional[int]									= scheduler.step(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      generator=lowerCamelCase__						).prev_sample

							UpperCAmelCase__:	str									= pred_prev_sample

						UpperCAmelCase__:	Optional[int]									= torch.sum(torch.abs(lowerCamelCase__						)						)
						UpperCAmelCase__:	Optional[Any]									= torch.mean(torch.abs(lowerCamelCase__						)						)

						assert abs(result_sum.item() - 192.7_614						) < 1e-2
						assert abs(result_mean.item() - 0.2_510						) < 1e-3





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Union[str, Any]									= self.scheduler_classes[0]
						UpperCAmelCase__:	List[str]									= self.get_scheduler_config()
						UpperCAmelCase__:	List[Any]									= scheduler_class(**lowerCamelCase__						)

						UpperCAmelCase__:	Any									= [1_0_6, 0]
						scheduler.set_timesteps(timesteps=lowerCamelCase__						)
						UpperCAmelCase__:	str									= scheduler.timesteps

						UpperCAmelCase__:	Dict									= torch.manual_seed(0						)

						UpperCAmelCase__:	Dict									= self.dummy_model()
						UpperCAmelCase__:	Optional[int]									= self.dummy_sample_deter * scheduler.init_noise_sigma

						for t in timesteps:
							# 1. scale model input
							UpperCAmelCase__:	Any									= scheduler.scale_model_input(lowerCamelCase__				,      lowerCamelCase__						)

							# 2. predict noise residual
							UpperCAmelCase__:	int									= model(lowerCamelCase__				,      lowerCamelCase__						)

							# 3. predict previous sample x_t-1
							UpperCAmelCase__:	Optional[Any]									= scheduler.step(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      generator=lowerCamelCase__						).prev_sample

							UpperCAmelCase__:	List[str]									= pred_prev_sample

						UpperCAmelCase__:	Union[str, Any]									= torch.sum(torch.abs(lowerCamelCase__						)						)
						UpperCAmelCase__:	str									= torch.mean(torch.abs(lowerCamelCase__						)						)

						assert abs(result_sum.item() - 347.6_357						) < 1e-2
						assert abs(result_mean.item() - 0.4_527						) < 1e-3





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[Any]									= self.scheduler_classes[0]
						UpperCAmelCase__:	int									= self.get_scheduler_config()
						UpperCAmelCase__:	List[Any]									= scheduler_class(**lowerCamelCase__						)

						UpperCAmelCase__:	Optional[int]									= [3_9, 3_0, 1_2, 1_5, 0]

						with self.assertRaises(lowerCamelCase__				,      msg="`timesteps` must be in descending order."						):
							scheduler.set_timesteps(timesteps=lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Tuple									= self.scheduler_classes[0]
						UpperCAmelCase__:	str									= self.get_scheduler_config()
						UpperCAmelCase__:	Union[str, Any]									= scheduler_class(**lowerCamelCase__						)

						UpperCAmelCase__:	List[str]									= [3_9, 3_0, 1_2, 1, 0]
						UpperCAmelCase__:	Tuple									= len(lowerCamelCase__						)

						with self.assertRaises(lowerCamelCase__				,      msg="Can only pass one of `num_inference_steps` or `timesteps`."						):
							scheduler.set_timesteps(num_inference_steps=lowerCamelCase__				,      timesteps=lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[str]									= self.scheduler_classes[0]
						UpperCAmelCase__:	int									= self.get_scheduler_config()
						UpperCAmelCase__:	Dict									= scheduler_class(**lowerCamelCase__						)

						UpperCAmelCase__:	Optional[Any]									= [scheduler.config.num_train_timesteps]

						with self.assertRaises(
						    lowerCamelCase__				,      msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"				,      ):
							scheduler.set_timesteps(timesteps=lowerCamelCase__						)


import torch
from torch import nn

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

class __UpperCamelCase     (				_a					,_a		):






					'''simple docstring'''



					@register_to_config
					def __init__(				self				,      *,
					    lowerCamelCase__ = 4				,      lowerCamelCase__ = 7_6_8				,      lowerCamelCase__				,      lowerCamelCase__				,      ):
						super().__init__()

						UpperCAmelCase__:	int									= nn.Parameter(torch.zeros(lowerCamelCase__						)						)

						# parameters for additional clip time embeddings
						UpperCAmelCase__:	Optional[Any]									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)
						UpperCAmelCase__:	str									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)

						# parameters for encoder hidden states
						UpperCAmelCase__:	Tuple									= clip_extra_context_tokens
						UpperCAmelCase__:	List[str]									= nn.Linear(
						    lowerCamelCase__				,      self.clip_extra_context_tokens * cross_attention_dim						)
						UpperCAmelCase__:	Union[str, Any]									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)
						UpperCAmelCase__:	str									= nn.LayerNorm(lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      *, lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						if do_classifier_free_guidance:
							# Add the classifier free guidance embeddings to the image embeddings
							UpperCAmelCase__:	Any									= image_embeddings.shape[0]
							UpperCAmelCase__:	Optional[Any]									= self.learned_classifier_free_guidance_embeddings.unsqueeze(0						)
							UpperCAmelCase__:	Union[str, Any]									= classifier_free_guidance_embeddings.expand(
							    lowerCamelCase__				,      -1						)
							UpperCAmelCase__:	Tuple									= 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]

						UpperCAmelCase__:	str									= 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, ...
						UpperCAmelCase__:	List[str]									= self.embedding_proj(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[int]									= self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase__						)
						UpperCAmelCase__:	Any									= 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"
						UpperCAmelCase__:	Dict									= self.clip_extra_context_tokens_proj(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[Any]									= clip_extra_context_tokens.reshape(lowerCamelCase__				,      -1				,      self.clip_extra_context_tokens						)
						UpperCAmelCase__:	Dict									= clip_extra_context_tokens.permute(0				,      2				,      1						)

						UpperCAmelCase__:	Any									= self.encoder_hidden_states_proj(lowerCamelCase__						)
						UpperCAmelCase__:	List[str]									= self.text_encoder_hidden_states_norm(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[int]									= torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]				,      dim=1						)

						return text_encoder_hidden_states, additive_clip_time_embeddings