
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

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





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     4_2
					SCREAMING_SNAKE_CASE           =     None



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Tuple      ,					UpperCAmelCase__					:   List[str]=0.9_9_9      ,					UpperCAmelCase__					:   List[str]="cosine"      ,					)	->     Union[str, Any]:





							if alpha_transform_type == "cosine":

														def alpha_bar_fn(UpperCAmelCase__					:   Optional[Any]							):
																					return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2							) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(UpperCAmelCase__					:   Optional[Any]							):
																					return math.exp(t * -1_2.0							)

							else:
														raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''							)

							lowerCamelCase_					=       []
							for i in range(snake_case__							):
														lowerCamelCase_					=       i / num_diffusion_timesteps
														lowerCamelCase_					=       (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(snake_case__							) / alpha_bar_fn(snake_case__							)      ,					snake_case__							)							)
							return torch.tensor(snake_case__      ,					dtype=torch.floataa							)





class    __A(					UpperCAmelCase  ,		UpperCAmelCase							):

					@register_to_config
					def __init__(						self       :  Dict ,       __UpperCamelCase       :  int = 1_0_0_0 ,       __UpperCamelCase       :  str = "fixed_small_log" ,       __UpperCamelCase       :  List[str] = True ,       __UpperCamelCase       :  int = 1.0 ,       __UpperCamelCase       :  Optional[int] = "epsilon" ,       __UpperCamelCase       :  Tuple = "squaredcos_cap_v2" ,       ):
												if beta_schedule != "squaredcos_cap_v2":
																			raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'""" )

												lowerCamelCase_					=       betas_for_alpha_bar(a_ )

												lowerCamelCase_					=       1.0 - self.betas
												lowerCamelCase_					=       torch.cumprod(self.alphas ,       dim=0 )
												lowerCamelCase_					=       torch.tensor(1.0 )

												# standard deviation of the initial noise distribution
												lowerCamelCase_					=       1.0

												# setable values
												lowerCamelCase_					=       None
												lowerCamelCase_					=       torch.from_numpy(np.arange(0 ,       a_ )[::-1].copy() )

												lowerCamelCase_					=       variance_type

					def  lowercase__   (						self       :  List[str] ,       __UpperCamelCase       :  Union[str, Any] ,       __UpperCamelCase       :  Dict = None ):
												return sample

					def  lowercase__   (						self       :  List[str] ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  List[str] = None ):
												lowerCamelCase_					=       num_inference_steps
												lowerCamelCase_					=       (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
												lowerCamelCase_					=       (np.arange(0 ,       a_ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
												lowerCamelCase_					=       torch.from_numpy(a_ ).to(a_ )

					def  lowercase__   (						self       :  int ,       __UpperCamelCase       :  Dict ,       __UpperCamelCase       :  Union[str, Any]=None ,       __UpperCamelCase       :  List[str]=None ,       __UpperCamelCase       :  Dict=None ):
												if prev_timestep is None:
																			lowerCamelCase_					=       t - 1

												lowerCamelCase_					=       self.alphas_cumprod[t]
												lowerCamelCase_					=       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
												lowerCamelCase_					=       1 - alpha_prod_t
												lowerCamelCase_					=       1 - alpha_prod_t_prev

												if prev_timestep == t - 1:
																			lowerCamelCase_					=       self.betas[t]
												else:
																			lowerCamelCase_					=       1 - alpha_prod_t / alpha_prod_t_prev

												# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
												# and sample from it to get previous sample
												# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
												lowerCamelCase_					=       beta_prod_t_prev / beta_prod_t * beta

												if variance_type is None:
																			lowerCamelCase_					=       self.config.variance_type

												# hacks - were probably added for training stability
												if variance_type == "fixed_small_log":
																			lowerCamelCase_					=       torch.log(torch.clamp(a_ ,       min=1E-20 ) )
																			lowerCamelCase_					=       torch.exp(0.5 * variance )
												elif variance_type == "learned_range":
																			# NOTE difference with DDPM scheduler
																			lowerCamelCase_					=       variance.log()
																			lowerCamelCase_					=       beta.log()

																			lowerCamelCase_					=       (predicted_variance + 1) / 2
																			lowerCamelCase_					=       frac * max_log + (1 - frac) * min_log

												return variance

					def  lowercase__   (						self       :  str ,       __UpperCamelCase       :  Optional[int] ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  Any ,       __UpperCamelCase       :  Optional[int] = None ,       __UpperCamelCase       :  List[Any]=None ,       __UpperCamelCase       :  List[Any] = True ,       ):
												lowerCamelCase_					=       timestep

												if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
																			lowerCamelCase_	, lowerCamelCase_					=       torch.split(a_ ,       sample.shape[1] ,       dim=1 )
												else:
																			lowerCamelCase_					=       None

												# 1. compute alphas, betas
												if prev_timestep is None:
																			lowerCamelCase_					=       t - 1

												lowerCamelCase_					=       self.alphas_cumprod[t]
												lowerCamelCase_					=       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
												lowerCamelCase_					=       1 - alpha_prod_t
												lowerCamelCase_					=       1 - alpha_prod_t_prev

												if prev_timestep == t - 1:
																			lowerCamelCase_					=       self.betas[t]
																			lowerCamelCase_					=       self.alphas[t]
												else:
																			lowerCamelCase_					=       1 - alpha_prod_t / alpha_prod_t_prev
																			lowerCamelCase_					=       1 - beta

												# 2. compute predicted original sample from predicted noise also called
												# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
												if self.config.prediction_type == "epsilon":
																			lowerCamelCase_					=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
												elif self.config.prediction_type == "sample":
																			lowerCamelCase_					=       model_output
												else:
																			raise ValueError(
																			    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
																			    """ for the UnCLIPScheduler.""" )

												# 3. Clip "predicted x_0"
												if self.config.clip_sample:
																			lowerCamelCase_					=       torch.clamp(
																			    a_ ,       -self.config.clip_sample_range ,       self.config.clip_sample_range )

												# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
												# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
												lowerCamelCase_					=       (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
												lowerCamelCase_					=       alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

												# 5. Compute predicted previous sample µ_t
												# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
												lowerCamelCase_					=       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

												# 6. Add noise
												lowerCamelCase_					=       0
												if t > 0:
																			lowerCamelCase_					=       randn_tensor(
																			    model_output.shape ,       dtype=model_output.dtype ,       generator=a_ ,       device=model_output.device )

																			lowerCamelCase_					=       self._get_variance(
																			    a_ ,       predicted_variance=a_ ,       prev_timestep=a_ ,       )

																			if self.variance_type == "fixed_small_log":
																										lowerCamelCase_					=       variance
																			elif self.variance_type == "learned_range":
																										lowerCamelCase_					=       (0.5 * variance).exp()
																			else:
																										raise ValueError(
																										    F'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
																										    """ for the UnCLIPScheduler.""" )

																			lowerCamelCase_					=       variance * variance_noise

												lowerCamelCase_					=       pred_prev_sample + variance

												if not return_dict:
																			return (pred_prev_sample,)

												return UnCLIPSchedulerOutput(prev_sample=a_ ,       pred_original_sample=a_ )



					def  lowercase__   (						self       :  List[str] ,       __UpperCamelCase       :  Dict ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  Optional[Any] ,       ):
												lowerCamelCase_					=       self.alphas_cumprod.to(device=original_samples.device ,       dtype=original_samples.dtype )
												lowerCamelCase_					=       timesteps.to(original_samples.device )

												lowerCamelCase_					=       alphas_cumprod[timesteps] ** 0.5
												lowerCamelCase_					=       sqrt_alpha_prod.flatten()
												while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
																			lowerCamelCase_					=       sqrt_alpha_prod.unsqueeze(-1 )

												lowerCamelCase_					=       (1 - alphas_cumprod[timesteps]) ** 0.5
												lowerCamelCase_					=       sqrt_one_minus_alpha_prod.flatten()
												while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
																			lowerCamelCase_					=       sqrt_one_minus_alpha_prod.unsqueeze(-1 )

												lowerCamelCase_					=       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
												return noisy_samples










from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin


@dataclass
class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	42



class 	__snake_case   ( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE ):
		@register_to_config
		def __init__(							self    ,a_ = 16    ,a_ = 88    ,a_ = None    ,a_ = None    ,a_ = 1    ,a_ = 0.0    ,a_ = 32    ,a_ = None    ,a_ = False    ,a_ = None    ,a_ = "geglu"    ,a_ = True    ,a_ = True    ,):







						"""simple docstring"""
						super().__init__()
						lowerCAmelCase__		  =  num_attention_heads
						lowerCAmelCase__		  =  attention_head_dim
						lowerCAmelCase__		  =  num_attention_heads * attention_head_dim

						lowerCAmelCase__		  =  in_channels

						lowerCAmelCase__		  =  torch.nn.GroupNorm(num_groups=a_    ,num_channels=a_    ,eps=1e-6    ,affine=a_						)
						lowerCAmelCase__		  =  nn.Linear(a_    ,a_						)

						# 3. Define transformers blocks
						lowerCAmelCase__		  =  nn.ModuleList(
						    [
						        BasicTransformerBlock(
						            a_    ,a_    ,a_    ,dropout=a_    ,cross_attention_dim=a_    ,activation_fn=a_    ,attention_bias=a_    ,double_self_attention=a_    ,norm_elementwise_affine=a_    ,)
						        for d in range(a_						)
						    ]						)

						lowerCAmelCase__		  =  nn.Linear(a_    ,a_						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_=None    ,a_=None    ,a_=None    ,a_=1    ,a_=None    ,a_ = True    ,):







						"""simple docstring"""
						lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  hidden_states.shape
						lowerCAmelCase__		  =  batch_frames // num_frames

						lowerCAmelCase__		  =  hidden_states

						lowerCAmelCase__		  =  hidden_states[None, :].reshape(a_    ,a_    ,a_    ,a_    ,a_						)
						lowerCAmelCase__		  =  hidden_states.permute(0    ,2    ,1    ,3    ,4						)

						lowerCAmelCase__		  =  self.norm(a_						)
						lowerCAmelCase__		  =  hidden_states.permute(0    ,3    ,4    ,2    ,1						).reshape(batch_size * height * width    ,a_    ,a_						)

						lowerCAmelCase__		  =  self.proj_in(a_						)

						# 2. Blocks
						for block in self.transformer_blocks:
										lowerCAmelCase__		  =  block(
										    a_    ,encoder_hidden_states=a_    ,timestep=a_    ,cross_attention_kwargs=a_    ,class_labels=a_    ,)

						# 3. Output
						lowerCAmelCase__		  =  self.proj_out(a_						)
						lowerCAmelCase__		  =  (
						    hidden_states[None, None, :]
						    .reshape(a_    ,a_    ,a_    ,a_    ,a_						)
						    .permute(0    ,3    ,4    ,1    ,2						)
						    .contiguous()
						)
						lowerCAmelCase__		  =  hidden_states.reshape(a_    ,a_    ,a_    ,a_						)

						lowerCAmelCase__		  =  hidden_states + residual

						if not return_dict:
										return (output,)

						return TransformerTemporalModelOutput(sample=a_						)
