

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 _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           =42
			lowerCamelCase__           =None

def  lowerCamelCase__ (					_a							,			_a=0.999							,			_a="cosine"							,			):
	if alpha_transform_type == "cosine":

		def alpha_bar_fn(_a):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(_a):
			return math.exp(t * -12.0)

	else:
		raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}")

	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       []
	for i in range(_a):
		SCREAMING_SNAKE_CASE   :      str				     =       i / num_diffusion_timesteps
		SCREAMING_SNAKE_CASE   :      Tuple				     =       (i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(_a) / alpha_bar_fn(_a)							,			_a))
	return torch.tensor(_a							,			dtype=torch.floataa)






class _UpperCamelCase							(							__A							,	__A   ):
			'''simple docstring'''







			@register_to_config
			def __init__(			self							: List[Any]      ,      a							: int = 1000      ,      a							: str = "fixed_small_log"      ,      a							: bool = True      ,      a							: Optional[float] = 1.0      ,      a							: str = "epsilon"      ,      a							: str = "squaredcos_cap_v2"      ,      )							->       List[str]:

				"""simple docstring"""

				if beta_schedule != "squaredcos_cap_v2":
					raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       betas_for_alpha_bar(a	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       1.0 - self.betas
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.cumprod(self.alphas      ,      dim=0	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       torch.tensor(1.0	)

				# standard deviation of the initial noise distribution
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       1.0

				# setable values
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       None
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       torch.from_numpy(np.arange(0      ,      a	)[::-1].copy()	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       variance_type
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: torch.FloatTensor      ,      a							: Optional[int] = None	)							->       torch.FloatTensor:

				"""simple docstring"""

				return sample
			def 							__UpperCamelCase (			self							: Any      ,      a							: int      ,      a							: Union[str, torch.device] = None	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       num_inference_steps
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       (np.arange(0      ,      a	) * step_ratio).round()[::-1].copy().astype(np.intaa	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       torch.from_numpy(a	).to(a	)
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: Any      ,      a							: int=None      ,      a							: List[str]=None      ,      a							: Any=None	)							->       Dict:

				"""simple docstring"""

				if prev_timestep is None:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       t - 1

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.alphas_cumprod[t]
				SCREAMING_SNAKE_CASE   :      Dict				     =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
				SCREAMING_SNAKE_CASE   :      List[str]				     =       1 - alpha_prod_t
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       1 - alpha_prod_t_prev

				if prev_timestep == t - 1:
					SCREAMING_SNAKE_CASE   :      int				     =       self.betas[t]
				else:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       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
				SCREAMING_SNAKE_CASE   :      str				     =       beta_prod_t_prev / beta_prod_t * beta

				if variance_type is None:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.config.variance_type

				# hacks - were probably added for training stability
				if variance_type == "fixed_small_log":
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       torch.log(torch.clamp(a      ,      min=1e-20	)	)
					SCREAMING_SNAKE_CASE   :      str				     =       torch.exp(0.5 * variance	)
				elif variance_type == "learned_range":
					# NOTE difference with DDPM scheduler
					SCREAMING_SNAKE_CASE   :      Any				     =       variance.log()
					SCREAMING_SNAKE_CASE   :      str				     =       beta.log()

					SCREAMING_SNAKE_CASE   :      str				     =       (predicted_variance + 1) / 2
					SCREAMING_SNAKE_CASE   :      List[str]				     =       frac * max_log + (1 - frac) * min_log

				return variance
			def 							__UpperCamelCase (			self							: Any      ,      a							: torch.FloatTensor      ,      a							: int      ,      a							: torch.FloatTensor      ,      a							: Optional[int] = None      ,      a							: Optional[int]=None      ,      a							: bool = True      ,      )							->       Union[UnCLIPSchedulerOutput, Tuple]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       timestep

				if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
					SCREAMING_SNAKE_CASE   :      Tuple				     =       torch.split(a      ,      sample.shape[1]      ,      dim=1	)
				else:
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       None

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

				SCREAMING_SNAKE_CASE   :      int				     =       self.alphas_cumprod[t]
				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
				SCREAMING_SNAKE_CASE   :      Dict				     =       1 - alpha_prod_t
				SCREAMING_SNAKE_CASE   :      List[str]				     =       1 - alpha_prod_t_prev

				if prev_timestep == t - 1:
					SCREAMING_SNAKE_CASE   :      List[str]				     =       self.betas[t]
					SCREAMING_SNAKE_CASE   :      Dict				     =       self.alphas[t]
				else:
					SCREAMING_SNAKE_CASE   :      Any				     =       1 - alpha_prod_t / alpha_prod_t_prev
					SCREAMING_SNAKE_CASE   :      int				     =       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":
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif self.config.prediction_type == "sample":
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       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:
					SCREAMING_SNAKE_CASE   :      Any				     =       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
				SCREAMING_SNAKE_CASE   :      Any				     =       (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
				SCREAMING_SNAKE_CASE   :      Tuple				     =       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
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

				# 6. Add noise
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       0
				if t > 0:
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       randn_tensor(
					    model_output.shape      ,      dtype=model_output.dtype      ,      generator=a      ,      device=model_output.device	)

					SCREAMING_SNAKE_CASE   :      List[Any]				     =       self._get_variance(
					    a      ,      predicted_variance=a      ,      prev_timestep=a      ,      )

					if self.variance_type == "fixed_small_log":
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       variance
					elif self.variance_type == "learned_range":
						SCREAMING_SNAKE_CASE   :      str				     =       (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."	)

					SCREAMING_SNAKE_CASE   :      Any				     =       variance * variance_noise

				SCREAMING_SNAKE_CASE   :      Any				     =       pred_prev_sample + variance

				if not return_dict:
					return (pred_prev_sample,)

				return UnCLIPSchedulerOutput(prev_sample=a      ,      pred_original_sample=a	)






			def 							__UpperCamelCase (			self							: Dict      ,      a							: torch.FloatTensor      ,      a							: torch.FloatTensor      ,      a							: torch.IntTensor      ,      )							->       torch.FloatTensor:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self.alphas_cumprod.to(device=original_samples.device      ,      dtype=original_samples.dtype	)
				SCREAMING_SNAKE_CASE   :      str				     =       timesteps.to(original_samples.device	)

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

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       (1 - alphas_cumprod[timesteps]) ** 0.5
				SCREAMING_SNAKE_CASE   :      List[str]				     =       sqrt_one_minus_alpha_prod.flatten()
				while len(sqrt_one_minus_alpha_prod.shape	) < len(original_samples.shape	):
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       sqrt_one_minus_alpha_prod.unsqueeze(-1	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       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 _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           =42






class _UpperCamelCase							(							__A							,	__A   ):
			'''simple docstring'''







			@register_to_config
			def __init__(			self							: str      ,      a							: int = 16      ,      a							: int = 88      ,      a							: Optional[int] = None      ,      a							: Optional[int] = None      ,      a							: int = 1      ,      a							: float = 0.0      ,      a							: int = 32      ,      a							: Optional[int] = None      ,      a							: bool = False      ,      a							: Optional[int] = None      ,      a							: str = "geglu"      ,      a							: bool = True      ,      a							: bool = True      ,      )							->       str:

				"""simple docstring"""

				super().__init__()
				SCREAMING_SNAKE_CASE   :      str				     =       num_attention_heads
				SCREAMING_SNAKE_CASE   :      Tuple				     =       attention_head_dim
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       num_attention_heads * attention_head_dim

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       in_channels

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       torch.nn.GroupNorm(num_groups=a      ,      num_channels=a      ,      eps=1e-6      ,      affine=a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       nn.Linear(a      ,      a	)

				# 3. Define transformers blocks
				SCREAMING_SNAKE_CASE   :      List[str]				     =       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	)
				    ]	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       nn.Linear(a      ,      a	)






			def 							__UpperCamelCase (			self							: Any      ,      a							: int      ,      a							: Optional[int]=None      ,      a							: List[str]=None      ,      a							: List[str]=None      ,      a							: Union[str, Any]=1      ,      a							: Any=None      ,      a							: bool = True      ,      )							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Any				     =       hidden_states.shape
				SCREAMING_SNAKE_CASE   :      Any				     =       batch_frames // num_frames

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       hidden_states

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       hidden_states[None, :].reshape(a      ,      a      ,      a      ,      a      ,      a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       hidden_states.permute(0      ,      2      ,      1      ,      3      ,      4	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.norm(a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       hidden_states.permute(0      ,      3      ,      4      ,      2      ,      1	).reshape(batch_size * height * width      ,      a      ,      a	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.proj_in(a	)

				# 2. Blocks
				for block in self.transformer_blocks:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       block(
					    a      ,      encoder_hidden_states=a      ,      timestep=a      ,      cross_attention_kwargs=a      ,      class_labels=a      ,      )

				# 3. Output
				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.proj_out(a	)
				SCREAMING_SNAKE_CASE   :      int				     =       (
				    hidden_states[None, None, :]
				    .reshape(a      ,      a      ,      a      ,      a      ,      a	)
				    .permute(0      ,      3      ,      4      ,      1      ,      2	)
				    .contiguous()
				)
				SCREAMING_SNAKE_CASE   :      Dict				     =       hidden_states.reshape(a      ,      a      ,      a      ,      a	)

				SCREAMING_SNAKE_CASE   :      int				     =       hidden_states + residual

				if not return_dict:
					return (output,)

				return TransformerTemporalModelOutput(sample=a	)