"""simple docstring"""





from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


lowerCAmelCase_     =	8



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=BITS  )->    Any:
							_SCREAMING_SNAKE_CASE		:						int			  =    x.device

							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    (x * 255).int().clamp(0     ,	255  )

							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    2 ** torch.arange(bits - 1     ,	-1     ,	-1     ,	device=__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    rearrange(__SCREAMING_SNAKE_CASE     ,	"""d -> d 1 1"""  )
							_SCREAMING_SNAKE_CASE		:						Dict			  =    rearrange(__SCREAMING_SNAKE_CASE     ,	"""b c h w -> b c 1 h w"""  )

							_SCREAMING_SNAKE_CASE		:						Tuple			  =    ((x & mask) != 0).float()
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    rearrange(__SCREAMING_SNAKE_CASE     ,	"""b c d h w -> b (c d) h w"""  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    bits * 2 - 1
							return bits



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=BITS  )->    Union[str, Any]:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    x.device

							_SCREAMING_SNAKE_CASE		:						Dict			  =    (x > 0).int()
							_SCREAMING_SNAKE_CASE		:						Dict			  =    2 ** torch.arange(bits - 1     ,	-1     ,	-1     ,	device=__SCREAMING_SNAKE_CASE     ,	dtype=torch.intaa  )

							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    rearrange(__SCREAMING_SNAKE_CASE     ,	"""d -> d 1 1"""  )
							_SCREAMING_SNAKE_CASE		:						Any			  =    rearrange(__SCREAMING_SNAKE_CASE     ,	"""b (c d) h w -> b c d h w"""     ,	d=8  )
							_SCREAMING_SNAKE_CASE		:						str			  =    reduce(x * mask     ,	"""b c d h w -> b c h w"""     ,	"""sum"""  )
							return (dec / 255).clamp(0.0     ,	1.0  )



def      lowerCamelCase_(self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = 0.0     ,	__SCREAMING_SNAKE_CASE = True     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE = True     ,	)->    Union[DDIMSchedulerOutput, Tuple]:
							if self.num_inference_steps is None:
														raise ValueError(
														    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""  )

							# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
							# Ideally, read DDIM paper in-detail understanding

							# Notation (<variable name> -> <name in paper>
							# - pred_noise_t -> e_theta(x_t, t)
							# - pred_original_sample -> f_theta(x_t, t) or x_0
							# - std_dev_t -> sigma_t
							# - eta -> η
							# - pred_sample_direction -> "direction pointing to x_t"
							# - pred_prev_sample -> "x_t-1"

							# 1. get previous step value (=t-1)
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 2. compute alphas, betas
							_SCREAMING_SNAKE_CASE		:						int			  =    self.alphas_cumprod[timestep]
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    1 - alpha_prod_t

							# 3. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

							# 4. Clip "predicted x_0"
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.bit_scale
							if self.config.clip_sample:
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    torch.clamp(__SCREAMING_SNAKE_CASE     ,	-scale     ,	__SCREAMING_SNAKE_CASE  )

							# 5. compute variance: "sigma_t(η)" -> see formula (16)
							# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self._get_variance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						str			  =    eta * variance ** 0.5

							if use_clipped_model_output:
														# the model_output is always re-derived from the clipped x_0 in Glide
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

							# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

							# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							_SCREAMING_SNAKE_CASE		:						Any			  =    alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

							if eta > 0:
														# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
														_SCREAMING_SNAKE_CASE		:						int			  =    model_output.device if torch.is_tensor(__SCREAMING_SNAKE_CASE  ) else """cpu"""
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    torch.randn(model_output.shape     ,	dtype=model_output.dtype     ,	generator=__SCREAMING_SNAKE_CASE  ).to(__SCREAMING_SNAKE_CASE  )
														_SCREAMING_SNAKE_CASE		:						int			  =    self._get_variance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ) ** 0.5 * eta * noise

														_SCREAMING_SNAKE_CASE		:						int			  =    prev_sample + variance

							if not return_dict:
														return (prev_sample,)

							return DDIMSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE     ,	pred_original_sample=__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_(self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE="epsilon"     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE = True     ,	)->    Union[DDPMSchedulerOutput, Tuple]:
							_SCREAMING_SNAKE_CASE		:						str			  =    timestep

							if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.split(__SCREAMING_SNAKE_CASE     ,	sample.shape[1]     ,	dim=1  )
							else:
														_SCREAMING_SNAKE_CASE		:						Any			  =    None

							# 1. compute alphas, betas
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    self.alphas_cumprod[t]
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    self.alphas_cumprod[t - 1] if t > 0 else self.one
							_SCREAMING_SNAKE_CASE		:						Dict			  =    1 - alpha_prod_t
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    1 - alpha_prod_t_prev

							# 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 prediction_type == "epsilon":
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif prediction_type == "sample":
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model_output
							else:
														raise ValueError(F"""Unsupported prediction_type {prediction_type}."""  )

							# 3. Clip "predicted x_0"
							_SCREAMING_SNAKE_CASE		:						int			  =    self.bit_scale
							if self.config.clip_sample:
														_SCREAMING_SNAKE_CASE		:						Any			  =    torch.clamp(__SCREAMING_SNAKE_CASE     ,	-scale     ,	__SCREAMING_SNAKE_CASE  )

							# 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		:						Tuple			  =    (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.alphas[t] ** 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		:						int			  =    pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    0
							if t > 0:
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.randn(
														    model_output.size()     ,	dtype=model_output.dtype     ,	layout=model_output.layout     ,	generator=__SCREAMING_SNAKE_CASE  ).to(model_output.device  )
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    (self._get_variance(__SCREAMING_SNAKE_CASE     ,	predicted_variance=__SCREAMING_SNAKE_CASE  ) ** 0.5) * noise

							_SCREAMING_SNAKE_CASE		:						Any			  =    pred_prev_sample + variance

							if not return_dict:
														return (pred_prev_sample,)

							return DDPMSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE     ,	pred_original_sample=__SCREAMING_SNAKE_CASE  )




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				def __init__(      self							:							Dict      ,    _A							:							UNetaDConditionModel      ,    _A							:							Union[DDIMScheduler, DDPMScheduler]      ,    _A							:							Optional[float] = 1.0      ,    ):



											"""simple docstring"""







											super().__init__()
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    bit_scale
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    (
											    ddim_bit_scheduler_step if isinstance(_A      ,    _A) else ddpm_bit_scheduler_step
											)

											self.register_modules(unet=_A      ,    scheduler=_A)





				@torch.no_grad()
				def __call__(      self							:							Optional[int]      ,    _A							:							Optional[int] = 2_5_6      ,    _A							:							Optional[int] = 2_5_6      ,    _A							:							Optional[int] = 5_0      ,    _A							:							Optional[torch.Generator] = None      ,    _A							:							Optional[int] = 1      ,    _A							:							Optional[str] = "pil"      ,    _A							:							bool = True      ,    **_A							:							List[Any]      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    torch.randn(
											    (batch_size, self.unet.config.in_channels, height, width)      ,    generator=_A      ,    )
											_SCREAMING_SNAKE_CASE		:						Dict			  =    decimal_to_bits(_A) * self.bit_scale
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    latents.to(self.device)

											self.scheduler.set_timesteps(_A)

											for t in self.progress_bar(self.scheduler.timesteps):
																		# predict the noise residual
																		_SCREAMING_SNAKE_CASE		:						Tuple			  =    self.unet(_A      ,    _A).sample

																		# compute the previous noisy sample x_t -> x_t-1
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.scheduler.step(_A      ,    _A      ,    _A).prev_sample

											_SCREAMING_SNAKE_CASE		:						int			  =    bits_to_decimal(_A)

											if output_type == "pil":
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.numpy_to_pil(_A)

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=_A)



"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase_     =	{'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_     =	[
			    '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''UniSpeechForCTC''',
			    '''UniSpeechForPreTraining''',
			    '''UniSpeechForSequenceClassification''',
			    '''UniSpeechModel''',
			    '''UniSpeechPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_unispeech import (
						    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
						    UniSpeechForCTC,
						    UniSpeechForPreTraining,
						    UniSpeechForSequenceClassification,
						    UniSpeechModel,
						    UniSpeechPreTrainedModel,
						)

else:
			import sys

			lowerCAmelCase_     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


