



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__		:					str							=8






def      __lowercase       (			a__							,       a__=BITS       )    ->			Any:
							__SCREAMING_SNAKE_CASE									= x.device

							__SCREAMING_SNAKE_CASE									= (x * 2_55).int().clamp(0							,       2_55       )

							__SCREAMING_SNAKE_CASE									= 2 ** torch.arange(bits - 1							,       -1							,       -1							,       device=a__       )
							__SCREAMING_SNAKE_CASE									= rearrange(a__							,       'd -> d 1 1'       )
							__SCREAMING_SNAKE_CASE									= rearrange(a__							,       'b c h w -> b c 1 h w'       )

							__SCREAMING_SNAKE_CASE									= ((x & mask) != 0).float()
							__SCREAMING_SNAKE_CASE									= rearrange(a__							,       'b c d h w -> b (c d) h w'       )
							__SCREAMING_SNAKE_CASE									= bits * 2 - 1
							return bits






def      __lowercase       (			a__							,       a__=BITS       )    ->			List[str]:
							__SCREAMING_SNAKE_CASE									= x.device

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

							__SCREAMING_SNAKE_CASE									= rearrange(a__							,       'd -> d 1 1'       )
							__SCREAMING_SNAKE_CASE									= rearrange(a__							,       'b (c d) h w -> b c d h w'							,       d=8       )
							__SCREAMING_SNAKE_CASE									= reduce(x * mask							,       'b c d h w -> b c h w'							,       'sum'       )
							return (dec / 2_55).clamp(0.0							,       1.0       )






def      __lowercase       (			self							,       a__							,       a__							,       a__							,       a__ = 0.0							,       a__ = True							,       a__=None							,       a__ = 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									= timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 2. compute alphas, betas
							__SCREAMING_SNAKE_CASE									= self.alphas_cumprod[timestep]
							__SCREAMING_SNAKE_CASE									= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

							__SCREAMING_SNAKE_CASE									= 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									= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

							# 4. Clip "predicted x_0"
							__SCREAMING_SNAKE_CASE									= self.bit_scale
							if self.config.clip_sample:
														__SCREAMING_SNAKE_CASE									= torch.clamp(a__							,       -scale							,       a__       )

							# 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									= self._get_variance(a__							,       a__       )
							__SCREAMING_SNAKE_CASE									= 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									= (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									= (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									= 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									= model_output.device if torch.is_tensor(a__       ) else 'cpu'
														__SCREAMING_SNAKE_CASE									= torch.randn(model_output.shape							,       dtype=model_output.dtype							,       generator=a__       ).to(a__       )
														__SCREAMING_SNAKE_CASE									= self._get_variance(a__							,       a__       ) ** 0.5 * eta * noise

														__SCREAMING_SNAKE_CASE									= prev_sample + variance

							if not return_dict:
														return (prev_sample,)

							return DDIMSchedulerOutput(prev_sample=a__							,       pred_original_sample=a__       )






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

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

							# 1. compute alphas, betas
							__SCREAMING_SNAKE_CASE									= self.alphas_cumprod[t]
							__SCREAMING_SNAKE_CASE									= self.alphas_cumprod[t - 1] if t > 0 else self.one
							__SCREAMING_SNAKE_CASE									= 1 - alpha_prod_t
							__SCREAMING_SNAKE_CASE									= 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									= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif prediction_type == "sample":
														__SCREAMING_SNAKE_CASE									= model_output
							else:
														raise ValueError(f"""Unsupported prediction_type {prediction_type}."""       )

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

							# 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									= (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
							__SCREAMING_SNAKE_CASE									= 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									= pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							__SCREAMING_SNAKE_CASE									= 0
							if t > 0:
														__SCREAMING_SNAKE_CASE									= torch.randn(
														    model_output.size()							,       dtype=model_output.dtype							,       layout=model_output.layout							,       generator=a__       ).to(model_output.device       )
														__SCREAMING_SNAKE_CASE									= (self._get_variance(a__							,       predicted_variance=a__       ) ** 0.5) * noise

							__SCREAMING_SNAKE_CASE									= pred_prev_sample + variance

							if not return_dict:
														return (pred_prev_sample,)

							return DDPMSchedulerOutput(prev_sample=a__							,       pred_original_sample=a__       )

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A				,							_A = 1.0				,							):







													'''simple docstring'''

													super().__init__()
													__SCREAMING_SNAKE_CASE									= bit_scale
													__SCREAMING_SNAKE_CASE									= (
													    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				,							_A = 256				,							_A = 256				,							_A = 50				,							_A = None				,							_A = 1				,							_A = "pil"				,							_A = True				,							**_A				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= torch.randn(
													    (batch_size, self.unet.config.in_channels, height, width)				,							generator=_A				,							)
													__SCREAMING_SNAKE_CASE									= decimal_to_bits(_A		) * self.bit_scale
													__SCREAMING_SNAKE_CASE									= 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									= self.unet(_A				,							_A		).sample

																				# compute the previous noisy sample x_t -> x_t-1
																				__SCREAMING_SNAKE_CASE									= self.scheduler.step(_A				,							_A				,							_A		).prev_sample

													__SCREAMING_SNAKE_CASE									= bits_to_decimal(_A		)

													if output_type == "pil":
																				__SCREAMING_SNAKE_CASE									= self.numpy_to_pil(_A		)

													if not return_dict:
																				return (image,)

													return ImagePipelineOutput(images=_A		)











from typing import TYPE_CHECKING

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


lowerCAmelCase__		:					str							={'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[int]							=[
			    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''ViTMAEForPreTraining''',
			    '''ViTMAELayer''',
			    '''ViTMAEModel''',
			    '''ViTMAEPreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					List[str]							=[
			    '''TFViTMAEForPreTraining''',
			    '''TFViTMAEModel''',
			    '''TFViTMAEPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_vit_mae import (
						    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ViTMAEForPreTraining,
						    ViTMAELayer,
						    ViTMAEModel,
						    ViTMAEPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
			import sys

			lowerCAmelCase__		:					Optional[int]							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






