


"""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


__UpperCamelCase		= 8


def        lowercase     (SCREAMING_SNAKE_CASE_						:							Optional[Any]					,     SCREAMING_SNAKE_CASE_						:							List[str]=BITS    )  ->		List[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=_SCREAMING_SNAKE_CASE    )
						SCREAMING_SNAKE_CASE						=   rearrange(_SCREAMING_SNAKE_CASE					,     'd -> d 1 1'    )
						SCREAMING_SNAKE_CASE						=   rearrange(_SCREAMING_SNAKE_CASE					,     'b c h w -> b c 1 h w'    )

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


def        lowercase     (SCREAMING_SNAKE_CASE_						:							Tuple					,     SCREAMING_SNAKE_CASE_						:							Dict=BITS    )  ->		Tuple:
						SCREAMING_SNAKE_CASE						=   x.device

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

						SCREAMING_SNAKE_CASE						=   rearrange(_SCREAMING_SNAKE_CASE					,     'd -> d 1 1'    )
						SCREAMING_SNAKE_CASE						=   rearrange(_SCREAMING_SNAKE_CASE					,     '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						:							Optional[Any]					,     SCREAMING_SNAKE_CASE_						:							torch.FloatTensor					,     SCREAMING_SNAKE_CASE_						:							int					,     SCREAMING_SNAKE_CASE_						:							torch.FloatTensor					,     SCREAMING_SNAKE_CASE_						:							float = 0.0					,     SCREAMING_SNAKE_CASE_						:							bool = True					,     SCREAMING_SNAKE_CASE_						:							List[Any]=None					,     SCREAMING_SNAKE_CASE_						:							bool = True					,     )  ->		List[Any]:
						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(_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						=   self._get_variance(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE    )
						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(_SCREAMING_SNAKE_CASE    ) else """cpu"""
												SCREAMING_SNAKE_CASE						=   torch.randn(model_output.shape					,     dtype=model_output.dtype					,     generator=_SCREAMING_SNAKE_CASE    ).to(_SCREAMING_SNAKE_CASE    )
												SCREAMING_SNAKE_CASE						=   self._get_variance(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE    ) ** 0.5 * eta * noise

												SCREAMING_SNAKE_CASE						=   prev_sample + variance

						if not return_dict:
												return (prev_sample,)

						return DDIMSchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE					,     pred_original_sample=_SCREAMING_SNAKE_CASE    )


def        lowercase     (self						:							Optional[Any]					,     SCREAMING_SNAKE_CASE_						:							torch.FloatTensor					,     SCREAMING_SNAKE_CASE_						:							int					,     SCREAMING_SNAKE_CASE_						:							torch.FloatTensor					,     SCREAMING_SNAKE_CASE_						:							int="epsilon"					,     SCREAMING_SNAKE_CASE_						:							int=None					,     SCREAMING_SNAKE_CASE_						:							bool = True					,     )  ->		Dict:
						SCREAMING_SNAKE_CASE						=   timestep

						if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
												SCREAMING_SNAKE_CASE						=   torch.split(_SCREAMING_SNAKE_CASE					,     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(_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						=   (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=_SCREAMING_SNAKE_CASE    ).to(model_output.device    )
												SCREAMING_SNAKE_CASE						=   (self._get_variance(_SCREAMING_SNAKE_CASE					,     predicted_variance=_SCREAMING_SNAKE_CASE    ) ** 0.5) * noise

						SCREAMING_SNAKE_CASE						=   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    lowerCAmelCase      (   __a       ):




		'''simple docstring'''







		def __init__(      self		,			lowerCAmelCase__		,			lowerCAmelCase__		,			lowerCAmelCase__ = 1.0		,			)	->				List[Any]:
								super().__init__()
								SCREAMING_SNAKE_CASE						=   bit_scale
								SCREAMING_SNAKE_CASE						=   (
								    ddim_bit_scheduler_step if isinstance(__UpperCamelCase		,			__UpperCamelCase							) else ddpm_bit_scheduler_step
								)

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







		@torch.no_grad()
		def __call__(      self		,			lowerCAmelCase__ = 256		,			lowerCAmelCase__ = 256		,			lowerCAmelCase__ = 50		,			lowerCAmelCase__ = None		,			lowerCAmelCase__ = 1		,			lowerCAmelCase__ = "pil"		,			lowerCAmelCase__ = True		,			**lowerCAmelCase__		,			)	->				Union[Tuple, ImagePipelineOutput]:
								SCREAMING_SNAKE_CASE						=   torch.randn(
								    (batch_size, self.unet.config.in_channels, height, width)		,			generator=__UpperCamelCase		,			)
								SCREAMING_SNAKE_CASE						=   decimal_to_bits(__UpperCamelCase							) * self.bit_scale
								SCREAMING_SNAKE_CASE						=   latents.to(self.device							)

								self.scheduler.set_timesteps(__UpperCamelCase							)

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

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

								SCREAMING_SNAKE_CASE						=   bits_to_decimal(__UpperCamelCase							)

								if output_type == "pil":
														SCREAMING_SNAKE_CASE						=   self.numpy_to_pil(__UpperCamelCase							)

								if not return_dict:
														return (image,)

								return ImagePipelineOutput(images=__UpperCamelCase							)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	{
    "configuration_bridgetower": [
        "BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BridgeTowerConfig",
        "BridgeTowerTextConfig",
        "BridgeTowerVisionConfig",
    ],
    "processing_bridgetower": ["BridgeTowerProcessor"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE		:     Optional[Any]         =	["BridgeTowerImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE		:     Tuple         =	[
	    "BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "BridgeTowerForContrastiveLearning",
	    "BridgeTowerForImageAndTextRetrieval",
	    "BridgeTowerForMaskedLM",
	    "BridgeTowerModel",
	    "BridgeTowerPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_bridgetower import (
	    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BridgeTowerConfig,
	    BridgeTowerTextConfig,
	    BridgeTowerVisionConfig,
	)
	from .processing_bridgetower import BridgeTowerProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_bridgetower import BridgeTowerImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_bridgetower import (
		    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BridgeTowerForContrastiveLearning,
		    BridgeTowerForImageAndTextRetrieval,
		    BridgeTowerForMaskedLM,
		    BridgeTowerModel,
		    BridgeTowerPreTrainedModel,
		)


else:
	import sys

	SCREAMING_SNAKE_CASE		:     List[Any]         =	_LazyModule(__name__, globals()["__file__"], _import_structure)






