
"""simple docstring"""


from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
		from .pipeline_kandinsky import KandinskyPipeline
		from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
		from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
		from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
		from .text_encoder import MultilingualCLIP

"""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


_lowercase							   =				8






def       _snake_case				(		snake_case__   :     Tuple	,      snake_case__   :     Optional[int]=BITS     ):
	A						   =   x.device

	A						   =   (x * 255).int().clamp(0	,      255     )

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

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






def       _snake_case				(		snake_case__   :     Any	,      snake_case__   :     Any=BITS     ):
	A						   =   x.device

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

	A						   =   rearrange(snake_case__	,      'd -> d 1 1'     )
	A						   =   rearrange(snake_case__	,      'b (c d) h w -> b c d h w'	,      d=8     )
	A						   =   reduce(x * mask	,      'b c d h w -> b c h w'	,      'sum'     )
	return (dec / 255).clamp(0.0	,      1.0     )






def       _snake_case				(		self   :     Optional[int]	,      snake_case__   :     torch.FloatTensor	,      snake_case__   :     int	,      snake_case__   :     torch.FloatTensor	,      snake_case__   :     float = 0.0	,      snake_case__   :     bool = True	,      snake_case__   :     List[str]=None	,      snake_case__   :     bool = True	,      ):
	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)
	A						   =   timestep - self.config.num_train_timesteps // self.num_inference_steps

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

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

	# 4. Clip "predicted x_0"
	A						   =   self.bit_scale
	if self.config.clip_sample:
		A						   =   torch.clamp(snake_case__	,      -scale	,      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)
	A						   =   self._get_variance(snake_case__	,      snake_case__     )
	A						   =   eta * variance ** 0.5

	if use_clipped_model_output:
		# the model_output is always re-derived from the clipped x_0 in Glide
		A						   =   (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
	A						   =   (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
	A						   =   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
		A						   =   model_output.device if torch.is_tensor(snake_case__     ) else 'cpu'
		A						   =   torch.randn(model_output.shape	,      dtype=model_output.dtype	,      generator=snake_case__     ).to(snake_case__     )
		A						   =   self._get_variance(snake_case__	,      snake_case__     ) ** 0.5 * eta * noise

		A						   =   prev_sample + variance

	if not return_dict:
		return (prev_sample,)

	return DDIMSchedulerOutput(prev_sample=snake_case__	,      pred_original_sample=snake_case__     )






def       _snake_case				(		self   :     Dict	,      snake_case__   :     torch.FloatTensor	,      snake_case__   :     int	,      snake_case__   :     torch.FloatTensor	,      snake_case__   :     Tuple="epsilon"	,      snake_case__   :     List[str]=None	,      snake_case__   :     bool = True	,      ):
	A						   =   timestep

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

	# 1. compute alphas, betas
	A						   =   self.alphas_cumprod[t]
	A						   =   self.alphas_cumprod[t - 1] if t > 0 else self.one
	A						   =   1 - alpha_prod_t
	A						   =   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":
		A						   =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
	elif prediction_type == "sample":
		A						   =   model_output
	else:
		raise ValueError(F'Unsupported prediction_type {prediction_type}.'     )

	# 3. Clip "predicted x_0"
	A						   =   self.bit_scale
	if self.config.clip_sample:
		A						   =   torch.clamp(snake_case__	,      -scale	,      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
	A						   =   (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
	A						   =   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
	A						   =   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

	A						   =   pred_prev_sample + variance

	if not return_dict:
		return (pred_prev_sample,)

	return DDPMSchedulerOutput(prev_sample=snake_case__	,      pred_original_sample=snake_case__     )







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						def __init__( self     :      Union[str, Any]  ,A_     :      UNetaDConditionModel  ,A_     :      Union[DDIMScheduler, DDPMScheduler]  ,A_     :      Optional[float] = 1.0  ,)     ->   Optional[int]:
							super().__init__()
							A						   =   bit_scale
							A						   =   (
							    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     :      Tuple  ,A_     :      Optional[int] = 256  ,A_     :      Optional[int] = 256  ,A_     :      Optional[int] = 50  ,A_     :      Optional[torch.Generator] = None  ,A_     :      Optional[int] = 1  ,A_     :      Optional[str] = "pil"  ,A_     :      bool = True  ,**A_     :      Optional[Any]  ,)     ->   Union[Tuple, ImagePipelineOutput]:
							A						   =   torch.randn(
							    (batch_size, self.unet.config.in_channels, height, width)  ,generator=A_  ,)
							A						   =   decimal_to_bits(A_      ) * self.bit_scale
							A						   =   latents.to(self.device      )

							self.scheduler.set_timesteps(A_      )

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

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

							A						   =   bits_to_decimal(A_      )

							if output_type == "pil":
								A						   =   self.numpy_to_pil(A_      )

							if not return_dict:
								return (image,)

							return ImagePipelineOutput(images=A_      )