
"""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_      )

"""simple docstring"""


from argparse import ArgumentParser

from . import BaseTransformersCLICommand






def       _snake_case				(		snake_case__   :     Optional[int]     ):
	return DownloadCommand(args.model	,      args.cache_dir	,      args.force	,      args.trust_remote_code     )







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						@staticmethod
						def 						_SCREAMING_SNAKE_CASE      ( A_     :      ArgumentParser      )     ->   Any:
							A						   =   parser.add_parser('download'      )
							download_parser.add_argument(
							    '--cache-dir'  ,type=A_  ,default=A_  ,help='Path to location to store the models'      )
							download_parser.add_argument(
							    '--force'  ,action='store_true'  ,help='Force the model to be download even if already in cache-dir'      )
							download_parser.add_argument(
							    '--trust-remote-code'  ,action='store_true'  ,help='Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'  ,)
							download_parser.add_argument('model'  ,type=A_  ,help='Name of the model to download'      )
							download_parser.set_defaults(func=A_      )


						def __init__( self     :      Dict  ,A_     :      str  ,A_     :      str  ,A_     :      bool  ,A_     :      bool      )     ->   Union[str, Any]:
							A						   =   model
							A						   =   cache
							A						   =   force
							A						   =   trust_remote_code




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Optional[int]:
							from ..models.auto import AutoModel, AutoTokenizer

							AutoModel.from_pretrained(
							    self._model  ,cache_dir=self._cache  ,force_download=self._force  ,trust_remote_code=self._trust_remote_code      )
							AutoTokenizer.from_pretrained(
							    self._model  ,cache_dir=self._cache  ,force_download=self._force  ,trust_remote_code=self._trust_remote_code      )