




"""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


SCREAMING_SNAKE_CASE    =	8







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_=BITS						)						->		List[str]:
 A__	=    x.device

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

 A__	=    2 ** torch.arange(bits - 1      ,			-1      ,			-1      ,			device=lowercase_						)
 A__	=    rearrange(lowercase_      ,			"d -> d 1 1"						)
 A__	=    rearrange(lowercase_      ,			"b c h w -> b c 1 h w"						)

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







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_=BITS						)						->		Tuple:
 A__	=    x.device

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

 A__	=    rearrange(lowercase_      ,			"d -> d 1 1"						)
 A__	=    rearrange(lowercase_      ,			"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 / 2_55).clamp(0.0      ,			1.0						)







def   _SCREAMING_SNAKE_CASE       (							self      ,			lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_ = 0.0      ,			lowercase_ = True      ,			lowercase_=None      ,			lowercase_ = 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)
 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(lowercase_      ,			-scale      ,			lowercase_						)

 # 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(lowercase_      ,			lowercase_						)
 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(lowercase_						) else "cpu"
  A__	=    torch.randn(model_output.shape      ,			dtype=model_output.dtype      ,			generator=lowercase_						).to(lowercase_						)
  A__	=    self._get_variance(lowercase_      ,			lowercase_						) ** 0.5 * eta * noise

  A__	=    prev_sample + variance

 if not return_dict:
  return (prev_sample,)

 return DDIMSchedulerOutput(prev_sample=lowercase_      ,			pred_original_sample=lowercase_						)







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

 if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
  A__,				A__	=    torch.split(lowercase_      ,			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(lowercase_      ,			-scale      ,			lowercase_						)

 # 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=lowercase_						).to(model_output.device						)
  A__	=    (self._get_variance(lowercase_      ,			predicted_variance=lowercase_						) ** 0.5) * noise

 A__	=    pred_prev_sample + variance

 if not return_dict:
  return (pred_prev_sample,)

 return DDPMSchedulerOutput(prev_sample=lowercase_      ,			pred_original_sample=lowercase_						)






class      UpperCAmelCase_  (						A_							):




    def __init__(   self						:	int     ,			snake_case_						:	UNetaDConditionModel     ,			snake_case_						:	Union[DDIMScheduler, DDPMScheduler]     ,			snake_case_						:	Optional[float] = 1.0     ,			)				->							int:




     '''simple docstring'''

     super().__init__()
     A__	=    bit_scale
     A__	=    (
         ddim_bit_scheduler_step if isinstance(snake_case_     ,			snake_case_				) else ddpm_bit_scheduler_step
     )

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




    @torch.no_grad()
    def __call__(   self						:	Dict     ,			snake_case_						:	Optional[int] = 256     ,			snake_case_						:	Optional[int] = 256     ,			snake_case_						:	Optional[int] = 50     ,			snake_case_						:	Optional[torch.Generator] = None     ,			snake_case_						:	Optional[int] = 1     ,			snake_case_						:	Optional[str] = "pil"     ,			snake_case_						:	bool = True     ,			**snake_case_						:	str     ,			)				->							Union[Tuple, ImagePipelineOutput]:




     '''simple docstring'''

     A__	=    torch.randn(
         (batch_size, self.unet.config.in_channels, height, width)     ,			generator=snake_case_     ,			)
     A__	=    decimal_to_bits(snake_case_				) * self.bit_scale
     A__	=    latents.to(self.device				)

     self.scheduler.set_timesteps(snake_case_				)

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

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

     A__	=    bits_to_decimal(snake_case_				)

     if output_type == "pil":
      A__	=    self.numpy_to_pil(snake_case_				)

     if not return_dict:
      return (image,)

     return ImagePipelineOutput(images=snake_case_				)











"""simple docstring"""



import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
SCREAMING_SNAKE_CASE    =	object()

# For specifying empty leaf dict `{}`
SCREAMING_SNAKE_CASE    =	object()







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		List[Any]:
 A__	=    tuple((re.compile(x + "$"						) for x in qs)						)
 for i in range(len(lowercase_						) - len(lowercase_						) + 1						):
  A__	=    [x.match(lowercase_						) for x, y in zip(lowercase_      ,			ks[i:]						)]
  if matches and all(lowercase_						):
   return True
 return False







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Optional[int]:
 def replace(lowercase_      ,			lowercase_						):
  for rule, replacement in rules:
   if _match(lowercase_      ,			lowercase_						):
    return replacement
  return val

 return replace







def   _SCREAMING_SNAKE_CASE       (							)						->		List[str]:
 return [
     # embeddings
     (("transformer", "wpe", "embedding"), P("mp"      ,			lowercase_						)),
     (("transformer", "wte", "embedding"), P("mp"      ,			lowercase_						)),
     # atention
     (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(lowercase_      ,			"mp"						)),
     (("attention", "out_proj", "kernel"), P("mp"      ,			lowercase_						)),
     (("attention", "out_proj", "bias"), None),
     # mlp
     (("mlp", "c_fc", "kernel"), P(lowercase_      ,			"mp"						)),
     (("mlp", "c_fc", "bias"), P("mp"						)),
     (("mlp", "c_proj", "kernel"), P("mp"      ,			lowercase_						)),
     (("mlp", "c_proj", "bias"), None),
     # layer norms
     ((r"ln_\d+", "bias"), None),
     ((r"\d+", r"ln_\d+", "scale"), None),
     (("ln_f", "bias"), None),
     (("ln_f", "scale"), None),
 ]







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		int:
 A__	=    _get_partition_rules()
 A__	=    _replacement_rules(lowercase_						)
 A__	=    {k: _unmatched for k in flatten_dict(lowercase_						)}
 A__	=    {k: replace(lowercase_      ,			lowercase_						) for k, v in initd.items()}
 assert _unmatched not in result.values(), "Incomplete partition spec."
 return freeze(unflatten_dict(lowercase_						)						)





