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__		:					List[Any]						=					8







def     _a				(      lowercase__  :			List[str]     ,  lowercase__  :			str=BITS ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				List[Any]			=		x.device

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

     SCREAMING_SNAKE_CASE__				:				int			=		2 ** torch.arange(bits - 1     ,  -1     ,  -1     ,  device=lowercase__ )
     SCREAMING_SNAKE_CASE__				:				str			=		rearrange(lowercase__     ,  'd -> d 1 1' )
     SCREAMING_SNAKE_CASE__				:				List[str]			=		rearrange(lowercase__     ,  'b c h w -> b c 1 h w' )

     SCREAMING_SNAKE_CASE__				:				List[Any]			=		((x & mask) != 0).float()
     SCREAMING_SNAKE_CASE__				:				Dict			=		rearrange(lowercase__     ,  'b c d h w -> b (c d) h w' )
     SCREAMING_SNAKE_CASE__				:				Any			=		bits * 2 - 1
     return bits







def     _a				(      lowercase__  :			Optional[Any]     ,  lowercase__  :			Union[str, Any]=BITS ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				str			=		x.device

     SCREAMING_SNAKE_CASE__				:				List[Any]			=		(x > 0).int()
     SCREAMING_SNAKE_CASE__				:				int			=		2 ** torch.arange(bits - 1     ,  -1     ,  -1     ,  device=lowercase__     ,  dtype=torch.intaa )

     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		rearrange(lowercase__     ,  'd -> d 1 1' )
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		rearrange(lowercase__     ,  'b (c d) h w -> b c d h w'     ,  d=8 )
     SCREAMING_SNAKE_CASE__				:				Dict			=		reduce(x * mask     ,  'b c d h w -> b c h w'     ,  'sum' )
     return (dec / 2_55).clamp(0.0     ,  1.0 )







def     _a				(      self  :			Any     ,  lowercase__  :			torch.FloatTensor     ,  lowercase__  :			int     ,  lowercase__  :			torch.FloatTensor     ,  lowercase__  :			float = 0.0     ,  lowercase__  :			bool = True     ,  lowercase__  :			List[str]=None     ,  lowercase__  :			bool = True     ,  ):







     '''simple docstring'''


     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__				:				List[Any]			=		timestep - self.config.num_train_timesteps // self.num_inference_steps

     # 2. compute alphas, betas
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.alphas_cumprod[timestep]
     SCREAMING_SNAKE_CASE__				:				Dict			=		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		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__				:				Dict			=		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

     # 4. Clip "predicted x_0"
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.bit_scale
     if self.config.clip_sample:
          SCREAMING_SNAKE_CASE__				:				Any			=		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)
     SCREAMING_SNAKE_CASE__				:				Any			=		self._get_variance(lowercase__     ,  lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Any			=		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__				:				int			=		(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__				:				Any			=		(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__				:				str			=		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__				:				Optional[Any]			=		model_output.device if torch.is_tensor(lowercase__ ) else 'cpu'
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.randn(model_output.shape     ,  dtype=model_output.dtype     ,  generator=lowercase__ ).to(lowercase__ )
          SCREAMING_SNAKE_CASE__				:				List[str]			=		self._get_variance(lowercase__     ,  lowercase__ ) ** 0.5 * eta * noise

          SCREAMING_SNAKE_CASE__				:				str			=		prev_sample + variance

     if not return_dict:
          return (prev_sample,)

     return DDIMSchedulerOutput(prev_sample=lowercase__     ,  pred_original_sample=lowercase__ )







def     _a				(      self  :			List[Any]     ,  lowercase__  :			torch.FloatTensor     ,  lowercase__  :			int     ,  lowercase__  :			torch.FloatTensor     ,  lowercase__  :			Optional[int]="epsilon"     ,  lowercase__  :			Any=None     ,  lowercase__  :			bool = True     ,  ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Dict			=		timestep

     if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
          SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.split(lowercase__     ,  sample.shape[1]     ,  dim=1 )
     else:
          SCREAMING_SNAKE_CASE__				:				Dict			=		None

     # 1. compute alphas, betas
     SCREAMING_SNAKE_CASE__				:				Tuple			=		self.alphas_cumprod[t]
     SCREAMING_SNAKE_CASE__				:				str			=		self.alphas_cumprod[t - 1] if t > 0 else self.one
     SCREAMING_SNAKE_CASE__				:				int			=		1 - alpha_prod_t
     SCREAMING_SNAKE_CASE__				:				int			=		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__				:				Optional[Any]			=		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif prediction_type == "sample":
          SCREAMING_SNAKE_CASE__				:				List[str]			=		model_output
     else:
          raise ValueError(f'''Unsupported prediction_type {prediction_type}.''' )

     # 3. Clip "predicted x_0"
     SCREAMING_SNAKE_CASE__				:				str			=		self.bit_scale
     if self.config.clip_sample:
          SCREAMING_SNAKE_CASE__				:				Any			=		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
     SCREAMING_SNAKE_CASE__				:				str			=		(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
     SCREAMING_SNAKE_CASE__				:				Dict			=		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__				:				Dict			=		pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

     SCREAMING_SNAKE_CASE__				:				str			=		pred_prev_sample + variance

     if not return_dict:
          return (pred_prev_sample,)

     return DDPMSchedulerOutput(prev_sample=lowercase__     ,  pred_original_sample=lowercase__ )







class       snake_case					(      UpperCamelCase_						):




  def __init__(      self     :	List[Any]     , a_     :	UNetaDConditionModel     , a_     :	Union[DDIMScheduler, DDPMScheduler]     , a_     :	Optional[float] = 1.0     , )->       Optional[Any]:




       """simple docstring"""
       super().__init__()
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		bit_scale
       SCREAMING_SNAKE_CASE__				:				List[str]			=		(
           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     :	int     , 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_     :	Union[str, Any]     , )->       Union[Tuple, ImagePipelineOutput]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.randn(
           (batch_size, self.unet.config.in_channels, height, width)     , generator=a_     , )
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		decimal_to_bits(a_				) * self.bit_scale
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		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__				:				Any			=		self.unet(a_     , a_				).sample

            # compute the previous noisy sample x_t -> x_t-1
            SCREAMING_SNAKE_CASE__				:				int			=		self.scheduler.step(a_     , a_     , a_				).prev_sample

       SCREAMING_SNAKE_CASE__				:				Any			=		bits_to_decimal(a_				)

       if output_type == "pil":
            SCREAMING_SNAKE_CASE__				:				str			=		self.numpy_to_pil(a_				)

       if not return_dict:
            return (image,)

       return ImagePipelineOutput(images=a_				)




import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
      from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     if is_torch_version('<'     ,  '2.0.0' ) or not hasattr(lowercase__     ,  '_dynamo' ):
          return False
     return isinstance(lowercase__     ,  torch._dynamo.eval_frame.OptimizedModule )







def     _a				(      lowercase__  :			Optional[Any]     ,  lowercase__  :			bool = True ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

     SCREAMING_SNAKE_CASE__				:				Dict			=		is_compiled_module(lowercase__ )
     if is_compiled:
          SCREAMING_SNAKE_CASE__				:				Tuple			=		model
          SCREAMING_SNAKE_CASE__				:				int			=		model._orig_mod

     if is_deepspeed_available():
          options += (DeepSpeedEngine,)

     while isinstance(lowercase__     ,  lowercase__ ):
          SCREAMING_SNAKE_CASE__				:				Any			=		model.module

     if not keep_fpaa_wrapper:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		getattr(lowercase__     ,  'forward' )
          SCREAMING_SNAKE_CASE__				:				str			=		model.__dict__.pop('_original_forward'     ,  lowercase__ )
          if original_forward is not None:
               while hasattr(lowercase__     ,  '__wrapped__' ):
                    SCREAMING_SNAKE_CASE__				:				Dict			=		forward.__wrapped__
                    if forward == original_forward:
                         break
               SCREAMING_SNAKE_CASE__				:				Dict			=		forward
          if getattr(lowercase__     ,  '_converted_to_transformer_engine'     ,  lowercase__ ):
               convert_model(lowercase__     ,  to_transformer_engine=lowercase__ )

     if is_compiled:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		model
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		compiled_model

     return model







def     _a				(      ):







     '''simple docstring'''


     PartialState().wait_for_everyone()







def     _a				(      lowercase__  :			str     ,  lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     if PartialState().distributed_type == DistributedType.TPU:
          xm.save(lowercase__     ,  lowercase__ )
     elif PartialState().local_process_index == 0:
          torch.save(lowercase__     ,  lowercase__ )







@contextmanager
def     _a				(      **lowercase__  :			str ):







     '''simple docstring'''


     for key, value in kwargs.items():
          SCREAMING_SNAKE_CASE__				:				int			=		str(lowercase__ )

     yield

     for key in kwargs:
          if key.upper() in os.environ:
               del os.environ[key.upper()]







def     _a				(      lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     if not hasattr(lowercase__     ,  '__qualname__' ) and not hasattr(lowercase__     ,  '__name__' ):
          SCREAMING_SNAKE_CASE__				:				Any			=		getattr(lowercase__     ,  '__class__'     ,  lowercase__ )
     if hasattr(lowercase__     ,  '__qualname__' ):
          return obj.__qualname__
     if hasattr(lowercase__     ,  '__name__' ):
          return obj.__name__
     return str(lowercase__ )







def     _a				(      lowercase__  :			List[str]     ,  lowercase__  :			List[Any] ):







     '''simple docstring'''


     for key, value in source.items():
          if isinstance(lowercase__     ,  lowercase__ ):
               SCREAMING_SNAKE_CASE__				:				List[str]			=		destination.setdefault(lowercase__     ,  {} )
               merge_dicts(lowercase__     ,  lowercase__ )
          else:
               SCREAMING_SNAKE_CASE__				:				List[Any]			=		value

     return destination







def     _a				(      lowercase__  :			int = None ):







     '''simple docstring'''


     if port is None:
          SCREAMING_SNAKE_CASE__				:				int			=		2_95_00
     with socket.socket(socket.AF_INET     ,  socket.SOCK_STREAM ) as s:
          return s.connect_ex(('localhost', port) ) == 0



