






from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput


@dataclass
class   __SCREAMING_SNAKE_CASE(	a_	):
       _UpperCAmelCase     =       None
       _UpperCAmelCase     =       None
       _UpperCAmelCase     =       None
       _UpperCAmelCase     =       None


class   __SCREAMING_SNAKE_CASE(	a_	):
       def __init__(     self:      Any       ,	UpperCamelCase:      int=1       ,	UpperCamelCase:      Any=0       ,	UpperCamelCase:      Dict=2       ,	UpperCamelCase:      Dict=5_12       ,	UpperCamelCase:      Union[str, Any]="cls"       ,	UpperCamelCase:      str=False       ,	UpperCamelCase:      int=True       ,	**UpperCamelCase:      List[str]       ,	)		->		Optional[Any]:
           super().__init__(pad_token_id=__A       ,	bos_token_id=__A       ,	eos_token_id=__A       ,	**__A						)
           snake_case__									=     project_dim
           snake_case__									=     pooler_fn
           snake_case__									=     learn_encoder
           snake_case__									=     use_attention_mask


class   __SCREAMING_SNAKE_CASE(	a_	):
       _UpperCAmelCase     =       [r"pooler", r"logit_scale"]
       _UpperCAmelCase     =       [r"position_ids", r"predictions.decoder.bias"]
       _UpperCAmelCase     =       "roberta"
       _UpperCAmelCase     =       RobertaSeriesConfig
       def __init__(     self:      Dict       ,	UpperCamelCase:      Optional[Any]						)		->		Any:
           super().__init__(__A						)
           snake_case__									=     XLMRobertaModel(__A						)
           snake_case__									=     nn.Linear(config.hidden_size       ,	config.project_dim						)
           snake_case__									=     getattr(__A       ,	'has_pre_transformation'       ,	__A						)
           if self.has_pre_transformation:
               snake_case__									=     nn.Linear(config.hidden_size       ,	config.project_dim						)
               snake_case__									=     nn.LayerNorm(config.hidden_size       ,	eps=config.layer_norm_eps						)
           self.post_init()


       def lowerCAmelCase_ (     self:      Any       ,	UpperCamelCase:      Optional[torch.Tensor] = None       ,	UpperCamelCase:      Optional[torch.Tensor] = None       ,	UpperCamelCase:      Optional[torch.Tensor] = None       ,	UpperCamelCase:      Optional[torch.Tensor] = None       ,	UpperCamelCase:      Optional[torch.Tensor] = None       ,	UpperCamelCase:      Optional[torch.Tensor] = None       ,	UpperCamelCase:      Optional[torch.Tensor] = None       ,	UpperCamelCase:      Optional[torch.Tensor] = None       ,	UpperCamelCase:      Optional[bool] = None       ,	UpperCamelCase:      Optional[bool] = None       ,	UpperCamelCase:      Optional[bool] = None       ,	)		->		Tuple:
           snake_case__									=     return_dict if return_dict is not None else self.config.use_return_dict

           snake_case__									=     self.base_model(
               input_ids=__A       ,	attention_mask=__A       ,	token_type_ids=__A       ,	position_ids=__A       ,	head_mask=__A       ,	inputs_embeds=__A       ,	encoder_hidden_states=__A       ,	encoder_attention_mask=__A       ,	output_attentions=__A       ,	output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,	return_dict=__A       ,	)

           if self.has_pre_transformation:
               snake_case__									=     outputs['hidden_states'][-2]
               snake_case__									=     self.pre_LN(__A						)
               snake_case__									=     self.transformation_pre(__A						)

               return TransformationModelOutput(
                   projection_state=__A       ,	last_hidden_state=outputs.last_hidden_state       ,	hidden_states=outputs.hidden_states       ,	attentions=outputs.attentions       ,	)
           else:
               snake_case__									=     self.transformation(outputs.last_hidden_state						)
               return TransformationModelOutput(
                   projection_state=__A       ,	last_hidden_state=outputs.last_hidden_state       ,	hidden_states=outputs.hidden_states       ,	attentions=outputs.attentions       ,	)












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					:		Optional[int]	  =						8
def      a_ (      _A      ,   _A=BITS  )			->			List[Any]:







				"""simple docstring"""
				snake_case__									=     x.device

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

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

				snake_case__									=     ((x & mask) != 0).float()
				snake_case__									=     rearrange(_A      ,   'b c d h w -> b (c d) h w'  )
				snake_case__									=     bits * 2 - 1
				return bits
def      a_ (      _A      ,   _A=BITS  )			->			List[str]:







				"""simple docstring"""
				snake_case__									=     x.device

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

				snake_case__									=     rearrange(_A      ,   'd -> d 1 1'  )
				snake_case__									=     rearrange(_A      ,   'b (c d) h w -> b c d h w'      ,   d=8  )
				snake_case__									=     reduce(x * mask      ,   'b c d h w -> b c h w'      ,   'sum'  )
				return (dec / 255).clamp(0.0      ,   1.0  )
def      a_ (      self      ,   _A      ,   _A      ,   _A      ,   _A = 0.0      ,   _A = True      ,   _A=None      ,   _A = True      ,   )			->			Union[DDIMSchedulerOutput, Tuple]:







				"""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)
				snake_case__									=     timestep - self.config.num_train_timesteps // self.num_inference_steps

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

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

				# 4. Clip "predicted x_0"
				snake_case__									=     self.bit_scale
				if self.config.clip_sample:
								snake_case__									=     torch.clamp(_A      ,   -scale      ,   _A  )

				# 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)
				snake_case__									=     self._get_variance(_A      ,   _A  )
				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
								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
				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
				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
								snake_case__									=     model_output.device if torch.is_tensor(_A  ) else 'cpu'
								snake_case__									=     torch.randn(model_output.shape      ,   dtype=model_output.dtype      ,   generator=_A  ).to(_A  )
								snake_case__									=     self._get_variance(_A      ,   _A  ) ** 0.5 * eta * noise

								snake_case__									=     prev_sample + variance

				if not return_dict:
								return (prev_sample,)

				return DDIMSchedulerOutput(prev_sample=_A      ,   pred_original_sample=_A  )
def      a_ (      self      ,   _A      ,   _A      ,   _A      ,   _A="epsilon"      ,   _A=None      ,   _A = True      ,   )			->			Union[DDPMSchedulerOutput, Tuple]:







				"""simple docstring"""
				snake_case__									=     timestep

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

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

				# 3. Clip "predicted x_0"
				snake_case__									=     self.bit_scale
				if self.config.clip_sample:
								snake_case__									=     torch.clamp(_A      ,   -scale      ,   _A  )

				# 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
				snake_case__									=     (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
				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
				snake_case__									=     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

				snake_case__									=     pred_prev_sample + variance

				if not return_dict:
								return (pred_prev_sample,)

				return DDPMSchedulerOutput(prev_sample=_A      ,   pred_original_sample=_A  )


class   __SCREAMING_SNAKE_CASE(	a_	):
							def __init__(     self:      Dict       ,	UpperCamelCase:      UNetaDConditionModel       ,	UpperCamelCase:      Union[DDIMScheduler, DDPMScheduler]       ,	UpperCamelCase:      Optional[float] = 1.0       ,	)		->		Union[str, Any]:
											super().__init__()
											snake_case__									=     bit_scale
											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:      Dict       ,	UpperCamelCase:      Optional[int] = 2_56       ,	UpperCamelCase:      Optional[int] = 2_56       ,	UpperCamelCase:      Optional[int] = 50       ,	UpperCamelCase:      Optional[torch.Generator] = None       ,	UpperCamelCase:      Optional[int] = 1       ,	UpperCamelCase:      Optional[str] = "pil"       ,	UpperCamelCase:      bool = True       ,	**UpperCamelCase:      int       ,	)		->		Union[Tuple, ImagePipelineOutput]:
											snake_case__									=     torch.randn(
											    (batch_size, self.unet.config.in_channels, height, width)       ,	generator=UpperCamelCase       ,	)
											snake_case__									=     decimal_to_bits(UpperCamelCase						) * self.bit_scale
											snake_case__									=     latents.to(self.device						)

											self.scheduler.set_timesteps(UpperCamelCase						)

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

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

											snake_case__									=     bits_to_decimal(UpperCamelCase						)

											if output_type == "pil":
															snake_case__									=     self.numpy_to_pil(UpperCamelCase						)

											if not return_dict:
															return (image,)

											return ImagePipelineOutput(images=UpperCamelCase						)




