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						:   Optional[int]         =			8
def  snake_case						(__lowercase ,      __lowercase=BITS						)	->	List[str]:

						'''simple docstring'''



						_snake_case						:					str    =   x.device

						_snake_case						:					Optional[Any]    =   (x * 255).int().clamp(0 ,      255						)

						_snake_case						:					Tuple    =   2 ** torch.arange(bits - 1 ,      -1 ,      -1 ,      device=__lowercase						)
						_snake_case						:					Dict    =   rearrange(__lowercase ,      "d -> d 1 1"						)
						_snake_case						:					Optional[Any]    =   rearrange(__lowercase ,      "b c h w -> b c 1 h w"						)

						_snake_case						:					List[str]    =   ((x & mask) != 0).float()
						_snake_case						:					Optional[int]    =   rearrange(__lowercase ,      "b c d h w -> b (c d) h w"						)
						_snake_case						:					List[Any]    =   bits * 2 - 1
						return bits
def  snake_case						(__lowercase ,      __lowercase=BITS						)	->	Any:

						'''simple docstring'''



						_snake_case						:					Dict    =   x.device

						_snake_case						:					Optional[int]    =   (x > 0).int()
						_snake_case						:					Any    =   2 ** torch.arange(bits - 1 ,      -1 ,      -1 ,      device=__lowercase ,      dtype=torch.intaa						)

						_snake_case						:					Tuple    =   rearrange(__lowercase ,      "d -> d 1 1"						)
						_snake_case						:					Dict    =   rearrange(__lowercase ,      "b (c d) h w -> b c d h w" ,      d=8						)
						_snake_case						:					Dict    =   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 ,      __lowercase ,      __lowercase ,      __lowercase ,      __lowercase = 0.0 ,      __lowercase = True ,      __lowercase=None ,      __lowercase = 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						:					int    =   timestep - self.config.num_train_timesteps // self.num_inference_steps

						# 2. compute alphas, betas
						_snake_case						:					List[Any]    =   self.alphas_cumprod[timestep]
						_snake_case						:					Optional[Any]    =   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

						_snake_case						:					str    =   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						:					Tuple    =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

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

												_snake_case						:					Optional[Any]    =   prev_sample + variance

						if not return_dict:
												return (prev_sample,)

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

						'''simple docstring'''



						_snake_case						:					List[str]    =   timestep

						if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
												_snake_case    ,_snake_case						:					Optional[int]    =   torch.split(__lowercase ,      sample.shape[1] ,      dim=1						)
						else:
												_snake_case						:					str    =   None

						# 1. compute alphas, betas
						_snake_case						:					Optional[Any]    =   self.alphas_cumprod[t]
						_snake_case						:					int    =   self.alphas_cumprod[t - 1] if t > 0 else self.one
						_snake_case						:					Optional[Any]    =   1 - alpha_prod_t
						_snake_case						:					Any    =   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						:					Dict    =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
						elif prediction_type == "sample":
												_snake_case						:					Optional[Any]    =   model_output
						else:
												raise ValueError(F"""Unsupported prediction_type {prediction_type}."""						)

						# 3. Clip "predicted x_0"
						_snake_case						:					List[Any]    =   self.bit_scale
						if self.config.clip_sample:
												_snake_case						:					int    =   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
						_snake_case						:					int    =   (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
						_snake_case						:					Tuple    =   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						:					Union[str, Any]    =   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

						# 6. Add noise
						_snake_case						:					str    =   0
						if t > 0:
												_snake_case						:					List[str]    =   torch.randn(
												    model_output.size() ,      dtype=model_output.dtype ,      layout=model_output.layout ,      generator=__lowercase						).to(model_output.device						)
												_snake_case						:					List[Any]    =   (self._get_variance(__lowercase ,      predicted_variance=__lowercase						) ** 0.5) * noise

						_snake_case						:					Optional[Any]    =   pred_prev_sample + variance

						if not return_dict:
												return (pred_prev_sample,)

						return DDPMSchedulerOutput(prev_sample=__lowercase ,      pred_original_sample=__lowercase						)



class        lowercase_   (					__snake_case		):



							def __init__(						self				,    lowercase_				,    lowercase_				,    lowercase_ = 1.0				,    ):
													super().__init__()
													_snake_case						:					Union[str, Any]    =   bit_scale
													_snake_case						:					List[str]    =   (
													    ddim_bit_scheduler_step if isinstance(lowercase_				,    lowercase_    ) else ddpm_bit_scheduler_step
													)

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



							@torch.no_grad()
							def __call__(						self				,    lowercase_ = 256				,    lowercase_ = 256				,    lowercase_ = 50				,    lowercase_ = None				,    lowercase_ = 1				,    lowercase_ = "pil"				,    lowercase_ = True				,    **lowercase_				,    ):
													_snake_case						:					Dict    =   torch.randn(
													    (batch_size, self.unet.config.in_channels, height, width)				,    generator=lowercase_				,    )
													_snake_case						:					Optional[int]    =   decimal_to_bits(lowercase_    ) * self.bit_scale
													_snake_case						:					Union[str, Any]    =   latents.to(self.device    )

													self.scheduler.set_timesteps(lowercase_    )

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

																			# compute the previous noisy sample x_t -> x_t-1
																			_snake_case						:					Any    =   self.scheduler.step(lowercase_				,    lowercase_				,    lowercase_    ).prev_sample

													_snake_case						:					Dict    =   bits_to_decimal(lowercase_    )

													if output_type == "pil":
																			_snake_case						:					Optional[Any]    =   self.numpy_to_pil(lowercase_    )

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=lowercase_    )
import logging

from transformers import PretrainedConfig


__SCREAMING_SNAKE_CASE						:   Any         =			logging.getLogger(__name__)


__SCREAMING_SNAKE_CASE						:   int         =			{
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'bertabs'



							def __init__(						self				,    lowercase_=30_522				,    lowercase_=512				,    lowercase_=6				,    lowercase_=512				,    lowercase_=8				,    lowercase_=512				,    lowercase_=0.2				,    lowercase_=6				,    lowercase_=768				,    lowercase_=8				,    lowercase_=2_048				,    lowercase_=0.2				,    **lowercase_				,    ):
													super().__init__(**lowercase_    )

													_snake_case						:					List[Any]    =   vocab_size
													_snake_case						:					int    =   max_pos

													_snake_case						:					Tuple    =   enc_layers
													_snake_case						:					Optional[Any]    =   enc_hidden_size
													_snake_case						:					Union[str, Any]    =   enc_heads
													_snake_case						:					str    =   enc_ff_size
													_snake_case						:					Any    =   enc_dropout

													_snake_case						:					Tuple    =   dec_layers
													_snake_case						:					Optional[Any]    =   dec_hidden_size
													_snake_case						:					Dict    =   dec_heads
													_snake_case						:					str    =   dec_ff_size
													_snake_case						:					List[str]    =   dec_dropout