





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


_snake_case  :    str						    =  8

def   a_    ( lowerCAmelCase_      :			Optional[int],	lowerCAmelCase_      :			int=BITS	):
   __lowerCAmelCase								=	x.device

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

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

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

def   a_    ( lowerCAmelCase_      :			int,	lowerCAmelCase_      :			Optional[Any]=BITS	):
   __lowerCAmelCase								=	x.device

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

   __lowerCAmelCase								=	rearrange(lowerCAmelCase_,	'd -> d 1 1'	)
   __lowerCAmelCase								=	rearrange(lowerCAmelCase_,	'b (c d) h w -> b c d h w',	d=8	)
   __lowerCAmelCase								=	reduce(x * mask,	'b c d h w -> b c h w',	'sum'	)
   return (dec / 255).clamp(0.0,	1.0	)

def   a_    ( self      :			Dict,	lowerCAmelCase_      :			torch.FloatTensor,	lowerCAmelCase_      :			int,	lowerCAmelCase_      :			torch.FloatTensor,	lowerCAmelCase_      :			float = 0.0,	lowerCAmelCase_      :			bool = True,	lowerCAmelCase_      :			Optional[int]=None,	lowerCAmelCase_      :			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)
   __lowerCAmelCase								=	timestep - self.config.num_train_timesteps // self.num_inference_steps

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

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

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

   # 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)
   __lowerCAmelCase								=	self._get_variance(lowerCAmelCase_,	lowerCAmelCase_	)
   __lowerCAmelCase								=	eta * variance ** 0.5

   if use_clipped_model_output:
      # the model_output is always re-derived from the clipped x_0 in Glide
      __lowerCAmelCase								=	(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
   __lowerCAmelCase								=	(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
   __lowerCAmelCase								=	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
      __lowerCAmelCase								=	model_output.device if torch.is_tensor(lowerCAmelCase_	) else 'cpu'
      __lowerCAmelCase								=	torch.randn(model_output.shape,	dtype=model_output.dtype,	generator=lowerCAmelCase_	).to(lowerCAmelCase_	)
      __lowerCAmelCase								=	self._get_variance(lowerCAmelCase_,	lowerCAmelCase_	) ** 0.5 * eta * noise

      __lowerCAmelCase								=	prev_sample + variance

   if not return_dict:
      return (prev_sample,)

   return DDIMSchedulerOutput(prev_sample=lowerCAmelCase_,	pred_original_sample=lowerCAmelCase_	)

def   a_    ( self      :			List[str],	lowerCAmelCase_      :			torch.FloatTensor,	lowerCAmelCase_      :			int,	lowerCAmelCase_      :			torch.FloatTensor,	lowerCAmelCase_      :			Any="epsilon",	lowerCAmelCase_      :			Optional[int]=None,	lowerCAmelCase_      :			bool = True,	):
   __lowerCAmelCase								=	timestep

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

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

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

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

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

   __lowerCAmelCase								=	pred_prev_sample + variance

   if not return_dict:
      return (pred_prev_sample,)

   return DDPMSchedulerOutput(prev_sample=lowerCAmelCase_,	pred_original_sample=lowerCAmelCase_	)




class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  def __init__(				self :		Any					,  lowerCAmelCase_ :		UNetaDConditionModel					,  lowerCAmelCase_ :		Union[DDIMScheduler, DDPMScheduler]					,  lowerCAmelCase_ :		Optional[float] = 1.0					,  )							->						List[str]:
     super().__init__()
     __lowerCAmelCase								=	bit_scale
     __lowerCAmelCase								=	(
         ddim_bit_scheduler_step if isinstance(lowerCAmelCase_					,  lowerCAmelCase_     ) else ddpm_bit_scheduler_step
     )

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






  @torch.no_grad()
  def __call__(				self :		Union[str, Any]					,  lowerCAmelCase_ :		Optional[int] = 2_5_6					,  lowerCAmelCase_ :		Optional[int] = 2_5_6					,  lowerCAmelCase_ :		Optional[int] = 5_0					,  lowerCAmelCase_ :		Optional[torch.Generator] = None					,  lowerCAmelCase_ :		Optional[int] = 1					,  lowerCAmelCase_ :		Optional[str] = "pil"					,  lowerCAmelCase_ :		bool = True					,  **lowerCAmelCase_ :		Optional[Any]					,  )							->						Union[Tuple, ImagePipelineOutput]:
     __lowerCAmelCase								=	torch.randn(
         (batch_size, self.unet.config.in_channels, height, width)					,  generator=lowerCAmelCase_					,  )
     __lowerCAmelCase								=	decimal_to_bits(lowerCAmelCase_     ) * self.bit_scale
     __lowerCAmelCase								=	latents.to(self.device     )

     self.scheduler.set_timesteps(lowerCAmelCase_     )

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

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

     __lowerCAmelCase								=	bits_to_decimal(lowerCAmelCase_     )

     if output_type == "pil":
        __lowerCAmelCase								=	self.numpy_to_pil(lowerCAmelCase_     )

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=lowerCAmelCase_     )








import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save

def   a_    ( lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			Dict,	lowerCAmelCase_      :			Tuple=1024,	lowerCAmelCase_      :			Optional[Any]=1024,	lowerCAmelCase_      :			Tuple=False,	**lowerCAmelCase_      :			Union[str, Any]	):
   __lowerCAmelCase								=	AutoTokenizer.from_pretrained(lowerCAmelCase_	)
   __lowerCAmelCase								=	SeqaSeqDataset(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	type_path='train',	**lowerCAmelCase_	)
   __lowerCAmelCase								=	tok.pad_token_id

   def get_lens(lowerCAmelCase_      :			Optional[Any]	):
      __lowerCAmelCase								=	tqdm(
          DataLoader(lowerCAmelCase_,	batch_size=512,	num_workers=8,	shuffle=lowerCAmelCase_,	collate_fn=ds.collate_fn	),	desc=str(ds.len_file	),	)
      __lowerCAmelCase								=	[]
      for batch in dl:
         __lowerCAmelCase								=	batch['input_ids'].ne(lowerCAmelCase_	).sum(1	).tolist()
         __lowerCAmelCase								=	batch['labels'].ne(lowerCAmelCase_	).sum(1	).tolist()
         if consider_target:
            for src, tgt in zip(lowerCAmelCase_,	lowerCAmelCase_	):
               max_lens.append(max(lowerCAmelCase_,	lowerCAmelCase_	)	)
         else:
            max_lens.extend(lowerCAmelCase_	)
      return max_lens

   __lowerCAmelCase								=	get_lens(lowerCAmelCase_	)
   __lowerCAmelCase								=	SeqaSeqDataset(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	type_path='val',	**lowerCAmelCase_	)
   __lowerCAmelCase								=	get_lens(lowerCAmelCase_	)
   pickle_save(lowerCAmelCase_,	train_ds.len_file	)
   pickle_save(lowerCAmelCase_,	val_ds.len_file	)


if __name__ == "__main__":
     fire.Fire(save_len_file)

