




'''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


lowerCamelCase_			=						8
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[Any]	,					__A    :			Dict=BITS  )			->			Union[str, Any]:
   _SCREAMING_SNAKE_CASE	=				x.device

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

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

   _SCREAMING_SNAKE_CASE	=				((x & mask) != 0).float()
   _SCREAMING_SNAKE_CASE	=				rearrange(__A	,					"b c d h w -> b (c d) h w"  )
   _SCREAMING_SNAKE_CASE	=				bits * 2 - 1
   return bits
def 			SCREAMING_SNAKE_CASE_			(							__A    :			str	,					__A    :			List[Any]=BITS  )			->			Tuple:
   _SCREAMING_SNAKE_CASE	=				x.device

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

   _SCREAMING_SNAKE_CASE	=				rearrange(__A	,					"d -> d 1 1"  )
   _SCREAMING_SNAKE_CASE	=				rearrange(__A	,					"b (c d) h w -> b c d h w"	,					d=8  )
   _SCREAMING_SNAKE_CASE	=				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    :			Optional[int]	,					__A    :			torch.FloatTensor	,					__A    :			int	,					__A    :			torch.FloatTensor	,					__A    :			float = 0.0	,					__A    :			bool = True	,					__A    :			Optional[int]=None	,					__A    :			bool = 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)
   _SCREAMING_SNAKE_CASE	=				timestep - self.config.num_train_timesteps // self.num_inference_steps

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

   _SCREAMING_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
   _SCREAMING_SNAKE_CASE	=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

   # 4. Clip "predicted x_0"
   _SCREAMING_SNAKE_CASE	=				self.bit_scale
   if self.config.clip_sample:
      _SCREAMING_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)
   _SCREAMING_SNAKE_CASE	=				self._get_variance(__A	,					__A  )
   _SCREAMING_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
      _SCREAMING_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
   _SCREAMING_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
   _SCREAMING_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
      _SCREAMING_SNAKE_CASE	=				model_output.device if torch.is_tensor(__A  ) else "cpu"
      _SCREAMING_SNAKE_CASE	=				torch.randn(model_output.shape	,					dtype=model_output.dtype	,					generator=__A  ).to(__A  )
      _SCREAMING_SNAKE_CASE	=				self._get_variance(__A	,					__A  ) ** 0.5 * eta * noise

      _SCREAMING_SNAKE_CASE	=				prev_sample + variance

   if not return_dict:
      return (prev_sample,)

   return DDIMSchedulerOutput(prev_sample=__A	,					pred_original_sample=__A  )
def 			SCREAMING_SNAKE_CASE_			(							self    :			Optional[Any]	,					__A    :			torch.FloatTensor	,					__A    :			int	,					__A    :			torch.FloatTensor	,					__A    :			Union[str, Any]="epsilon"	,					__A    :			Union[str, Any]=None	,					__A    :			bool = True	,					)			->			Union[DDPMSchedulerOutput, Tuple]:
   _SCREAMING_SNAKE_CASE	=				timestep

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

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

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

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

   _SCREAMING_SNAKE_CASE	=				pred_prev_sample + variance

   if not return_dict:
      return (pred_prev_sample,)

   return DDPMSchedulerOutput(prev_sample=__A	,					pred_original_sample=__A  )







class 		lowercase_   (			A       ):


   """simple docstring"""

   def __init__(   self :     Tuple  ,				__lowerCamelCase :     UNetaDConditionModel  ,				__lowerCamelCase :     Union[DDIMScheduler, DDPMScheduler]  ,				__lowerCamelCase :     Optional[float] = 1.0  ,				):

      """simple docstring"""
      super().__init__()
      _SCREAMING_SNAKE_CASE	=				bit_scale
      _SCREAMING_SNAKE_CASE	=				(
          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 :     int  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				torch.randn(
          (batch_size, self.unet.config.in_channels, height, width)  ,				generator=__lowerCamelCase  ,				)
      _SCREAMING_SNAKE_CASE	=				decimal_to_bits(__lowerCamelCase   ) * self.bit_scale
      _SCREAMING_SNAKE_CASE	=				latents.to(self.device   )

      self.scheduler.set_timesteps(__lowerCamelCase   )

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

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

      _SCREAMING_SNAKE_CASE	=				bits_to_decimal(__lowerCamelCase   )

      if output_type == "pil":
         _SCREAMING_SNAKE_CASE	=				self.numpy_to_pil(__lowerCamelCase   )

      if not return_dict:
         return (image,)

      return ImagePipelineOutput(images=__lowerCamelCase   )







'''simple docstring'''



import torch

from transformers import AutoModel







class 		lowercase_   (			torch.nn.Module       ):


   """simple docstring"""

   def __init__(   self :     List[Any]  ,				__lowerCamelCase :     Union[str, Any]="sayef/fsner-bert-base-uncased"   ):

      """simple docstring"""
      super(__lowerCamelCase  ,				self   ).__init__()

      _SCREAMING_SNAKE_CASE	=				AutoModel.from_pretrained(__lowerCamelCase  ,				return_dict=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				torch.nn.CosineSimilarity(3  ,				1e-08   )
      _SCREAMING_SNAKE_CASE	=				torch.nn.Softmax(dim=1   )



   def 							lowerCAmelCase_      (   self :     Dict  ,				**__lowerCamelCase :     Any   ):

      """simple docstring"""
      return self.bert(**__lowerCamelCase   ).last_hidden_state



   def 							lowerCAmelCase_      (   self :     Optional[Any]  ,				__lowerCamelCase :     List[str]   ):

      """simple docstring"""
      return token_embeddings.sum(2  ,				keepdim=__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Tuple=1   ):

      """simple docstring"""
      return self.softmax(T * self.cos(__lowerCamelCase  ,				__lowerCamelCase   )   )



   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     str   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				W_supports["sizes"].tolist()
      _SCREAMING_SNAKE_CASE	=				W_supports["start_token_id"].item()
      _SCREAMING_SNAKE_CASE	=				W_supports["end_token_id"].item()

      del W_supports["sizes"]
      del W_supports["start_token_id"]
      del W_supports["end_token_id"]

      _SCREAMING_SNAKE_CASE	=				self.BERT(**__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				self.BERT(**__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				None
      _SCREAMING_SNAKE_CASE	=				None

      _SCREAMING_SNAKE_CASE	=				W_supports["input_ids"] == start_token_id
      _SCREAMING_SNAKE_CASE	=				W_supports["input_ids"] == end_token_id

      for i, size in enumerate(__lowerCamelCase   ):
         if i == 0:
            _SCREAMING_SNAKE_CASE	=				0
         else:
            _SCREAMING_SNAKE_CASE	=				support_sizes[i - 1]

         _SCREAMING_SNAKE_CASE	=				S[s : s + size][start_token_masks[s : s + size]]
         _SCREAMING_SNAKE_CASE	=				S[s : s + size][end_token_masks[s : s + size]]

         _SCREAMING_SNAKE_CASE	=				torch.matmul(q[i]  ,				s_start.T   ).sum(1   ).softmax(0   )
         _SCREAMING_SNAKE_CASE	=				torch.matmul(q[i]  ,				s_end.T   ).sum(1   ).softmax(0   )

         if p_starts is not None:
            _SCREAMING_SNAKE_CASE	=				torch.vstack((p_starts, p_start)   )
            _SCREAMING_SNAKE_CASE	=				torch.vstack((p_ends, p_end)   )
         else:
            _SCREAMING_SNAKE_CASE	=				p_start
            _SCREAMING_SNAKE_CASE	=				p_end

      return p_starts, p_ends

