




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    :		str			   =       8

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	int     ,							lowercase     :	Dict=BITS					):


      '''simple docstring'''



      lowerCamelCase_							=   x.device

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

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

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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Optional[Any]     ,							lowercase     :	Tuple=BITS					):


      '''simple docstring'''



      lowerCamelCase_							=   x.device

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

      lowerCamelCase_							=   rearrange(lowercase     ,							'd -> d 1 1'					)
      lowerCamelCase_							=   rearrange(lowercase     ,							'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 / 2_55).clamp(0.0     ,							1.0					)

def       _SCREAMING_SNAKE_CASE      (							self     :	Optional[int]     ,							lowercase     :	torch.FloatTensor     ,							lowercase     :	int     ,							lowercase     :	torch.FloatTensor     ,							lowercase     :	float = 0.0     ,							lowercase     :	bool = True     ,							lowercase     :	int=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)
      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(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)
      lowerCamelCase_							=   self._get_variance(lowercase     ,							lowercase					)
      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(lowercase					) else 'cpu'
            lowerCamelCase_							=   torch.randn(model_output.shape     ,							dtype=model_output.dtype     ,							generator=lowercase					).to(lowercase					)
            lowerCamelCase_							=   self._get_variance(lowercase     ,							lowercase					) ** 0.5 * eta * noise

            lowerCamelCase_							=   prev_sample + variance

      if not return_dict:
            return (prev_sample,)

      return DDIMSchedulerOutput(prev_sample=lowercase     ,							pred_original_sample=lowercase					)

def       _SCREAMING_SNAKE_CASE      (							self     :	Optional[int]     ,							lowercase     :	torch.FloatTensor     ,							lowercase     :	int     ,							lowercase     :	torch.FloatTensor     ,							lowercase     :	Dict="epsilon"     ,							lowercase     :	Tuple=None     ,							lowercase     :	bool = True     ,							):


      '''simple docstring'''



      lowerCamelCase_							=   timestep

      if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
            lowerCamelCase_					,    lowerCamelCase_							=   torch.split(lowercase     ,							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(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
      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=lowercase					).to(model_output.device					)
            lowerCamelCase_							=   (self._get_variance(lowercase     ,							predicted_variance=lowercase					) ** 0.5) * noise

      lowerCamelCase_							=   pred_prev_sample + variance

      if not return_dict:
            return (pred_prev_sample,)

      return DDPMSchedulerOutput(prev_sample=lowercase     ,							pred_original_sample=lowercase					)







class   A(						UpperCamelCase       ):



 '''simple docstring'''


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

       """simple docstring"""






       super().__init__()
       lowerCamelCase_							=   bit_scale
       lowerCamelCase_							=   (
           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 :  Optional[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_ :  Tuple						,							)       ->     Union[Tuple, ImagePipelineOutput]:

       """simple docstring"""






       lowerCamelCase_							=   torch.randn(
           (batch_size, self.unet.config.in_channels, height, width)						,							generator=A_						,							)
       lowerCamelCase_							=   decimal_to_bits(A_  ) * self.bit_scale
       lowerCamelCase_							=   latents.to(self.device  )

       self.scheduler.set_timesteps(A_  )

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

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

       lowerCamelCase_							=   bits_to_decimal(A_  )

       if output_type == "pil":
             lowerCamelCase_							=   self.numpy_to_pil(A_  )

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=A_  )









from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase    :		Optional[Any]			   =       logging.get_logger(__name__)

lowerCamelCase    :		Tuple			   =       {
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''transfo-xl'''
 UpperCamelCase						       =						['''mems''']
 UpperCamelCase						       =						{
     '''n_token''': '''vocab_size''',
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''n_head''',
     '''num_hidden_layers''': '''n_layer''',
 }

 def __init__(    self :  Any						,							A_ :  Optional[Any]=267735						,							A_ :  Optional[Any]=[20000, 40000, 200000]						,							A_ :  Union[str, Any]=1024						,							A_ :  Optional[Any]=1024						,							A_ :  Optional[int]=16						,							A_ :  Any=64						,							A_ :  List[Any]=4096						,							A_ :  str=4						,							A_ :  int=False						,							A_ :  List[Any]=18						,							A_ :  Optional[int]=1600						,							A_ :  Union[str, Any]=1000						,							A_ :  Optional[Any]=True						,							A_ :  Optional[int]=True						,							A_ :  List[str]=0						,							A_ :  int=-1						,							A_ :  List[Any]=True						,							A_ :  List[Any]=0.1						,							A_ :  str=0.0						,							A_ :  Dict=True						,							A_ :  Dict="normal"						,							A_ :  Dict=0.01						,							A_ :  Optional[Any]=0.01						,							A_ :  Any=0.02						,							A_ :  int=1E-5						,							A_ :  List[str]=0						,							**A_ :  Optional[Any]						,							)       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   vocab_size
       lowerCamelCase_							=   []
       self.cutoffs.extend(A_  )
       if proj_share_all_but_first:
             lowerCamelCase_							=   [False] + [True] * len(self.cutoffs  )
       else:
             lowerCamelCase_							=   [False] + [False] * len(self.cutoffs  )
       lowerCamelCase_							=   d_model
       lowerCamelCase_							=   d_embed
       lowerCamelCase_							=   d_head
       lowerCamelCase_							=   d_inner
       lowerCamelCase_							=   div_val
       lowerCamelCase_							=   pre_lnorm
       lowerCamelCase_							=   n_layer
       lowerCamelCase_							=   n_head
       lowerCamelCase_							=   mem_len
       lowerCamelCase_							=   same_length
       lowerCamelCase_							=   attn_type
       lowerCamelCase_							=   clamp_len
       lowerCamelCase_							=   sample_softmax
       lowerCamelCase_							=   adaptive
       lowerCamelCase_							=   dropout
       lowerCamelCase_							=   dropatt
       lowerCamelCase_							=   untie_r
       lowerCamelCase_							=   init
       lowerCamelCase_							=   init_range
       lowerCamelCase_							=   proj_init_std
       lowerCamelCase_							=   init_std
       lowerCamelCase_							=   layer_norm_epsilon
       super().__init__(eos_token_id=A_						,							**A_  )

 @property
 def     a__			(    self :  Tuple  )       ->     Any:

       """simple docstring"""






       logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit."""  )
       return -1





 @max_position_embeddings.setter
 def     a__			(    self :  Dict						,							A_ :  Optional[int]  )       ->     List[Any]:

       """simple docstring"""






       raise NotImplementedError(
           f"""The model {self.model_type} is one of the few models that has no sequence length limit."""  )



