


'''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


A__						:   int				=    8



def      UpperCAmelCase__   (      UpperCAmelCase_       :							List[Any] ,       UpperCAmelCase_       :							int=BITS				)	->						int:
     __lowerCamelCase  :      List[Any]														=						x.device

     __lowerCamelCase  :      Union[str, Any]														=						(x * 2_55).int().clamp(0 ,       2_55				)

     __lowerCamelCase  :      str														=						2 ** torch.arange(bits - 1 ,       -1 ,       -1 ,       device=UpperCAmelCase_				)
     __lowerCamelCase  :      List[str]														=						rearrange(UpperCAmelCase_ ,       'd -> d 1 1'				)
     __lowerCamelCase  :      Optional[int]														=						rearrange(UpperCAmelCase_ ,       'b c h w -> b c 1 h w'				)

     __lowerCamelCase  :      int														=						((x & mask) != 0).float()
     __lowerCamelCase  :      List[Any]														=						rearrange(UpperCAmelCase_ ,       'b c d h w -> b (c d) h w'				)
     __lowerCamelCase  :      str														=						bits * 2 - 1
     return bits



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Dict ,       UpperCAmelCase_       :							str=BITS				)	->						Tuple:
     __lowerCamelCase  :      int														=						x.device

     __lowerCamelCase  :      Tuple														=						(x > 0).int()
     __lowerCamelCase  :      Optional[int]														=						2 ** torch.arange(bits - 1 ,       -1 ,       -1 ,       device=UpperCAmelCase_ ,       dtype=torch.intaa				)

     __lowerCamelCase  :      Tuple														=						rearrange(UpperCAmelCase_ ,       'd -> d 1 1'				)
     __lowerCamelCase  :      List[Any]														=						rearrange(UpperCAmelCase_ ,       'b (c d) h w -> b c d h w' ,       d=8				)
     __lowerCamelCase  :      Dict														=						reduce(x * mask ,       'b c d h w -> b c h w' ,       'sum'				)
     return (dec / 2_55).clamp(0.0 ,       1.0				)



def      UpperCAmelCase__   (      self       :							str ,       UpperCAmelCase_       :							torch.FloatTensor ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							torch.FloatTensor ,       UpperCAmelCase_       :							float = 0.0 ,       UpperCAmelCase_       :							bool = True ,       UpperCAmelCase_       :							Any=None ,       UpperCAmelCase_       :							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)
     __lowerCamelCase  :      Tuple														=						timestep - self.config.num_train_timesteps // self.num_inference_steps

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

     __lowerCamelCase  :      Dict														=						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  :      Union[str, Any]														=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

     # 4. Clip "predicted x_0"
     __lowerCamelCase  :      Any														=						self.bit_scale
     if self.config.clip_sample:
          __lowerCamelCase  :      Tuple														=						torch.clamp(UpperCAmelCase_ ,       -scale ,       UpperCAmelCase_				)

     # 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  :      int														=						self._get_variance(UpperCAmelCase_ ,       UpperCAmelCase_				)
     __lowerCamelCase  :      Any														=						eta * variance ** 0.5

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

          __lowerCamelCase  :      Tuple														=						prev_sample + variance

     if not return_dict:
          return (prev_sample,)

     return DDIMSchedulerOutput(prev_sample=UpperCAmelCase_ ,       pred_original_sample=UpperCAmelCase_				)



def      UpperCAmelCase__   (      self       :							Optional[Any] ,       UpperCAmelCase_       :							torch.FloatTensor ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							torch.FloatTensor ,       UpperCAmelCase_       :							int="epsilon" ,       UpperCAmelCase_       :							List[str]=None ,       UpperCAmelCase_       :							bool = True ,       )	->						Union[DDPMSchedulerOutput, Tuple]:
     __lowerCamelCase  :      Tuple														=						timestep

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

     # 1. compute alphas, betas
     __lowerCamelCase  :      Tuple														=						self.alphas_cumprod[t]
     __lowerCamelCase  :      Optional[Any]														=						self.alphas_cumprod[t - 1] if t > 0 else self.one
     __lowerCamelCase  :      Optional[Any]														=						1 - alpha_prod_t
     __lowerCamelCase  :      int														=						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  :      Optional[int]														=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif prediction_type == "sample":
          __lowerCamelCase  :      Union[str, Any]														=						model_output
     else:
          raise ValueError(F'Unsupported prediction_type {prediction_type}.'				)

     # 3. Clip "predicted x_0"
     __lowerCamelCase  :      List[Any]														=						self.bit_scale
     if self.config.clip_sample:
          __lowerCamelCase  :      List[Any]														=						torch.clamp(UpperCAmelCase_ ,       -scale ,       UpperCAmelCase_				)

     # 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  :      List[str]														=						(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
     __lowerCamelCase  :      Dict														=						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  :      str														=						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

     __lowerCamelCase  :      List[Any]														=						pred_prev_sample + variance

     if not return_dict:
          return (pred_prev_sample,)

     return DDPMSchedulerOutput(prev_sample=UpperCAmelCase_ ,       pred_original_sample=UpperCAmelCase_				)





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = 1.0     ,	)							->	Dict:
         super().__init__()
         __lowerCamelCase  :      Tuple														=						bit_scale
         __lowerCamelCase  :      str														=						(
             ddim_bit_scheduler_step if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ) else ddpm_bit_scheduler_step
         )

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







    @torch.no_grad()
    def __call__(				self     ,	SCREAMING_SNAKE_CASE_ = 2_56     ,	SCREAMING_SNAKE_CASE_ = 2_56     ,	SCREAMING_SNAKE_CASE_ = 50     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = 1     ,	SCREAMING_SNAKE_CASE_ = "pil"     ,	SCREAMING_SNAKE_CASE_ = True     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	Union[Tuple, ImagePipelineOutput]:
         __lowerCamelCase  :      Optional[Any]														=						torch.randn(
             (batch_size, self.unet.config.in_channels, height, width)     ,	generator=SCREAMING_SNAKE_CASE_     ,	)
         __lowerCamelCase  :      Optional[Any]														=						decimal_to_bits(SCREAMING_SNAKE_CASE_      ) * self.bit_scale
         __lowerCamelCase  :      Optional[int]														=						latents.to(self.device      )

         self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_      )

         for t in self.progress_bar(self.scheduler.timesteps      ):
              # predict the noise residual
              __lowerCamelCase  :      List[str]														=						self.unet(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ).sample

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

         __lowerCamelCase  :      Dict														=						bits_to_decimal(SCREAMING_SNAKE_CASE_      )

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

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_      )













'''simple docstring'''




from ...configuration_utils import PretrainedConfig


lowercase__       : Any         =       {
    '''google/tapas-base-finetuned-sqa''': (
        '''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wtq''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wikisql-supervised''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-tabfact''': (
        '''https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'''
    ),
}


class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			'''tapas'''


     def __init__(		self		,      _UpperCAmelCase=3_0522		,      _UpperCAmelCase=768		,      _UpperCAmelCase=12		,      _UpperCAmelCase=12		,      _UpperCAmelCase=3072		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=1024		,      _UpperCAmelCase=[3, 256, 256, 2, 256, 256, 10]		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=1e-1_2		,      _UpperCAmelCase=0		,      _UpperCAmelCase=10.0		,      _UpperCAmelCase=0		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=None		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=False		,      _UpperCAmelCase=None		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=False		,      _UpperCAmelCase=False		,      _UpperCAmelCase="ratio"		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      _UpperCAmelCase=64		,      _UpperCAmelCase=32		,      _UpperCAmelCase=False		,      _UpperCAmelCase=True		,      _UpperCAmelCase=False		,      _UpperCAmelCase=False		,      _UpperCAmelCase=True		,      _UpperCAmelCase=False		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase		,      **_UpperCAmelCase)

          # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
          __A		:					Dict						=			vocab_size
          __A		:					Tuple						=			hidden_size
          __A		:					Any						=			num_hidden_layers
          __A		:					int						=			num_attention_heads
          __A		:					Tuple						=			hidden_act
          __A		:					Tuple						=			intermediate_size
          __A		:					List[Any]						=			hidden_dropout_prob
          __A		:					int						=			attention_probs_dropout_prob
          __A		:					List[str]						=			max_position_embeddings
          __A		:					Optional[int]						=			type_vocab_sizes
          __A		:					str						=			initializer_range
          __A		:					List[str]						=			layer_norm_eps

          # Fine-tuning task hyperparameters
          __A		:					List[str]						=			positive_label_weight
          __A		:					List[Any]						=			num_aggregation_labels
          __A		:					Optional[Any]						=			aggregation_loss_weight
          __A		:					Tuple						=			use_answer_as_supervision
          __A		:					List[str]						=			answer_loss_importance
          __A		:					Any						=			use_normalized_answer_loss
          __A		:					Any						=			huber_loss_delta
          __A		:					Union[str, Any]						=			temperature
          __A		:					Tuple						=			aggregation_temperature
          __A		:					Optional[Any]						=			use_gumbel_for_cells
          __A		:					List[str]						=			use_gumbel_for_aggregation
          __A		:					Tuple						=			average_approximation_function
          __A		:					List[str]						=			cell_selection_preference
          __A		:					Dict						=			answer_loss_cutoff
          __A		:					Union[str, Any]						=			max_num_rows
          __A		:					Optional[Any]						=			max_num_columns
          __A		:					int						=			average_logits_per_cell
          __A		:					Optional[Any]						=			select_one_column
          __A		:					int						=			allow_empty_column_selection
          __A		:					List[Any]						=			init_cell_selection_weights_to_zero
          __A		:					int						=			reset_position_index_per_cell
          __A		:					Union[str, Any]						=			disable_per_token_loss

          # Aggregation hyperparameters
          __A		:					Optional[Any]						=			aggregation_labels
          __A		:					List[str]						=			no_aggregation_label_index

          if isinstance(self.aggregation_labels		,      _UpperCAmelCase):
               __A		:					Optional[Any]						=			{int(_UpperCAmelCase): v for k, v in aggregation_labels.items()}