

'''simple docstring'''






import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Union[str, Any]  ,  SCREAMING_SNAKE_CASE_					:     List[str]=0.999  ,  SCREAMING_SNAKE_CASE_					:     List[Any]="cosine"  ,  )							->       Dict:
      """simple docstring"""

      if alpha_transform_type == "cosine":

            def alpha_bar_fn(SCREAMING_SNAKE_CASE_					:     Dict   ):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(SCREAMING_SNAKE_CASE_					:     Any   ):
                  return math.exp(t * -12.0   )

      else:
            raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"   )

      SCREAMING_SNAKE_CASE_				:      Optional[int]					=	[]
      for i in range(SCREAMING_SNAKE_CASE_   ):
            SCREAMING_SNAKE_CASE_				:      Optional[int]					=	i / num_diffusion_timesteps
            SCREAMING_SNAKE_CASE_				:      List[str]					=	(i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE_   ) / alpha_bar_fn(SCREAMING_SNAKE_CASE_   )  ,  SCREAMING_SNAKE_CASE_   )   )
      return torch.tensor(SCREAMING_SNAKE_CASE_  ,  dtype=torch.floataa   )





class      SCREAMING_SNAKE_CASE__      (    _UpperCAmelCase,_UpperCAmelCase				):
    _A         =							[e.name for e in KarrasDiffusionSchedulers]
    _A         =							2




    @register_to_config
    def __init__( self    ,					lowercase__ = 1000    ,					lowercase__ = 0.00085    ,					lowercase__ = 0.012    ,					lowercase__ = "linear"    ,					lowercase__ = None    ,					lowercase__ = "epsilon"    ,					lowercase__ = "linspace"    ,					lowercase__ = 0    ,					):






          """simple docstring"""






          if trained_betas is not None:
                SCREAMING_SNAKE_CASE_				:      int					=	torch.tensor(lowercase__    ,					dtype=torch.floataa      )
          elif beta_schedule == "linear":
                SCREAMING_SNAKE_CASE_				:      Any					=	torch.linspace(lowercase__    ,					lowercase__    ,					lowercase__    ,					dtype=torch.floataa      )
          elif beta_schedule == "scaled_linear":
                # this schedule is very specific to the latent diffusion model.
                SCREAMING_SNAKE_CASE_				:      int					=	(
                    torch.linspace(beta_start**0.5    ,					beta_end**0.5    ,					lowercase__    ,					dtype=torch.floataa      ) ** 2
                )
          elif beta_schedule == "squaredcos_cap_v2":
                # Glide cosine schedule
                SCREAMING_SNAKE_CASE_				:      int					=	betas_for_alpha_bar(lowercase__      )
          else:
                raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"      )

          SCREAMING_SNAKE_CASE_				:      Tuple					=	1.0 - self.betas
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	torch.cumprod(self.alphas    ,					dim=0      )

          #  set all values
          self.set_timesteps(lowercase__    ,					lowercase__    ,					lowercase__      )




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__=None      ):






          """simple docstring"""






          if schedule_timesteps is None:
                SCREAMING_SNAKE_CASE_				:      Optional[int]					=	self.timesteps

          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	(schedule_timesteps == timestep).nonzero()

          # The sigma index that is taken for the **very** first `step`
          # is always the second index (or the last index if there is only 1)
          # This way we can ensure we don't accidentally skip a sigma in
          # case we start in the middle of the denoising schedule (e.g. for image-to-image)
          if len(self._index_counter      ) == 0:
                SCREAMING_SNAKE_CASE_				:      List[str]					=	1 if len(lowercase__      ) > 1 else 0
          else:
                SCREAMING_SNAKE_CASE_				:      List[Any]					=	timestep.cpu().item() if torch.is_tensor(lowercase__      ) else timestep
                SCREAMING_SNAKE_CASE_				:      Dict					=	self._index_counter[timestep_int]

          return indices[pos].item()




    @property
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          if self.config.timestep_spacing in ["linspace", "trailing"]:
                return self.sigmas.max()

          return (self.sigmas.max() ** 2 + 1) ** 0.5




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	self.index_for_timestep(lowercase__      )

          if self.state_in_first_order:
                SCREAMING_SNAKE_CASE_				:      List[str]					=	self.sigmas[step_index]
          else:
                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	self.sigmas_interpol[step_index - 1]

          SCREAMING_SNAKE_CASE_				:      str					=	sample / ((sigma**2 + 1) ** 0.5)
          return sample




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__ = None    ,					lowercase__ = None    ,					):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      List[str]					=	num_inference_steps

          SCREAMING_SNAKE_CASE_				:      str					=	num_train_timesteps or self.config.num_train_timesteps

          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	np.linspace(0    ,					num_train_timesteps - 1    ,					lowercase__    ,					dtype=lowercase__      )[::-1].copy()
          elif self.config.timestep_spacing == "leading":
                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	num_train_timesteps // self.num_inference_steps
                # creates integer timesteps by multiplying by ratio
                # casting to int to avoid issues when num_inference_step is power of 3
                SCREAMING_SNAKE_CASE_				:      List[str]					=	(np.arange(0    ,					lowercase__      ) * step_ratio).round()[::-1].copy().astype(lowercase__      )
                timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
                SCREAMING_SNAKE_CASE_				:      str					=	num_train_timesteps / self.num_inference_steps
                # creates integer timesteps by multiplying by ratio
                # casting to int to avoid issues when num_inference_step is power of 3
                SCREAMING_SNAKE_CASE_				:      int					=	(np.arange(lowercase__    ,					0    ,					-step_ratio      )).round().copy().astype(lowercase__      )
                timesteps -= 1
          else:
                raise ValueError(
                    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."      )

          SCREAMING_SNAKE_CASE_				:      Dict					=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
          SCREAMING_SNAKE_CASE_				:      List[str]					=	torch.from_numpy(np.log(lowercase__      )      ).to(lowercase__      )

          SCREAMING_SNAKE_CASE_				:      Any					=	np.interp(lowercase__    ,					np.arange(0    ,					len(lowercase__      )      )    ,					lowercase__      )
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
          SCREAMING_SNAKE_CASE_				:      Dict					=	torch.from_numpy(lowercase__      ).to(device=lowercase__      )

          # compute up and down sigmas
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	sigmas.roll(-1      )
          SCREAMING_SNAKE_CASE_				:      Any					=	0.0
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
          SCREAMING_SNAKE_CASE_				:      List[str]					=	(sigmas_next**2 - sigmas_up**2) ** 0.5
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	0.0

          # compute interpolated sigmas
          SCREAMING_SNAKE_CASE_				:      int					=	sigmas.log().lerp(sigmas_down.log()    ,					0.5      ).exp()
          SCREAMING_SNAKE_CASE_				:      Any					=	0.0

          # set sigmas
          SCREAMING_SNAKE_CASE_				:      Any					=	torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2      ), sigmas[-1:]]      )
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	torch.cat(
              [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2      ), sigmas_interpol[-1:]]      )
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2      ), sigmas_up[-1:]]      )
          SCREAMING_SNAKE_CASE_				:      Tuple					=	torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2      ), sigmas_down[-1:]]      )

          if str(lowercase__      ).startswith("mps"      ):
                # mps does not support float64
                SCREAMING_SNAKE_CASE_				:      Optional[int]					=	torch.from_numpy(lowercase__      ).to(lowercase__    ,					dtype=torch.floataa      )
          else:
                SCREAMING_SNAKE_CASE_				:      Optional[int]					=	torch.from_numpy(lowercase__      ).to(lowercase__      )

          SCREAMING_SNAKE_CASE_				:      List[str]					=	self.sigma_to_t(lowercase__      ).to(lowercase__    ,					dtype=timesteps.dtype      )
          SCREAMING_SNAKE_CASE_				:      Any					=	torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])    ,					dim=-1      ).flatten()

          SCREAMING_SNAKE_CASE_				:      Dict					=	torch.cat([timesteps[:1], interleaved_timesteps]      )

          SCREAMING_SNAKE_CASE_				:      List[str]					=	None

          # for exp beta schedules, such as the one for `pipeline_shap_e.py`
          # we need an index counter
          SCREAMING_SNAKE_CASE_				:      Dict					=	defaultdict(lowercase__      )




    def   __lowerCamelCase				( self    ,					lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      List[str]					=	sigma.log()

          # get distribution
          SCREAMING_SNAKE_CASE_				:      int					=	log_sigma - self.log_sigmas[:, None]

          # get sigmas range
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	dists.ge(0      ).cumsum(dim=0      ).argmax(dim=0      ).clamp(max=self.log_sigmas.shape[0] - 2      )
          SCREAMING_SNAKE_CASE_				:      int					=	low_idx + 1

          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	self.log_sigmas[low_idx]
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	self.log_sigmas[high_idx]

          # interpolate sigmas
          SCREAMING_SNAKE_CASE_				:      Any					=	(low - log_sigma) / (low - high)
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	w.clamp(0    ,					1      )

          # transform interpolation to time range
          SCREAMING_SNAKE_CASE_				:      Tuple					=	(1 - w) * low_idx + w * high_idx
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	t.view(sigma.shape      )
          return t




    @property
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          return self.sample is None




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__ = None    ,					lowercase__ = True    ,					):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      str					=	self.index_for_timestep(lowercase__      )

          # advance index counter by 1
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	timestep.cpu().item() if torch.is_tensor(lowercase__      ) else timestep
          self._index_counter[timestep_int] += 1

          if self.state_in_first_order:
                SCREAMING_SNAKE_CASE_				:      Any					=	self.sigmas[step_index]
                SCREAMING_SNAKE_CASE_				:      Dict					=	self.sigmas_interpol[step_index]
                SCREAMING_SNAKE_CASE_				:      List[str]					=	self.sigmas_up[step_index]
                SCREAMING_SNAKE_CASE_				:      List[Any]					=	self.sigmas_down[step_index - 1]
          else:
                # 2nd order / KPDM2's method
                SCREAMING_SNAKE_CASE_				:      List[str]					=	self.sigmas[step_index - 1]
                SCREAMING_SNAKE_CASE_				:      Tuple					=	self.sigmas_interpol[step_index - 1]
                SCREAMING_SNAKE_CASE_				:      Any					=	self.sigmas_up[step_index - 1]
                SCREAMING_SNAKE_CASE_				:      Dict					=	self.sigmas_down[step_index - 1]

          # currently only gamma=0 is supported. This usually works best anyways.
          # We can support gamma in the future but then need to scale the timestep before
          # passing it to the model which requires a change in API
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	0
          SCREAMING_SNAKE_CASE_				:      Tuple					=	sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

          SCREAMING_SNAKE_CASE_				:      str					=	model_output.device
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	randn_tensor(model_output.shape    ,					dtype=model_output.dtype    ,					device=lowercase__    ,					generator=lowercase__      )

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
                SCREAMING_SNAKE_CASE_				:      Dict					=	sigma_hat if self.state_in_first_order else sigma_interpol
                SCREAMING_SNAKE_CASE_				:      Any					=	sample - sigma_input * model_output
          elif self.config.prediction_type == "v_prediction":
                SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	sigma_hat if self.state_in_first_order else sigma_interpol
                SCREAMING_SNAKE_CASE_				:      int					=	model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                    sample / (sigma_input**2 + 1)
                )
          elif self.config.prediction_type == "sample":
                raise NotImplementedError("prediction_type not implemented yet: sample"      )
          else:
                raise ValueError(
                    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"      )

          if self.state_in_first_order:
                # 2. Convert to an ODE derivative for 1st order
                SCREAMING_SNAKE_CASE_				:      Optional[int]					=	(sample - pred_original_sample) / sigma_hat
                # 3. delta timestep
                SCREAMING_SNAKE_CASE_				:      int					=	sigma_interpol - sigma_hat

                # store for 2nd order step
                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	sample
                SCREAMING_SNAKE_CASE_				:      Optional[int]					=	dt
                SCREAMING_SNAKE_CASE_				:      Dict					=	sample + derivative * dt
          else:
                # DPM-Solver-2
                # 2. Convert to an ODE derivative for 2nd order
                SCREAMING_SNAKE_CASE_				:      Tuple					=	(sample - pred_original_sample) / sigma_interpol
                # 3. delta timestep
                SCREAMING_SNAKE_CASE_				:      str					=	sigma_down - sigma_hat

                SCREAMING_SNAKE_CASE_				:      List[Any]					=	self.sample
                SCREAMING_SNAKE_CASE_				:      List[Any]					=	None

                SCREAMING_SNAKE_CASE_				:      Any					=	sample + derivative * dt
                SCREAMING_SNAKE_CASE_				:      Dict					=	prev_sample + noise * sigma_up

          if not return_dict:
                return (prev_sample,)

          return SchedulerOutput(prev_sample=lowercase__      )




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					lowercase__    ,					):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Any					=	self.sigmas.to(device=original_samples.device    ,					dtype=original_samples.dtype      )
          if original_samples.device.type == "mps" and torch.is_floating_point(lowercase__      ):
                # mps does not support float64
                SCREAMING_SNAKE_CASE_				:      str					=	self.timesteps.to(original_samples.device    ,					dtype=torch.floataa      )
                SCREAMING_SNAKE_CASE_				:      Dict					=	timesteps.to(original_samples.device    ,					dtype=torch.floataa      )
          else:
                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	self.timesteps.to(original_samples.device      )
                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	timesteps.to(original_samples.device      )

          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	[self.index_for_timestep(lowercase__    ,					lowercase__      ) for t in timesteps]

          SCREAMING_SNAKE_CASE_				:      List[Any]					=	sigmas[step_indices].flatten()
          while len(sigma.shape      ) < len(original_samples.shape      ):
                SCREAMING_SNAKE_CASE_				:      List[str]					=	sigma.unsqueeze(-1      )

          SCREAMING_SNAKE_CASE_				:      Any					=	original_samples + noise * sigma
          return noisy_samples







    def __len__( self      ):






          """simple docstring"""






          return self.config.num_train_timesteps




'''simple docstring'''






import functools
import operator

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


snake_case_					  =	logging.get_logger(__name__)

snake_case_					  =	{
    'facebook/hubert-base-ls960': 'https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}
class      SCREAMING_SNAKE_CASE__      (    _UpperCAmelCase				):
    _A         =							"hubert"




    def __init__( self    ,					lowercase__=32    ,					lowercase__=768    ,					lowercase__=12    ,					lowercase__=12    ,					lowercase__=3072    ,					lowercase__="gelu"    ,					lowercase__=0.1    ,					lowercase__=0.1    ,					lowercase__=0.1    ,					lowercase__=True    ,					lowercase__=0.0    ,					lowercase__=0.1    ,					lowercase__=0.1    ,					lowercase__=0.02    ,					lowercase__=1e-5    ,					lowercase__="group"    ,					lowercase__="gelu"    ,					lowercase__=(512, 512, 512, 512, 512, 512, 512)    ,					lowercase__=(5, 2, 2, 2, 2, 2, 2)    ,					lowercase__=(10, 3, 3, 3, 3, 2, 2)    ,					lowercase__=False    ,					lowercase__=128    ,					lowercase__=16    ,					lowercase__=False    ,					lowercase__=True    ,					lowercase__=0.05    ,					lowercase__=10    ,					lowercase__=2    ,					lowercase__=0.0    ,					lowercase__=10    ,					lowercase__=0    ,					lowercase__="sum"    ,					lowercase__=False    ,					lowercase__=False    ,					lowercase__=256    ,					lowercase__=0    ,					lowercase__=1    ,					lowercase__=2    ,					**lowercase__    ,					):






          """simple docstring"""






          super().__init__(**lowercase__    ,					pad_token_id=lowercase__    ,					bos_token_id=lowercase__    ,					eos_token_id=lowercase__      )
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	hidden_size
          SCREAMING_SNAKE_CASE_				:      List[str]					=	feat_extract_norm
          SCREAMING_SNAKE_CASE_				:      Dict					=	feat_extract_activation
          SCREAMING_SNAKE_CASE_				:      str					=	list(lowercase__      )
          SCREAMING_SNAKE_CASE_				:      int					=	list(lowercase__      )
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	list(lowercase__      )
          SCREAMING_SNAKE_CASE_				:      List[str]					=	conv_bias
          SCREAMING_SNAKE_CASE_				:      str					=	num_conv_pos_embeddings
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	num_conv_pos_embedding_groups
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	len(self.conv_dim      )
          SCREAMING_SNAKE_CASE_				:      Any					=	num_hidden_layers
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	intermediate_size
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	hidden_act
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	num_attention_heads
          SCREAMING_SNAKE_CASE_				:      Any					=	hidden_dropout
          SCREAMING_SNAKE_CASE_				:      List[str]					=	attention_dropout
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	activation_dropout
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	feat_proj_layer_norm
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	feat_proj_dropout
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	final_dropout
          SCREAMING_SNAKE_CASE_				:      Dict					=	layerdrop
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	layer_norm_eps
          SCREAMING_SNAKE_CASE_				:      Tuple					=	initializer_range
          SCREAMING_SNAKE_CASE_				:      List[str]					=	vocab_size
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	do_stable_layer_norm
          SCREAMING_SNAKE_CASE_				:      List[str]					=	use_weighted_layer_sum
          SCREAMING_SNAKE_CASE_				:      Any					=	classifier_proj_size

          if (
              (len(self.conv_stride      ) != self.num_feat_extract_layers)
              or (len(self.conv_kernel      ) != self.num_feat_extract_layers)
              or (len(self.conv_dim      ) != self.num_feat_extract_layers)
          ):
                raise ValueError(
                    "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
                    " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
                    F" {len(self.conv_dim      )}`, `len(config.conv_stride) = {len(self.conv_stride      )}`,"
                    F" `len(config.conv_kernel) = {len(self.conv_kernel      )}`."      )

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          SCREAMING_SNAKE_CASE_				:      Tuple					=	apply_spec_augment
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	mask_time_prob
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	mask_time_length
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	mask_time_min_masks
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	mask_feature_prob
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	mask_feature_length
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	mask_feature_min_masks

          # ctc loss
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	ctc_loss_reduction
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	ctc_zero_infinity







    @property
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          return functools.reduce(operator.mul    ,					self.conv_stride    ,					1      )

