code: 
  
  
  
  """simple docstring"""
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, logging, randn_tensor
  from .scheduling_utils import SchedulerMixin
  
  
  _lowerCAmelCase					:Optional[int]  =      logging.get_logger(__name__)  # pylint: disable=invalid-name
  @dataclass
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =4_2
  
  
  
  class 							_UpperCAmelCase							(				a	,a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =1
  
  
  
  
  
       @register_to_config
       def __init__(      self ,			A = 4_0 ,			A = 0.002 ,			A = 80.0 ,			A = 0.5 ,			A = 1.0 ,			A = 7.0 ,			A = True ,			)							->      Dict:
           # standard deviation of the initial noise distribution
           _UpperCAmelCase							:						str       							=					sigma_max
  
           _UpperCAmelCase							:						Any       							=					np.linspace(0 ,			1 ,			A	)
           _UpperCAmelCase							:						Dict       							=					self._convert_to_karras(A	)
           _UpperCAmelCase							:						Dict       							=					self.sigma_to_t(A	)
  
           # setable values
           _UpperCAmelCase							:						Dict       							=					None
           _UpperCAmelCase							:						Optional[Any]       							=					torch.from_numpy(A	)
           _UpperCAmelCase							:						Any       							=					torch.from_numpy(A	)
           _UpperCAmelCase							:						Union[str, Any]       							=					False
           _UpperCAmelCase							:						int       							=					False
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A=None	)							->      Optional[int]:
           if schedule_timesteps is None:
               _UpperCAmelCase							:						Optional[Any]       							=					self.timesteps
  
           _UpperCAmelCase							:						Optional[int]       							=					(schedule_timesteps == timestep).nonzero()
           return indices.item()
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A	)							->      torch.FloatTensor:
           # Get sigma corresponding to timestep
           if isinstance(A ,			torch.Tensor	):
               _UpperCAmelCase							:						Any       							=					timestep.to(self.timesteps.device	)
           _UpperCAmelCase							:						str       							=					self.index_for_timestep(A	)
           _UpperCAmelCase							:						List[str]       							=					self.sigmas[step_idx]
  
           _UpperCAmelCase							:						Optional[Any]       							=					sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)
  
           _UpperCAmelCase							:						Dict       							=					True
           return sample
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Tuple:
           if not isinstance(A ,			np.ndarray	):
               _UpperCAmelCase							:						str       							=					np.array(A ,			dtype=np.floataa	)
  
           _UpperCAmelCase							:						Tuple       							=					1_0_0_0 * 0.25 * np.log(sigmas + 1E-44	)
  
           return timesteps
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A = None ,			A = None ,			A = None ,			)							->      int:
           if num_inference_steps is None and timesteps is None:
               raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''	)
  
           if num_inference_steps is not None and timesteps is not None:
               raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''	)
  
           # Follow DDPMScheduler custom timesteps logic
           if timesteps is not None:
               for i in range(1 ,			len(A	)	):
                   if timesteps[i] >= timesteps[i - 1]:
                       raise ValueError('''`timesteps` must be in descending order.'''	)
  
               if timesteps[0] >= self.config.num_train_timesteps:
                   raise ValueError(
                       f'`timesteps` must start before `self.config.train_timesteps`:'
                       f' {self.config.num_train_timesteps}.'	)
  
               _UpperCAmelCase							:						Optional[int]       							=					np.array(A ,			dtype=np.intaa	)
               _UpperCAmelCase							:						Optional[int]       							=					True
           else:
               if num_inference_steps > self.config.num_train_timesteps:
                   raise ValueError(
                       f'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
                       f' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
                       f' maximal {self.config.num_train_timesteps} timesteps.'	)
  
               _UpperCAmelCase							:						Tuple       							=					num_inference_steps
  
               _UpperCAmelCase							:						Optional[Any]       							=					self.config.num_train_timesteps // self.num_inference_steps
               _UpperCAmelCase							:						Optional[Any]       							=					(np.arange(0 ,			A	) * step_ratio).round()[::-1].copy().astype(np.intaa	)
               _UpperCAmelCase							:						Dict       							=					False
  
           # Map timesteps to Karras sigmas directly for multistep sampling
           # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
           _UpperCAmelCase							:						Optional[int]       							=					self.config.num_train_timesteps
           _UpperCAmelCase							:						Optional[int]       							=					timesteps[::-1].copy()
           _UpperCAmelCase							:						str       							=					ramp / (num_train_timesteps - 1)
           _UpperCAmelCase							:						Union[str, Any]       							=					self._convert_to_karras(A	)
           _UpperCAmelCase							:						List[str]       							=					self.sigma_to_t(A	)
  
           _UpperCAmelCase							:						int       							=					np.concatenate([sigmas, [self.sigma_min]]	).astype(np.floataa	)
           _UpperCAmelCase							:						Union[str, Any]       							=					torch.from_numpy(A	).to(device=A	)
  
           if str(A	).startswith('''mps'''	):
               # mps does not support float64
               _UpperCAmelCase							:						str       							=					torch.from_numpy(A	).to(A ,			dtype=torch.floataa	)
           else:
               _UpperCAmelCase							:						Dict       							=					torch.from_numpy(A	).to(device=A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[Any]:
           _UpperCAmelCase							:						float       							=					self.config.sigma_min
           _UpperCAmelCase							:						float       							=					self.config.sigma_max
  
           _UpperCAmelCase							:						int       							=					self.config.rho
           _UpperCAmelCase							:						List[Any]       							=					sigma_min ** (1 / rho)
           _UpperCAmelCase							:						List[str]       							=					sigma_max ** (1 / rho)
           _UpperCAmelCase							:						List[Any]       							=					(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Any:
           _UpperCAmelCase							:						Tuple       							=					self.config.sigma_data
  
           _UpperCAmelCase							:						List[Any]       							=					sigma_data**2 / (sigma**2 + sigma_data**2)
           _UpperCAmelCase							:						Optional[int]       							=					sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
           return c_skip, c_out
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      List[Any]:
           _UpperCAmelCase							:						Tuple       							=					self.config.sigma_min
           _UpperCAmelCase							:						List[Any]       							=					self.config.sigma_data
  
           _UpperCAmelCase							:						Union[str, Any]       							=					sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
           _UpperCAmelCase							:						str       							=					(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
           return c_skip, c_out
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A ,			A ,			A = None ,			A = True ,			)							->      Union[CMStochasticIterativeSchedulerOutput, Tuple]:
  
           if (
               isinstance(A ,			A	)
               or isinstance(A ,			torch.IntTensor	)
               or isinstance(A ,			torch.LongTensor	)
           ):
               raise ValueError(
                   (
                       '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
                       f' `{self.__class__}.step()` is not supported. Make sure to pass'
                       ''' one of the `scheduler.timesteps` as a timestep.'''
                   ) ,			)
  
           if not self.is_scale_input_called:
               logger.warning(
                   '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
                   '''See `StableDiffusionPipeline` for a usage example.'''	)
  
           if isinstance(A ,			torch.Tensor	):
               _UpperCAmelCase							:						Tuple       							=					timestep.to(self.timesteps.device	)
  
           _UpperCAmelCase							:						Dict       							=					self.config.sigma_min
           _UpperCAmelCase							:						Union[str, Any]       							=					self.config.sigma_max
  
           _UpperCAmelCase							:						str       							=					self.index_for_timestep(A	)
  
           # sigma_next corresponds to next_t in original implementation
           _UpperCAmelCase							:						List[Any]       							=					self.sigmas[step_index]
           if step_index + 1 < self.config.num_train_timesteps:
               _UpperCAmelCase							:						Tuple       							=					self.sigmas[step_index + 1]
           else:
               # Set sigma_next to sigma_min
               _UpperCAmelCase							:						Dict       							=					self.sigmas[-1]
  
           # Get scalings for boundary conditions
           _UpperCAmelCase							:						List[Any]       							=					self.get_scalings_for_boundary_condition(A	)
  
           # 1. Denoise model output using boundary conditions
           _UpperCAmelCase							:						Optional[int]       							=					c_out * model_output + c_skip * sample
           if self.config.clip_denoised:
               _UpperCAmelCase							:						Optional[int]       							=					denoised.clamp(-1 ,			1	)
  
           # 2. Sample z ~ N(0, s_noise^2 * I)
           # Noise is not used for onestep sampling.
           if len(self.timesteps	) > 1:
               _UpperCAmelCase							:						Union[str, Any]       							=					randn_tensor(
                   model_output.shape ,			dtype=model_output.dtype ,			device=model_output.device ,			generator=A	)
           else:
               _UpperCAmelCase							:						Dict       							=					torch.zeros_like(A	)
           _UpperCAmelCase							:						Union[str, Any]       							=					noise * self.config.s_noise
  
           _UpperCAmelCase							:						Dict       							=					sigma_next.clamp(min=A ,			max=A	)
  
           # 3. Return noisy sample
           # tau = sigma_hat, eps = sigma_min
           _UpperCAmelCase							:						Union[str, Any]       							=					denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5
  
           if not return_dict:
               return (prev_sample,)
  
           return CMStochasticIterativeSchedulerOutput(prev_sample=A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A ,			A ,			)							->      torch.FloatTensor:
           # Make sure sigmas and timesteps have the same device and dtype as original_samples
           _UpperCAmelCase							:						str       							=					self.sigmas.to(device=original_samples.device ,			dtype=original_samples.dtype	)
           if original_samples.device.type == "mps" and torch.is_floating_point(A	):
               # mps does not support float64
               _UpperCAmelCase							:						Any       							=					self.timesteps.to(original_samples.device ,			dtype=torch.floataa	)
               _UpperCAmelCase							:						Dict       							=					timesteps.to(original_samples.device ,			dtype=torch.floataa	)
           else:
               _UpperCAmelCase							:						Any       							=					self.timesteps.to(original_samples.device	)
               _UpperCAmelCase							:						Optional[int]       							=					timesteps.to(original_samples.device	)
  
           _UpperCAmelCase							:						Optional[Any]       							=					[(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
           _UpperCAmelCase							:						str       							=					sigmas[step_indices].flatten()
           while len(sigma.shape	) < len(original_samples.shape	):
               _UpperCAmelCase							:						int       							=					sigma.unsqueeze(-1	)
  
           _UpperCAmelCase							:						Optional[int]       							=					original_samples + noise * sigma
           return noisy_samples
  
  
  
  
  
       def __len__(      self	)							->      Optional[Any]:
           return self.config.num_train_timesteps
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  import logging
  
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  
  from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
  from transformers.models.albert.modeling_albert import (
      ALBERT_INPUTS_DOCSTRING,
      ALBERT_START_DOCSTRING,
      AlbertModel,
      AlbertPreTrainedModel,
      AlbertTransformer,
  )
  
  
  _lowerCAmelCase					:Union[str, Any]  =      logging.getLogger(__name__)
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A ,			A=None ,			A=None	)							->      Tuple:
           if current_layer == 0:
               _UpperCAmelCase							:						Optional[Any]       							=					self.embedding_hidden_mapping_in(A	)
           else:
               _UpperCAmelCase							:						Union[str, Any]       							=					hidden_states[0]
  
           _UpperCAmelCase							:						Dict       							=					int(self.config.num_hidden_layers / self.config.num_hidden_groups	)
  
           # Index of the hidden group
           _UpperCAmelCase							:						Dict       							=					int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)	)
  
           _UpperCAmelCase							:						List[Any]       							=					self.albert_layer_groups[group_idx](
               A ,			A ,			head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group] ,			)
           _UpperCAmelCase							:						str       							=					layer_group_output[0]
  
           return (hidden_states,)
  
  
  
  @add_start_docstrings(
      '''The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top.'''	,a	,)
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A	)							->      str:
           super().__init__(A	)
  
           _UpperCAmelCase							:						Dict       							=					AlbertTransformerWithPabee(A	)
  
           self.init_weights()
           _UpperCAmelCase							:						str       							=					0
           _UpperCAmelCase							:						Union[str, Any]       							=					0
           _UpperCAmelCase							:						Dict       							=					0
  
           _UpperCAmelCase							:						str       							=					0
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[int]:
           _UpperCAmelCase							:						List[Any]       							=					threshold
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[Any]:
           _UpperCAmelCase							:						int       							=					patience
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      Any:
           _UpperCAmelCase							:						Dict       							=					0
           _UpperCAmelCase							:						List[str]       							=					0
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      List[Any]:
           _UpperCAmelCase							:						Union[str, Any]       							=					self.inference_layers_num / self.inference_instances_num
           _UpperCAmelCase							:						str       							=					(
               f'*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='
               f' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'
           )
           print(A	)
  
  
  
  
  
       @add_start_docstrings_to_model_forward(A	)
       def 				__lowerCAmelCase			(      self ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=False ,			)							->      int:
  
           if input_ids is not None and inputs_embeds is not None:
               raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''	)
           elif input_ids is not None:
               _UpperCAmelCase							:						Optional[Any]       							=					input_ids.size()
           elif inputs_embeds is not None:
               _UpperCAmelCase							:						Optional[Any]       							=					inputs_embeds.size()[:-1]
           else:
               raise ValueError('''You have to specify either input_ids or inputs_embeds'''	)
  
           _UpperCAmelCase							:						Tuple       							=					input_ids.device if input_ids is not None else inputs_embeds.device
  
           if attention_mask is None:
               _UpperCAmelCase							:						Union[str, Any]       							=					torch.ones(A ,			device=A	)
           if token_type_ids is None:
               _UpperCAmelCase							:						Tuple       							=					torch.zeros(A ,			dtype=torch.long ,			device=A	)
  
           _UpperCAmelCase							:						Optional[Any]       							=					attention_mask.unsqueeze(1	).unsqueeze(2	)
           _UpperCAmelCase							:						Union[str, Any]       							=					extended_attention_mask.to(dtype=self.dtype	)  # fp16 compatibility
           _UpperCAmelCase							:						Dict       							=					(1.0 - extended_attention_mask) * -10_000.0
           _UpperCAmelCase							:						Any       							=					self.get_head_mask(A ,			self.config.num_hidden_layers	)
  
           _UpperCAmelCase							:						int       							=					self.embeddings(
               A ,			position_ids=A ,			token_type_ids=A ,			inputs_embeds=A	)
           _UpperCAmelCase							:						Any       							=					embedding_output
  
           if self.training:
               _UpperCAmelCase							:						Optional[Any]       							=					[]
               for i in range(self.config.num_hidden_layers	):
                   _UpperCAmelCase							:						Union[str, Any]       							=					self.encoder.adaptive_forward(
                       A ,			current_layer=A ,			attention_mask=A ,			head_mask=A ,			)
  
                   _UpperCAmelCase							:						List[str]       							=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]	)	)
                   _UpperCAmelCase							:						Dict       							=					output_layers[i](output_dropout(A	)	)
                   res.append(A	)
           elif self.patience == 0:  # Use all layers for inference
               _UpperCAmelCase							:						Dict       							=					self.encoder(A ,			A ,			head_mask=A	)
               _UpperCAmelCase							:						Tuple       							=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]	)	)
               _UpperCAmelCase							:						Optional[int]       							=					[output_layers[self.config.num_hidden_layers - 1](A	)]
           else:
               _UpperCAmelCase							:						List[Any]       							=					0
               _UpperCAmelCase							:						int       							=					None
               _UpperCAmelCase							:						int       							=					0
               for i in range(self.config.num_hidden_layers	):
                   calculated_layer_num += 1
                   _UpperCAmelCase							:						Tuple       							=					self.encoder.adaptive_forward(
                       A ,			current_layer=A ,			attention_mask=A ,			head_mask=A ,			)
  
                   _UpperCAmelCase							:						Any       							=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]	)	)
                   _UpperCAmelCase							:						Optional[Any]       							=					output_layers[i](A	)
                   if regression:
                       _UpperCAmelCase							:						str       							=					logits.detach()
                       if patient_result is not None:
                           _UpperCAmelCase							:						str       							=					patient_result.detach()
                       if (patient_result is not None) and torch.abs(patient_result - labels	) < self.regression_threshold:
                           patient_counter += 1
                       else:
                           _UpperCAmelCase							:						List[Any]       							=					0
                   else:
                       _UpperCAmelCase							:						List[str]       							=					logits.detach().argmax(dim=1	)
                       if patient_result is not None:
                           _UpperCAmelCase							:						Optional[Any]       							=					patient_result.detach().argmax(dim=1	)
                       if (patient_result is not None) and torch.all(labels.eq(A	)	):
                           patient_counter += 1
                       else:
                           _UpperCAmelCase							:						Tuple       							=					0
  
                   _UpperCAmelCase							:						Any       							=					logits
                   if patient_counter == self.patience:
                       break
               _UpperCAmelCase							:						List[str]       							=					[patient_result]
               self.inference_layers_num += calculated_layer_num
               self.inference_instances_num += 1
  
           return res
  
  
  
  @add_start_docstrings(
      '''Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
      the pooled output) e.g. for GLUE tasks. '''	,a	,)
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A	)							->      Optional[int]:
           super().__init__(A	)
           _UpperCAmelCase							:						List[Any]       							=					config.num_labels
  
           _UpperCAmelCase							:						Optional[int]       							=					AlbertModelWithPabee(A	)
           _UpperCAmelCase							:						List[str]       							=					nn.Dropout(config.classifier_dropout_prob	)
           _UpperCAmelCase							:						Optional[Any]       							=					nn.ModuleList(
               [nn.Linear(config.hidden_size ,			self.config.num_labels	) for _ in range(config.num_hidden_layers	)]	)
  
           self.init_weights()
  
  
  
  
  
       @add_start_docstrings_to_model_forward(A	)
       def 				__lowerCAmelCase			(      self ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			)							->      List[str]:
           _UpperCAmelCase							:						Optional[Any]       							=					self.albert(
               input_ids=A ,			attention_mask=A ,			token_type_ids=A ,			position_ids=A ,			head_mask=A ,			inputs_embeds=A ,			output_dropout=self.dropout ,			output_layers=self.classifiers ,			regression=self.num_labels == 1 ,			)
  
           _UpperCAmelCase							:						int       							=					(logits[-1],)
  
           if labels is not None:
               _UpperCAmelCase							:						int       							=					None
               _UpperCAmelCase							:						Tuple       							=					0
               for ix, logits_item in enumerate(A	):
                   if self.num_labels == 1:
                       #  We are doing regression
                       _UpperCAmelCase							:						Dict       							=					MSELoss()
                       _UpperCAmelCase							:						Optional[int]       							=					loss_fct(logits_item.view(-1	) ,			labels.view(-1	)	)
                   else:
                       _UpperCAmelCase							:						List[str]       							=					CrossEntropyLoss()
                       _UpperCAmelCase							:						Any       							=					loss_fct(logits_item.view(-1 ,			self.num_labels	) ,			labels.view(-1	)	)
                   if total_loss is None:
                       _UpperCAmelCase							:						Optional[Any]       							=					loss
                   else:
                       total_loss += loss * (ix + 1)
                   total_weights += ix + 1
               _UpperCAmelCase							:						Any       							=					(total_loss / total_weights,) + outputs
  
           return outputs
  
  
  