






'''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							=logging.getLogger(__name__)


class    _snake_case   (				UpperCamelCase_					):

					"""simple docstring"""





					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__=None   , UpperCAmelCase__=None     )      ->	Tuple:


										if current_layer == 0:
															a_									=   self.embedding_hidden_mapping_in(__UpperCamelCase     )
										else:
															a_									=   hidden_states[0]

										a_									=   int(self.config.num_hidden_layers / self.config.num_hidden_groups     )

										# Index of the hidden group
										a_									=   int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)     )

										a_									=   self.albert_layer_groups[group_idx](
										    __UpperCamelCase   , __UpperCamelCase   , head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]   , )
										a_									=   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."     ,					UpperCamelCase_     ,					)
class    _snake_case   (				UpperCamelCase_					):

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__     )      ->	Tuple:
										super().__init__(__UpperCamelCase     )

										a_									=   AlbertTransformerWithPabee(__UpperCamelCase     )

										self.init_weights()
										a_									=   0
										a_									=   0
										a_									=   0

										a_									=   0




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	Union[str, Any]:
										a_									=   threshold




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	Optional[int]:
										a_									=   patience




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Dict:
										a_									=   0
										a_									=   0




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	str:
										a_									=   self.inference_layers_num / self.inference_instances_num
										a_									=   (
										    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(__UpperCamelCase     )




					@add_start_docstrings_to_model_forward(__UpperCamelCase     )
					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__=None   , UpperCAmelCase__=None   , UpperCAmelCase__=None   , UpperCAmelCase__=None   , UpperCAmelCase__=None   , UpperCAmelCase__=None   , UpperCAmelCase__=None   , UpperCAmelCase__=None   , UpperCAmelCase__=False   , )      ->	Optional[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:
															a_									=   input_ids.size()
										elif inputs_embeds is not None:
															a_									=   inputs_embeds.size()[:-1]
										else:
															raise ValueError('You have to specify either input_ids or inputs_embeds'     )

										a_									=   input_ids.device if input_ids is not None else inputs_embeds.device

										if attention_mask is None:
															a_									=   torch.ones(__UpperCamelCase   , device=__UpperCamelCase     )
										if token_type_ids is None:
															a_									=   torch.zeros(__UpperCamelCase   , dtype=torch.long   , device=__UpperCamelCase     )

										a_									=   attention_mask.unsqueeze(1     ).unsqueeze(2     )
										a_									=   extended_attention_mask.to(dtype=self.dtype     )  # fp16 compatibility
										a_									=   (1.0 - extended_attention_mask) * -1_0000.0
										a_									=   self.get_head_mask(__UpperCamelCase   , self.config.num_hidden_layers     )

										a_									=   self.embeddings(
										    __UpperCamelCase   , position_ids=__UpperCamelCase   , token_type_ids=__UpperCamelCase   , inputs_embeds=__UpperCamelCase     )
										a_									=   embedding_output

										if self.training:
															a_									=   []
															for i in range(self.config.num_hidden_layers     ):
																				a_									=   self.encoder.adaptive_forward(
																				    __UpperCamelCase   , current_layer=__UpperCamelCase   , attention_mask=__UpperCamelCase   , head_mask=__UpperCamelCase   , )

																				a_									=   self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]     )     )
																				a_									=   output_layers[i](output_dropout(__UpperCamelCase     )     )
																				res.append(__UpperCamelCase     )
										elif self.patience == 0:  # Use all layers for inference
															a_									=   self.encoder(__UpperCamelCase   , __UpperCamelCase   , head_mask=__UpperCamelCase     )
															a_									=   self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]     )     )
															a_									=   [output_layers[self.config.num_hidden_layers - 1](__UpperCamelCase     )]
										else:
															a_									=   0
															a_									=   None
															a_									=   0
															for i in range(self.config.num_hidden_layers     ):
																				calculated_layer_num += 1
																				a_									=   self.encoder.adaptive_forward(
																				    __UpperCamelCase   , current_layer=__UpperCamelCase   , attention_mask=__UpperCamelCase   , head_mask=__UpperCamelCase   , )

																				a_									=   self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]     )     )
																				a_									=   output_layers[i](__UpperCamelCase     )
																				if regression:
																									a_									=   logits.detach()
																									if patient_result is not None:
																														a_									=   patient_result.detach()
																									if (patient_result is not None) and torch.abs(patient_result - labels     ) < self.regression_threshold:
																														patient_counter += 1
																									else:
																														a_									=   0
																				else:
																									a_									=   logits.detach().argmax(dim=1     )
																									if patient_result is not None:
																														a_									=   patient_result.detach().argmax(dim=1     )
																									if (patient_result is not None) and torch.all(labels.eq(__UpperCamelCase     )     ):
																														patient_counter += 1
																									else:
																														a_									=   0

																				a_									=   logits
																				if patient_counter == self.patience:
																									break
															a_									=   [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\n    the pooled output) e.g. for GLUE tasks. "     ,					UpperCamelCase_     ,					)
class    _snake_case   (				UpperCamelCase_					):

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__     )      ->	List[str]:
										super().__init__(__UpperCamelCase     )
										a_									=   config.num_labels

										a_									=   AlbertModelWithPabee(__UpperCamelCase     )
										a_									=   nn.Dropout(config.classifier_dropout_prob     )
										a_									=   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(__UpperCamelCase     )
					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__=None   , UpperCAmelCase__=None   , UpperCAmelCase__=None   , UpperCAmelCase__=None   , UpperCAmelCase__=None   , UpperCAmelCase__=None   , UpperCAmelCase__=None   , )      ->	int:
										a_									=   self.albert(
										    input_ids=__UpperCamelCase   , attention_mask=__UpperCamelCase   , token_type_ids=__UpperCamelCase   , position_ids=__UpperCamelCase   , head_mask=__UpperCamelCase   , inputs_embeds=__UpperCamelCase   , output_dropout=self.dropout   , output_layers=self.classifiers   , regression=self.num_labels == 1   , )

										a_									=   (logits[-1],)

										if labels is not None:
															a_									=   None
															a_									=   0
															for ix, logits_item in enumerate(__UpperCamelCase     ):
																				if self.num_labels == 1:
																									#  We are doing regression
																									a_									=   MSELoss()
																									a_									=   loss_fct(logits_item.view(-1     )   , labels.view(-1     )     )
																				else:
																									a_									=   CrossEntropyLoss()
																									a_									=   loss_fct(logits_item.view(-1   , self.num_labels     )   , labels.view(-1     )     )
																				if total_loss is None:
																									a_									=   loss
																				else:
																									total_loss += loss * (ix + 1)
																				total_weights += ix + 1
															a_									=   (total_loss / total_weights,) + outputs

										return outputs









'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class    _snake_case   (				snake_case					):

					"""simple docstring"""





					_UpperCamelCase		 =      42
					_UpperCamelCase		 =      42




					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	int:
										super().__init__()

										a_									=   PNDMScheduler.from_config(scheduler.config     )

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




					@torch.no_grad()
					def __call__(     self   , UpperCAmelCase__ = 1   , UpperCAmelCase__ = 50   , UpperCAmelCase__ = None   , UpperCAmelCase__ = "pil"   , UpperCAmelCase__ = True   , **UpperCAmelCase__   , )      ->	Union[ImagePipelineOutput, Tuple]:
										a_									=   randn_tensor(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   , generator=UpperCAmelCase__   , device=self.device   , )

										self.scheduler.set_timesteps(UpperCAmelCase__     )
										for t in self.progress_bar(self.scheduler.timesteps     ):
															a_									=   self.unet(UpperCAmelCase__   , UpperCAmelCase__     ).sample

															a_									=   self.scheduler.step(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     ).prev_sample

										a_									=   (image / 2 + 0.5).clamp(0   , 1     )
										a_									=   image.cpu().permute(0   , 2   , 3   , 1     ).numpy()
										if output_type == "pil":
															a_									=   self.numpy_to_pil(UpperCAmelCase__     )

										if not return_dict:
															return (image,)

										return ImagePipelineOutput(images=UpperCAmelCase__     )

