
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,
)


snake_case__  							=							logging.getLogger(__name__)







class 							UpperCAmelCase	(   __lowerCamelCase					):

					def        _lowerCAmelCase	(			self      :					Union[str, Any] ,      lowerCAmelCase      :					Optional[int] ,      lowerCAmelCase      :					Union[str, Any] ,      lowerCAmelCase      :					Optional[Any]=None ,      lowerCAmelCase      :					str=None					):
											if current_layer == 0:
																	lowercase       :    Union[str, Any]											=  self.embedding_hidden_mapping_in(lowerCAmelCase					)
											else:
																	lowercase       :    int											=  hidden_states[0]

											lowercase       :    Any											=  int(self.config.num_hidden_layers / self.config.num_hidden_groups					)

											# Index of the hidden group
											lowercase       :    Optional[Any]											=  int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)					)

											lowercase       :    Union[str, Any]											=  self.albert_layer_groups[group_idx](
											    lowerCAmelCase ,      lowerCAmelCase ,      head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group] ,      )
											lowercase       :    Union[str, Any]											=  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."""						, __lowerCamelCase						, )
class 							UpperCAmelCase	(   __lowerCamelCase					):

					def __init__(			self      :					int ,      lowerCAmelCase      :					Optional[int]					):
											super().__init__(lowerCAmelCase					)

											lowercase       :    Any											=  AlbertTransformerWithPabee(lowerCAmelCase					)

											self.init_weights()
											lowercase       :    List[str]											=  0
											lowercase       :    List[Any]											=  0
											lowercase       :    Dict											=  0

											lowercase       :    Optional[int]											=  0

					def        _lowerCAmelCase	(			self      :					int ,      lowerCAmelCase      :					List[Any]					):
											lowercase       :    str											=  threshold

					def        _lowerCAmelCase	(			self      :					Optional[int] ,      lowerCAmelCase      :					Dict					):
											lowercase       :    List[str]											=  patience

					def        _lowerCAmelCase	(			self      :					Optional[Any]					):
											lowercase       :    List[Any]											=  0
											lowercase       :    List[Any]											=  0

					def        _lowerCAmelCase	(			self      :					Dict					):
											lowercase       :    Any											=  self.inference_layers_num / self.inference_instances_num
											lowercase       :    Optional[Any]											=  (
											    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(lowerCAmelCase					)




					@add_start_docstrings_to_model_forward(lowerCAmelCase					)
					def        _lowerCAmelCase	(			self      :					Optional[int] ,      lowerCAmelCase      :					Tuple=None ,      lowerCAmelCase      :					str=None ,      lowerCAmelCase      :					Dict=None ,      lowerCAmelCase      :					int=None ,      lowerCAmelCase      :					str=None ,      lowerCAmelCase      :					Union[str, Any]=None ,      lowerCAmelCase      :					int=None ,      lowerCAmelCase      :					List[Any]=None ,      lowerCAmelCase      :					int=False ,      ):

											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:
																	lowercase       :    int											=  input_ids.size()
											elif inputs_embeds is not None:
																	lowercase       :    List[Any]											=  inputs_embeds.size()[:-1]
											else:
																	raise ValueError('''You have to specify either input_ids or inputs_embeds'''					)

											lowercase       :    str											=  input_ids.device if input_ids is not None else inputs_embeds.device

											if attention_mask is None:
																	lowercase       :    Tuple											=  torch.ones(lowerCAmelCase ,      device=lowerCAmelCase					)
											if token_type_ids is None:
																	lowercase       :    Optional[Any]											=  torch.zeros(lowerCAmelCase ,      dtype=torch.long ,      device=lowerCAmelCase					)

											lowercase       :    Union[str, Any]											=  attention_mask.unsqueeze(1					).unsqueeze(2					)
											lowercase       :    List[str]											=  extended_attention_mask.to(dtype=self.dtype					)  # fp16 compatibility
											lowercase       :    List[str]											=  (1.0 - extended_attention_mask) * -1_00_00.0
											lowercase       :    Tuple											=  self.get_head_mask(lowerCAmelCase ,      self.config.num_hidden_layers					)

											lowercase       :    Dict											=  self.embeddings(
											    lowerCAmelCase ,      position_ids=lowerCAmelCase ,      token_type_ids=lowerCAmelCase ,      inputs_embeds=lowerCAmelCase					)
											lowercase       :    List[str]											=  embedding_output

											if self.training:
																	lowercase       :    List[Any]											=  []
																	for i in range(self.config.num_hidden_layers					):
																							lowercase       :    Optional[int]											=  self.encoder.adaptive_forward(
																							    lowerCAmelCase ,      current_layer=lowerCAmelCase ,      attention_mask=lowerCAmelCase ,      head_mask=lowerCAmelCase ,      )

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

																							lowercase       :    List[Any]											=  self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]					)					)
																							lowercase       :    str											=  output_layers[i](lowerCAmelCase					)
																							if regression:
																													lowercase       :    Union[str, Any]											=  logits.detach()
																													if patient_result is not None:
																																			lowercase       :    Dict											=  patient_result.detach()
																													if (patient_result is not None) and torch.abs(patient_result - labels					) < self.regression_threshold:
																																			patient_counter += 1
																													else:
																																			lowercase       :    int											=  0
																							else:
																													lowercase       :    List[Any]											=  logits.detach().argmax(dim=1					)
																													if patient_result is not None:
																																			lowercase       :    Dict											=  patient_result.detach().argmax(dim=1					)
																													if (patient_result is not None) and torch.all(labels.eq(lowerCAmelCase					)					):
																																			patient_counter += 1
																													else:
																																			lowercase       :    List[Any]											=  0

																							lowercase       :    int											=  logits
																							if patient_counter == self.patience:
																													break
																	lowercase       :    Optional[int]											=  [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. """						, __lowerCamelCase						, )
class 							UpperCAmelCase	(   __lowerCamelCase					):

					def __init__(			self      :					Tuple ,      lowerCAmelCase      :					Union[str, Any]					):
											super().__init__(lowerCAmelCase					)
											lowercase       :    Tuple											=  config.num_labels

											lowercase       :    List[Any]											=  AlbertModelWithPabee(lowerCAmelCase					)
											lowercase       :    Tuple											=  nn.Dropout(config.classifier_dropout_prob					)
											lowercase       :    Union[str, 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(lowerCAmelCase					)
					def        _lowerCAmelCase	(			self      :					str ,      lowerCAmelCase      :					List[str]=None ,      lowerCAmelCase      :					Tuple=None ,      lowerCAmelCase      :					Optional[Any]=None ,      lowerCAmelCase      :					Dict=None ,      lowerCAmelCase      :					int=None ,      lowerCAmelCase      :					Optional[Any]=None ,      lowerCAmelCase      :					str=None ,      ):
											lowercase       :    Optional[int]											=  self.albert(
											    input_ids=lowerCAmelCase ,      attention_mask=lowerCAmelCase ,      token_type_ids=lowerCAmelCase ,      position_ids=lowerCAmelCase ,      head_mask=lowerCAmelCase ,      inputs_embeds=lowerCAmelCase ,      output_dropout=self.dropout ,      output_layers=self.classifiers ,      regression=self.num_labels == 1 ,      )

											lowercase       :    str											=  (logits[-1],)

											if labels is not None:
																	lowercase       :    Optional[int]											=  None
																	lowercase       :    Dict											=  0
																	for ix, logits_item in enumerate(lowerCAmelCase					):
																							if self.num_labels == 1:
																													#  We are doing regression
																													lowercase       :    int											=  MSELoss()
																													lowercase       :    List[Any]											=  loss_fct(logits_item.view(-1					) ,      labels.view(-1					)					)
																							else:
																													lowercase       :    Optional[int]											=  CrossEntropyLoss()
																													lowercase       :    List[Any]											=  loss_fct(logits_item.view(-1 ,      self.num_labels					) ,      labels.view(-1					)					)
																							if total_loss is None:
																													lowercase       :    Dict											=  loss
																							else:
																													total_loss += loss * (ix + 1)
																							total_weights += ix + 1
																	lowercase       :    List[str]											=  (total_loss / total_weights,) + outputs

											return outputs




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 							UpperCAmelCase	(   __lowerCamelCase					):
					a__: UNetaDModel
					a__: PNDMScheduler

					def __init__(			self      :					List[str] ,      lowerCAmelCase      :					UNetaDModel ,      lowerCAmelCase      :					PNDMScheduler					):
											super().__init__()

											lowercase       :    int											=  PNDMScheduler.from_config(scheduler.config					)

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




					@torch.no_grad()
					def __call__(			self      :					List[str] ,      lowerCAmelCase      :					int = 1 ,      lowerCAmelCase      :					int = 50 ,      lowerCAmelCase      :					Optional[Union[torch.Generator, List[torch.Generator]]] = None ,      lowerCAmelCase      :					Optional[str] = "pil" ,      lowerCAmelCase      :					bool = True ,      **lowerCAmelCase      :					int ,      ):
											lowercase       :    Tuple											=  randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,      generator=lowerCAmelCase ,      device=self.device ,      )

											self.scheduler.set_timesteps(lowerCAmelCase					)
											for t in self.progress_bar(self.scheduler.timesteps					):
																	lowercase       :    Dict											=  self.unet(lowerCAmelCase ,      lowerCAmelCase					).sample

																	lowercase       :    Tuple											=  self.scheduler.step(lowerCAmelCase ,      lowerCAmelCase ,      lowerCAmelCase					).prev_sample

											lowercase       :    Optional[Any]											=  (image / 2 + 0.5).clamp(0 ,      1					)
											lowercase       :    Optional[int]											=  image.cpu().permute(0 ,      2 ,      3 ,      1					).numpy()
											if output_type == "pil":
																	lowercase       :    Union[str, Any]											=  self.numpy_to_pil(lowerCAmelCase					)

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=lowerCAmelCase					)


