


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.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


lowerCAmelCase			=				logging.getLogger(__name__)


class 			_a				(     UpperCamelCase__     ):

							def  lowerCamelCase_					(				self:      str       ,		UpperCamelCase_:      str       ,		UpperCamelCase_:      Tuple       ,		UpperCamelCase_:      Optional[Any]=None       ,		UpperCamelCase_:      Dict=None				)     ->  Union[str, Any]:





									"""simple docstring"""







									lowercase__				= self.layer[current_layer](UpperCamelCase_       ,		UpperCamelCase_       ,		head_mask[current_layer]				)

									lowercase__				= layer_outputs[0]

									return hidden_states


@add_start_docstrings(
    '''The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.'''	,      UpperCamelCase__	,      )
class 			_a				(     UpperCamelCase__     ):

							def __init__(				self:      List[Any]       ,		UpperCamelCase_:      Union[str, Any]				)     ->  Tuple:





									"""simple docstring"""







									super().__init__(UpperCamelCase_				)

									lowercase__				= BertEncoderWithPabee(UpperCamelCase_				)

									self.init_weights()
									lowercase__				= 0
									lowercase__				= 0
									lowercase__				= 0

									lowercase__				= 0

							def  lowerCamelCase_					(				self:      Optional[int]       ,		UpperCamelCase_:      str				)     ->  Tuple:





									"""simple docstring"""







									lowercase__				= threshold

							def  lowerCamelCase_					(				self:      int       ,		UpperCamelCase_:      Dict				)     ->  Any:





									"""simple docstring"""







									lowercase__				= patience

							def  lowerCamelCase_					(				self:      Optional[int]				)     ->  Tuple:





									"""simple docstring"""







									lowercase__				= 0
									lowercase__				= 0

							def  lowerCamelCase_					(				self:      List[Any]				)     ->  int:





									"""simple docstring"""







									lowercase__				= self.inference_layers_num / self.inference_instances_num
									lowercase__				= (
									    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  lowerCamelCase_					(				self:      List[str]       ,		UpperCamelCase_:      List[Any]=None       ,		UpperCamelCase_:      Union[str, Any]=None       ,		UpperCamelCase_:      str=None       ,		UpperCamelCase_:      List[str]=None       ,		UpperCamelCase_:      str=None       ,		UpperCamelCase_:      Optional[Any]=None       ,		UpperCamelCase_:      Tuple=None       ,		UpperCamelCase_:      List[str]=None       ,		UpperCamelCase_:      Union[str, Any]=None       ,		UpperCamelCase_:      str=None       ,		UpperCamelCase_:      str=False       ,		)     ->  List[Any]:





									"""simple docstring"""







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

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

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

									# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
									# ourselves in which case we just need to make it broadcastable to all heads.
									lowercase__				= self.get_extended_attention_mask(UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_				)

									# If a 2D ou 3D attention mask is provided for the cross-attention
									# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
									if self.config.is_decoder and encoder_hidden_states is not None:
											lowercase__	,						lowercase__	,						lowercase__				= encoder_hidden_states.size()
											lowercase__				= (encoder_batch_size, encoder_sequence_length)
											if encoder_attention_mask is None:
													lowercase__				= torch.ones(UpperCamelCase_       ,		device=UpperCamelCase_				)
											lowercase__				= self.invert_attention_mask(UpperCamelCase_				)
									else:
											lowercase__				= None

									# Prepare head mask if needed
									# 1.0 in head_mask indicate we keep the head
									# attention_probs has shape bsz x n_heads x N x N
									# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
									# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
									lowercase__				= self.get_head_mask(UpperCamelCase_       ,		self.config.num_hidden_layers				)

									lowercase__				= self.embeddings(
									    input_ids=UpperCamelCase_       ,		position_ids=UpperCamelCase_       ,		token_type_ids=UpperCamelCase_       ,		inputs_embeds=UpperCamelCase_				)
									lowercase__				= embedding_output

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

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

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

													lowercase__				= logits
													if patient_counter == self.patience:
															break
											lowercase__				= [patient_result]
											self.inference_layers_num += calculated_layer_num
											self.inference_instances_num += 1

									return res


@add_start_docstrings(
    '''Bert 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. '''	,      UpperCamelCase__	,      )
class 			_a				(     UpperCamelCase__     ):

							def __init__(				self:      Optional[int]       ,		UpperCamelCase_:      int				)     ->  int:





									"""simple docstring"""







									super().__init__(UpperCamelCase_				)
									lowercase__				= config.num_labels

									lowercase__				= BertModelWithPabee(UpperCamelCase_				)
									lowercase__				= nn.Dropout(config.hidden_dropout_prob				)
									lowercase__				= 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  lowerCamelCase_					(				self:      List[Any]       ,		UpperCamelCase_:      Union[str, Any]=None       ,		UpperCamelCase_:      Optional[Any]=None       ,		UpperCamelCase_:      int=None       ,		UpperCamelCase_:      Optional[Any]=None       ,		UpperCamelCase_:      Any=None       ,		UpperCamelCase_:      Optional[Any]=None       ,		UpperCamelCase_:      List[str]=None       ,		)     ->  List[str]:





									"""simple docstring"""







									lowercase__				= self.bert(
									    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       ,		)

									lowercase__				= (logits[-1],)

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

									return outputs










from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


A__      :     Union[str, Any]       =					transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				if isinstance(lowerCamelCase_						,  torch.Tensor							):
								return image
				elif isinstance(lowerCamelCase_						,  PIL.Image.Image							):
								lowercase__  					=     [image]

				lowercase__  					=     [trans(img.convert('''RGB'''							)							) for img in image]
				lowercase__  					=     torch.stack(lowerCamelCase_							)
				return image




class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				def __init__(	self		:       Tuple,    lowerCamelCase		:       Dict,    lowerCamelCase		:       str    ):

								'''simple docstring'''
								super().__init__()

								# make sure scheduler can always be converted to DDIM
								lowercase__  					=     DDIMScheduler.from_config(scheduler.config    )

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




				def 							lowercase__  (	self		:       int,    lowerCamelCase		:       Optional[int]    ):

								'''simple docstring'''
								if strength < 0 or strength > 1:
												raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}"""    )




				def 							lowercase__  (	self		:       Optional[int],    lowerCamelCase		:       Union[str, Any],    lowerCamelCase		:       Dict,    lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								# get the original timestep using init_timestep
								lowercase__  					=     min(int(num_inference_steps * strength    ),    lowerCamelCase    )

								lowercase__  					=     max(num_inference_steps - init_timestep,    0    )
								lowercase__  					=     self.scheduler.timesteps[t_start:]

								return timesteps, num_inference_steps - t_start




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       List[Any],    lowerCamelCase		:       int,    lowerCamelCase		:       Dict,    lowerCamelCase		:       Any,    lowerCamelCase		:       Optional[Any],    lowerCamelCase		:       Any=None    ):

								'''simple docstring'''
								if not isinstance(lowerCamelCase,    (torch.Tensor, PIL.Image.Image, list)    ):
												raise ValueError(
												    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCamelCase    )}"""    )

								lowercase__  					=     image.to(device=lowerCamelCase,    dtype=lowerCamelCase    )

								if isinstance(lowerCamelCase,    lowerCamelCase    ) and len(lowerCamelCase    ) != batch_size:
												raise ValueError(
												    F"""You have passed a list of generators of length {len(lowerCamelCase    )}, but requested an effective batch"""
												    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""    )

								lowercase__  					=     init_latents.shape
								lowercase__  					=     randn_tensor(lowerCamelCase,    generator=lowerCamelCase,    device=lowerCamelCase,    dtype=lowerCamelCase    )

								# get latents
								print('''add noise to latents at timestep''',    lowerCamelCase    )
								lowercase__  					=     self.scheduler.add_noise(lowerCamelCase,    lowerCamelCase,    lowerCamelCase    )
								lowercase__  					=     init_latents

								return latents




				@torch.no_grad()
				def __call__(	self		:       Dict,    lowerCamelCase		:       Union[torch.FloatTensor, PIL.Image.Image] = None,    lowerCamelCase		:       float = 0.8,    lowerCamelCase		:       int = 1,    lowerCamelCase		:       Optional[Union[torch.Generator, List[torch.Generator]]] = None,    lowerCamelCase		:       float = 0.0,    lowerCamelCase		:       int = 50,    lowerCamelCase		:       Optional[bool] = None,    lowerCamelCase		:       Optional[str] = "pil",    lowerCamelCase		:       bool = True,    ):

								'''simple docstring'''
								self.check_inputs(lowerCamelCase    )

								# 2. Preprocess image
								lowercase__  					=     preprocess(lowerCamelCase    )

								# 3. set timesteps
								self.scheduler.set_timesteps(lowerCamelCase,    device=self.device    )
								lowercase__					,		lowercase__  					=     self.get_timesteps(lowerCamelCase,    lowerCamelCase,    self.device    )
								lowercase__  					=     timesteps[:1].repeat(lowerCamelCase    )

								# 4. Prepare latent variables
								lowercase__  					=     self.prepare_latents(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    self.unet.dtype,    self.device,    lowerCamelCase    )
								lowercase__  					=     latents

								# 5. Denoising loop
								for t in self.progress_bar(lowerCamelCase    ):
												# 1. predict noise model_output
												lowercase__  					=     self.unet(lowerCamelCase,    lowerCamelCase    ).sample

												# 2. predict previous mean of image x_t-1 and add variance depending on eta
												# eta corresponds to η in paper and should be between [0, 1]
												# do x_t -> x_t-1
												lowercase__  					=     self.scheduler.step(
												    lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    eta=lowerCamelCase,    use_clipped_model_output=lowerCamelCase,    generator=lowerCamelCase,    ).prev_sample

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

								if not return_dict:
												return (image, latent_timestep.item())

								return ImagePipelineOutput(images=lowerCamelCase    )




