






from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


__lowerCAmelCase     :			Optional[int]      =getLogger(__name__)


__lowerCAmelCase     :			Optional[Any]      =4
__lowerCAmelCase     :			List[Any]      =7

__lowerCAmelCase     :			List[str]      ={
    'float16': torch.floataa,
    'float32': torch.floataa,
}


def 	_UpperCamelCase							(				lowercase__				):
						if isinstance(_lowerCAmelCase   ,	torch.Tensor				):
												return image
						elif isinstance(_lowerCAmelCase   ,	PIL.Image.Image				):
												__SCREAMING_SNAKE_CASE						:					Tuple		      =		[image]

						if isinstance(image[0]   ,	PIL.Image.Image				):
												__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Any		      =		image[0].size
												__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					List[Any]		      =		(x - x % 64 for x in (w, h))  # resize to integer multiple of 32

												__SCREAMING_SNAKE_CASE						:					Any		      =		[np.array(i.resize((w, h)				)				)[None, :] for i in image]
												__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		np.concatenate(_lowerCAmelCase   ,	axis=0				)
												__SCREAMING_SNAKE_CASE						:					Dict		      =		np.array(_lowerCAmelCase				).astype(np.floataa				) / 255.0
												__SCREAMING_SNAKE_CASE						:					Dict		      =		image.transpose(0   ,	3   ,	1   ,	2				)
												__SCREAMING_SNAKE_CASE						:					int		      =		2.0 * image - 1.0
												__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		torch.from_numpy(_lowerCAmelCase				)
						elif isinstance(image[0]   ,	torch.Tensor				):
												__SCREAMING_SNAKE_CASE						:					Dict		      =		torch.cat(_lowerCAmelCase   ,	dim=0				)

						return image
class 	_lowercase				(      A__	):






				'''simple docstring'''



				def __init__(	self      :Union[str, Any]      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :str      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Union[str, Any] = 350      ,      )		->      List[str]:
										super().__init__(
										    vae=snake_case_      ,      text_encoder=snake_case_      ,      tokenizer=snake_case_      ,      unet=snake_case_      ,      low_res_scheduler=snake_case_      ,      scheduler=snake_case_      ,      safety_checker=snake_case_      ,      feature_extractor=snake_case_      ,      watermarker=snake_case_      ,      max_noise_level=snake_case_      ,      )


				def __call__(	self      :Dict      ,      lowerCAmelCase__      :str      ,      lowerCAmelCase__      :int      ,      lowerCAmelCase__      :str = 75      ,      lowerCAmelCase__      :Union[str, Any] = 9.0      ,      lowerCAmelCase__      :Optional[Any] = 20      ,      lowerCAmelCase__      :List[str] = None      ,      lowerCAmelCase__      :List[str] = 1      ,      lowerCAmelCase__      :Tuple = 0.0      ,      lowerCAmelCase__      :Dict = None      ,      lowerCAmelCase__      :Any = None      ,      lowerCAmelCase__      :int = None      ,      lowerCAmelCase__      :str = None      ,      lowerCAmelCase__      :Optional[Any] = "pil"      ,      lowerCAmelCase__      :Any = True      ,      lowerCAmelCase__      :Union[str, Any] = None      ,      lowerCAmelCase__      :str = 1      ,      )		->      Any:
										self.check_inputs(snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_     )

										# 2. Define call parameters
										if prompt is not None and isinstance(snake_case_      ,      snake_case_     ):
																__SCREAMING_SNAKE_CASE						:					str		      =		1
										elif prompt is not None and isinstance(snake_case_      ,      snake_case_     ):
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		len(snake_case_     )
										else:
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		prompt_embeds.shape[0]

										__SCREAMING_SNAKE_CASE						:					Dict		      =		self._execution_device
										# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
										# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
										# corresponds to doing no classifier free guidance.
										__SCREAMING_SNAKE_CASE						:					int		      =		guidance_scale > 1.0

										# 3. Encode input prompt
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self._encode_prompt(
										    snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      prompt_embeds=snake_case_      ,      negative_prompt_embeds=snake_case_      ,      )

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		ORT_TO_PT_TYPE[str(text_embeddings.dtype     )]

										# 4. Preprocess image
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		preprocess(snake_case_     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		image.cpu()

										# 5. set timesteps
										self.scheduler.set_timesteps(snake_case_      ,      device=snake_case_     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.scheduler.timesteps

										# 5. Add noise to image
										__SCREAMING_SNAKE_CASE						:					str		      =		torch.tensor([noise_level]      ,      dtype=torch.long      ,      device=snake_case_     )
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		torch.randn(image.shape      ,      generator=snake_case_      ,      device=snake_case_      ,      dtype=snake_case_     )
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		self.low_res_scheduler.add_noise(snake_case_      ,      snake_case_      ,      snake_case_     )

										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		2 if do_classifier_free_guidance else 1
										__SCREAMING_SNAKE_CASE						:					Any		      =		np.concatenate([image] * batch_multiplier * num_images_per_prompt     )
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		np.concatenate([noise_level] * image.shape[0]     )

										# 6. Prepare latent variables
										__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		image.shape[2:]
										__SCREAMING_SNAKE_CASE						:					int		      =		self.prepare_latents(
										    batch_size * num_images_per_prompt      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      )

										# 7. Check that sizes of image and latents match
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		image.shape[1]
										if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
																raise ValueError(
																    '''Incorrect configuration settings! The config of `pipeline.unet` expects'''
																    f''' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'''
																    f''' `num_channels_image`: {num_channels_image} '''
																    f''' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'''
																    ''' `pipeline.unet` or your `image` input.'''     )

										# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
										__SCREAMING_SNAKE_CASE						:					Any		      =		self.prepare_extra_step_kwargs(snake_case_      ,      snake_case_     )

										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		next(
										    (input.type for input in self.unet.model.get_inputs() if input.name == '''timestep''')      ,      '''tensor(float)'''     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		ORT_TO_NP_TYPE[timestep_dtype]

										# 9. Denoising loop
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		len(snake_case_     ) - num_inference_steps * self.scheduler.order
										with self.progress_bar(total=snake_case_     ) as progress_bar:
																for i, t in enumerate(snake_case_     ):
																						# expand the latents if we are doing classifier free guidance
																						__SCREAMING_SNAKE_CASE						:					Tuple		      =		np.concatenate([latents] * 2     ) if do_classifier_free_guidance else latents

																						# concat latents, mask, masked_image_latents in the channel dimension
																						__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.scheduler.scale_model_input(snake_case_      ,      snake_case_     )
																						__SCREAMING_SNAKE_CASE						:					Tuple		      =		np.concatenate([latent_model_input, image]      ,      axis=1     )

																						# timestep to tensor
																						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		np.array([t]      ,      dtype=snake_case_     )

																						# predict the noise residual
																						__SCREAMING_SNAKE_CASE						:					int		      =		self.unet(
																						    sample=snake_case_      ,      timestep=snake_case_      ,      encoder_hidden_states=snake_case_      ,      class_labels=noise_level.astype(np.intaa     )      ,      )[0]

																						# perform guidance
																						if do_classifier_free_guidance:
																												__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					List[str]		      =		np.split(snake_case_      ,      2     )
																						__SCREAMING_SNAKE_CASE						:					Any		      =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																						# compute the previous noisy sample x_t -> x_t-1
																						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		self.scheduler.step(
																						    torch.from_numpy(snake_case_     )      ,      snake_case_      ,      snake_case_      ,      **snake_case_     ).prev_sample

																						# call the callback, if provided
																						if i == len(snake_case_     ) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
																												progress_bar.update()
																												if callback is not None and i % callback_steps == 0:
																																		callback(snake_case_      ,      snake_case_      ,      snake_case_     )

        # 10. Post-processing
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		self.decode_latents(latents.float()     )

										# 11. Convert to PIL
										if output_type == "pil":
																__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.numpy_to_pil(snake_case_     )

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=snake_case_     )


				def 		__magic_name__(	self      :List[str]      ,      lowerCAmelCase__      :Optional[Any]     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		1 / 0.0_8333 * latents
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.vae(latent_sample=snake_case_     )[0]
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		np.clip(image / 2 + 0.5      ,      0      ,      1     )
										__SCREAMING_SNAKE_CASE						:					str		      =		image.transpose((0, 2, 3, 1)     )
										return image



				def 		__magic_name__(	self      :Union[str, Any]      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :List[str]      ,      lowerCAmelCase__      :List[str]      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Tuple = None      ,      lowerCAmelCase__      :Optional[Any] = None      ,      )		->      List[str]:
										if prompt is not None and isinstance(snake_case_      ,      snake_case_     ):
																__SCREAMING_SNAKE_CASE						:					Any		      =		1
										elif prompt is not None and isinstance(snake_case_      ,      snake_case_     ):
																__SCREAMING_SNAKE_CASE						:					int		      =		len(snake_case_     )
										else:
																__SCREAMING_SNAKE_CASE						:					Any		      =		prompt_embeds.shape[0]

										if prompt_embeds is None:
																__SCREAMING_SNAKE_CASE						:					str		      =		self.tokenizer(
																    snake_case_      ,      padding='''max_length'''      ,      max_length=self.tokenizer.model_max_length      ,      truncation=snake_case_      ,      return_tensors='''pt'''      ,      )
																__SCREAMING_SNAKE_CASE						:					Dict		      =		text_inputs.input_ids
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.tokenizer(snake_case_      ,      padding='''longest'''      ,      return_tensors='''pt'''     ).input_ids

																if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
																    snake_case_      ,      snake_case_     ):
																						__SCREAMING_SNAKE_CASE						:					int		      =		self.tokenizer.batch_decode(
																						    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]     )
																						logger.warning(
																						    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
																						    f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''     )

																# no positional arguments to text_encoder
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.text_encoder(
																    input_ids=text_input_ids.int().to(snake_case_     )      ,      )
																__SCREAMING_SNAKE_CASE						:					str		      =		prompt_embeds[0]

										__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		prompt_embeds.shape
										# duplicate text embeddings for each generation per prompt, using mps friendly method
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		prompt_embeds.repeat(1      ,      snake_case_     )
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		prompt_embeds.reshape(bs_embed * num_images_per_prompt      ,      snake_case_      ,      -1     )

										# get unconditional embeddings for classifier free guidance
										if do_classifier_free_guidance and negative_prompt_embeds is None:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		42
																if negative_prompt is None:
																						__SCREAMING_SNAKE_CASE						:					int		      =		[''''''] * batch_size
																elif type(snake_case_     ) is not type(snake_case_     ):
																						raise TypeError(
																						    f'''`negative_prompt` should be the same type to `prompt`, but got {type(snake_case_     )} !='''
																						    f''' {type(snake_case_     )}.'''     )
																elif isinstance(snake_case_      ,      snake_case_     ):
																						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		[negative_prompt]
																elif batch_size != len(snake_case_     ):
																						raise ValueError(
																						    f'''`negative_prompt`: {negative_prompt} has batch size {len(snake_case_     )}, but `prompt`:'''
																						    f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
																						    ''' the batch size of `prompt`.'''     )
																else:
																						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		negative_prompt

																__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		text_input_ids.shape[-1]
																__SCREAMING_SNAKE_CASE						:					Any		      =		self.tokenizer(
																    snake_case_      ,      padding='''max_length'''      ,      max_length=snake_case_      ,      truncation=snake_case_      ,      return_tensors='''pt'''      ,      )

																# if hasattr(uncond_input, "attention_mask"):
																#     attention_mask = uncond_input.attention_mask.to(device)
																# else:
																#     attention_mask = None

																__SCREAMING_SNAKE_CASE						:					Any		      =		self.text_encoder(
																    input_ids=uncond_input.input_ids.int().to(snake_case_     )      ,      )
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		uncond_embeddings[0]

										if do_classifier_free_guidance:
																__SCREAMING_SNAKE_CASE						:					Dict		      =		uncond_embeddings.shape[1]
																# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		uncond_embeddings.repeat(1      ,      snake_case_     )
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		uncond_embeddings.reshape(batch_size * num_images_per_prompt      ,      snake_case_      ,      -1     )

																# For classifier free guidance, we need to do two forward passes.
																# Here we concatenate the unconditional and text embeddings into a single batch
																# to avoid doing two forward passes
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		np.concatenate([uncond_embeddings, prompt_embeds]     )

										return prompt_embeds




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     :			Any      =logging.getLogger(__name__)
class 	_lowercase				(      A__	):






				'''simple docstring'''



				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :List[str]      ,      lowerCAmelCase__      :List[Any]=None      ,      lowerCAmelCase__      :Optional[Any]=None     )		->      List[Any]:
										if current_layer == 0:
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		self.embedding_hidden_mapping_in(lowerCAmelCase__     )
										else:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		hidden_states[0]

										__SCREAMING_SNAKE_CASE						:					Tuple		      =		int(self.config.num_hidden_layers / self.config.num_hidden_groups     )

										# Index of the hidden group
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)     )

										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.albert_layer_groups[group_idx](
										    lowerCAmelCase__      ,      lowerCAmelCase__      ,      head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]      ,      )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		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 	_lowercase				(      A__	):






				'''simple docstring'''



				def __init__(	self      :int      ,      lowerCAmelCase__      :List[str]     )		->      List[Any]:
										super().__init__(lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		AlbertTransformerWithPabee(lowerCAmelCase__     )

										self.init_weights()
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		0
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		0
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		0

										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		0


				def 		__magic_name__(	self      :int      ,      lowerCAmelCase__      :Optional[int]     )		->      str:
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		threshold


				def 		__magic_name__(	self      :List[Any]      ,      lowerCAmelCase__      :Optional[int]     )		->      List[Any]:
										__SCREAMING_SNAKE_CASE						:					Dict		      =		patience


				def 		__magic_name__(	self      :Union[str, Any]     )		->      str:
										__SCREAMING_SNAKE_CASE						:					Any		      =		0
										__SCREAMING_SNAKE_CASE						:					int		      =		0


				def 		__magic_name__(	self      :Optional[int]     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.inference_layers_num / self.inference_instances_num
										__SCREAMING_SNAKE_CASE						:					Union[str, 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 		__magic_name__(	self      :Dict      ,      lowerCAmelCase__      :List[str]=None      ,      lowerCAmelCase__      :List[str]=None      ,      lowerCAmelCase__      :Optional[Any]=None      ,      lowerCAmelCase__      :Any=None      ,      lowerCAmelCase__      :Dict=None      ,      lowerCAmelCase__      :Optional[int]=None      ,      lowerCAmelCase__      :str=None      ,      lowerCAmelCase__      :str=None      ,      lowerCAmelCase__      :str=False      ,      )		->      Union[str, Any]:

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

										__SCREAMING_SNAKE_CASE						:					List[str]		      =		input_ids.device if input_ids is not None else inputs_embeds.device

										if attention_mask is None:
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		torch.ones(lowerCAmelCase__      ,      device=lowerCAmelCase__     )
										if token_type_ids is None:
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		torch.zeros(lowerCAmelCase__      ,      dtype=torch.long      ,      device=lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					Any		      =		attention_mask.unsqueeze(1     ).unsqueeze(2     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		extended_attention_mask.to(dtype=self.dtype     )  # fp16 compatibility
										__SCREAMING_SNAKE_CASE						:					Dict		      =		(1.0 - extended_attention_mask) * -1_0000.0
										__SCREAMING_SNAKE_CASE						:					str		      =		self.get_head_mask(lowerCAmelCase__      ,      self.config.num_hidden_layers     )

										__SCREAMING_SNAKE_CASE						:					List[str]		      =		self.embeddings(
										    lowerCAmelCase__      ,      position_ids=lowerCAmelCase__      ,      token_type_ids=lowerCAmelCase__      ,      inputs_embeds=lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		embedding_output

										if self.training:
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		[]
																for i in range(self.config.num_hidden_layers     ):
																						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.encoder.adaptive_forward(
																						    lowerCAmelCase__      ,      current_layer=lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      head_mask=lowerCAmelCase__      ,      )

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

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

																						__SCREAMING_SNAKE_CASE						:					str		      =		logits
																						if patient_counter == self.patience:
																												break
																__SCREAMING_SNAKE_CASE						:					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 	_lowercase				(      A__	):






				'''simple docstring'''



				def __init__(	self      :List[Any]      ,      lowerCAmelCase__      :int     )		->      Optional[int]:
										super().__init__(lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Any		      =		config.num_labels

										__SCREAMING_SNAKE_CASE						:					str		      =		AlbertModelWithPabee(lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		nn.Dropout(config.classifier_dropout_prob     )
										__SCREAMING_SNAKE_CASE						:					Dict		      =		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 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :str=None      ,      lowerCAmelCase__      :List[str]=None      ,      lowerCAmelCase__      :Dict=None      ,      lowerCAmelCase__      :str=None      ,      lowerCAmelCase__      :Tuple=None      ,      lowerCAmelCase__      :int=None      ,      lowerCAmelCase__      :Any=None      ,      )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		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      ,      )

										__SCREAMING_SNAKE_CASE						:					int		      =		(logits[-1],)

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

										return outputs
