


'''simple docstring'''

import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


_UpperCAmelCase					:			List[Any]				     =					logging.get_logger(__name__)

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase):
						__lowerCAmelCase											=					nn.functional.normalize(lowerCamelCase)
						__lowerCAmelCase											=					nn.functional.normalize(lowerCamelCase)
						return torch.mm(lowerCamelCase,					normalized_text_embeds.t())




class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		Tuple						=						CLIPConfig

				__UpperCamelCase     :		Optional[int]						=						['CLIPEncoderLayer']




				def __init__(self							,      __lowercase		):
										super().__init__(__lowercase		)

										__lowerCAmelCase											=					CLIPVisionModel(config.vision_config		)
										__lowerCAmelCase											=					nn.Linear(config.vision_config.hidden_size							,      config.projection_dim							,      bias=__lowercase		)

										__lowerCAmelCase											=					nn.Parameter(torch.ones(17							,      config.projection_dim		)							,      requires_grad=__lowercase		)
										__lowerCAmelCase											=					nn.Parameter(torch.ones(3							,      config.projection_dim		)							,      requires_grad=__lowercase		)

										__lowerCAmelCase											=					nn.Parameter(torch.ones(17		)							,      requires_grad=__lowercase		)
										__lowerCAmelCase											=					nn.Parameter(torch.ones(3		)							,      requires_grad=__lowercase		)




				@torch.no_grad()
				def        _snake_case	(self							,      __lowercase							,      __lowercase		):
										__lowerCAmelCase											=					self.vision_model(__lowercase		)[1]  # pooled_output
										__lowerCAmelCase											=					self.visual_projection(__lowercase		)

										# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
										__lowerCAmelCase											=					cosine_distance(__lowercase							,      self.special_care_embeds		).cpu().float().numpy()
										__lowerCAmelCase											=					cosine_distance(__lowercase							,      self.concept_embeds		).cpu().float().numpy()

										__lowerCAmelCase											=					[]
										__lowerCAmelCase											=					image_embeds.shape[0]
										for i in range(__lowercase		):
																__lowerCAmelCase											=					{'''special_scores''': {}, '''special_care''': [], '''concept_scores''': {}, '''bad_concepts''': []}

																# increase this value to create a stronger `nfsw` filter
																# at the cost of increasing the possibility of filtering benign images
																__lowerCAmelCase											=					0.0

																for concept_idx in range(len(special_cos_dist[0]		)		):
																						__lowerCAmelCase											=					special_cos_dist[i][concept_idx]
																						__lowerCAmelCase											=					self.special_care_embeds_weights[concept_idx].item()
																						__lowerCAmelCase											=					round(concept_cos - concept_threshold + adjustment							,      3		)
																						if result_img["special_scores"][concept_idx] > 0:
																												result_img["special_care"].append({concept_idx, result_img['''special_scores'''][concept_idx]}		)
																												__lowerCAmelCase											=					0.0_1

																for concept_idx in range(len(cos_dist[0]		)		):
																						__lowerCAmelCase											=					cos_dist[i][concept_idx]
																						__lowerCAmelCase											=					self.concept_embeds_weights[concept_idx].item()
																						__lowerCAmelCase											=					round(concept_cos - concept_threshold + adjustment							,      3		)
																						if result_img["concept_scores"][concept_idx] > 0:
																												result_img["bad_concepts"].append(__lowercase		)

																result.append(__lowercase		)

										__lowerCAmelCase											=					[len(res['''bad_concepts''']		) > 0 for res in result]

										for idx, has_nsfw_concept in enumerate(__lowercase		):
																if has_nsfw_concept:
																						if torch.is_tensor(__lowercase		) or torch.is_tensor(images[0]		):
																												__lowerCAmelCase											=					torch.zeros_like(images[idx]		)  # black image
																						else:
																												__lowerCAmelCase											=					np.zeros(images[idx].shape		)  # black image

										if any(__lowercase		):
																logger.warning(
																    '''Potential NSFW content was detected in one or more images. A black image will be returned instead.'''
																    ''' Try again with a different prompt and/or seed.'''		)

										return images, has_nsfw_concepts




				@torch.no_grad()
				def        _snake_case	(self							,      __lowercase							,      __lowercase		):
										__lowerCAmelCase											=					self.vision_model(__lowercase		)[1]  # pooled_output
										__lowerCAmelCase											=					self.visual_projection(__lowercase		)

										__lowerCAmelCase											=					cosine_distance(__lowercase							,      self.special_care_embeds		)
										__lowerCAmelCase											=					cosine_distance(__lowercase							,      self.concept_embeds		)

										# increase this value to create a stronger `nsfw` filter
										# at the cost of increasing the possibility of filtering benign images
										__lowerCAmelCase											=					0.0

										__lowerCAmelCase											=					special_cos_dist - self.special_care_embeds_weights + adjustment
										# special_scores = special_scores.round(decimals=3)
										__lowerCAmelCase											=					torch.any(special_scores > 0							,      dim=1		)
										__lowerCAmelCase											=					special_care * 0.0_1
										__lowerCAmelCase											=					special_adjustment.unsqueeze(1		).expand(-1							,      cos_dist.shape[1]		)

										__lowerCAmelCase											=					(cos_dist - self.concept_embeds_weights) + special_adjustment
										# concept_scores = concept_scores.round(decimals=3)
										__lowerCAmelCase											=					torch.any(concept_scores > 0							,      dim=1		)

										__lowerCAmelCase											=					0.0  # black image

										return images, has_nsfw_concepts





'''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,
)


_UpperCAmelCase					:			List[str]				     =					logging.getLogger(__name__)


class    a__					(			__A   ):







				"""simple docstring"""



				def        _snake_case	(self							,      __lowercase							,      __lowercase							,      __lowercase=None							,      __lowercase=None		):
										if current_layer == 0:
																__lowerCAmelCase											=					self.embedding_hidden_mapping_in(__lowercase		)
										else:
																__lowerCAmelCase											=					hidden_states[0]

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

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

										__lowerCAmelCase											=					self.albert_layer_groups[group_idx](
										    __lowercase							,      __lowercase							,      head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]							,      )
										__lowerCAmelCase											=					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    a__					(			__A   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase		):
										super().__init__(__lowercase		)

										__lowerCAmelCase											=					AlbertTransformerWithPabee(__lowercase		)

										self.init_weights()
										__lowerCAmelCase											=					0
										__lowerCAmelCase											=					0
										__lowerCAmelCase											=					0

										__lowerCAmelCase											=					0




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					threshold




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					patience




				def        _snake_case	(self		):
										__lowerCAmelCase											=					0
										__lowerCAmelCase											=					0




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.inference_layers_num / self.inference_instances_num
										__lowerCAmelCase											=					(
										    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(__lowercase		)




				@add_start_docstrings_to_model_forward(__lowercase		)
				def        _snake_case	(self							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=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:
																__lowerCAmelCase											=					input_ids.size()
										elif inputs_embeds is not None:
																__lowerCAmelCase											=					inputs_embeds.size()[:-1]
										else:
																raise ValueError('''You have to specify either input_ids or inputs_embeds'''		)

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

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

										__lowerCAmelCase											=					attention_mask.unsqueeze(1		).unsqueeze(2		)
										__lowerCAmelCase											=					extended_attention_mask.to(dtype=self.dtype		)  # fp16 compatibility
										__lowerCAmelCase											=					(1.0 - extended_attention_mask) * -1_0_0_0_0.0
										__lowerCAmelCase											=					self.get_head_mask(__lowercase							,      self.config.num_hidden_layers		)

										__lowerCAmelCase											=					self.embeddings(
										    __lowercase							,      position_ids=__lowercase							,      token_type_ids=__lowercase							,      inputs_embeds=__lowercase		)
										__lowerCAmelCase											=					embedding_output

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

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

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

																						__lowerCAmelCase											=					logits
																						if patient_counter == self.patience:
																												break
																__lowerCAmelCase											=					[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. '				,    __A				,    )
class    a__					(			__A   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase		):
										super().__init__(__lowercase		)
										__lowerCAmelCase											=					config.num_labels

										__lowerCAmelCase											=					AlbertModelWithPabee(__lowercase		)
										__lowerCAmelCase											=					nn.Dropout(config.classifier_dropout_prob		)
										__lowerCAmelCase											=					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(__lowercase		)
				def        _snake_case	(self							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      ):
										__lowerCAmelCase											=					self.albert(
										    input_ids=__lowercase							,      attention_mask=__lowercase							,      token_type_ids=__lowercase							,      position_ids=__lowercase							,      head_mask=__lowercase							,      inputs_embeds=__lowercase							,      output_dropout=self.dropout							,      output_layers=self.classifiers							,      regression=self.num_labels == 1							,      )

										__lowerCAmelCase											=					(logits[-1],)

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

										return outputs

