


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,
)


SCREAMING_SNAKE_CASE__				:    Dict         =    logging.getLogger(__name__)


class 		_SCREAMING_SNAKE_CASE    (	A    ):


     def 					__snake_case(     self       ,				A_       ,				A_       ,				A_=None       ,				A_=None						):
       if current_layer == 0:
         _UpperCAmelCase		:       Any       =    self.embedding_hidden_mapping_in(A_						)
       else:
         _UpperCAmelCase		:       Optional[int]       =    hidden_states[0]

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

       # Index of the hidden group
       _UpperCAmelCase		:       Union[str, Any]       =    int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)						)

       _UpperCAmelCase		:       int       =    self.albert_layer_groups[group_idx](
           A_       ,				A_       ,				head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]       ,				)
       _UpperCAmelCase		:       Dict       =    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 		_SCREAMING_SNAKE_CASE    (	A    ):


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

       _UpperCAmelCase		:       List[Any]       =    AlbertTransformerWithPabee(A_						)

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

       _UpperCAmelCase		:       Dict       =    0


     def 					__snake_case(     self       ,				A_						):
       _UpperCAmelCase		:       str       =    threshold


     def 					__snake_case(     self       ,				A_						):
       _UpperCAmelCase		:       Optional[Any]       =    patience


     def 					__snake_case(     self						):
       _UpperCAmelCase		:       Tuple       =    0
       _UpperCAmelCase		:       Tuple       =    0


     def 					__snake_case(     self						):
       _UpperCAmelCase		:       Dict       =    self.inference_layers_num / self.inference_instances_num
       _UpperCAmelCase		:       Optional[int]       =    (
           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(A_						)




     @add_start_docstrings_to_model_forward(A_						)
     def 					__snake_case(     self       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=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:
         _UpperCAmelCase		:       List[str]       =    input_ids.size()
       elif inputs_embeds is not None:
         _UpperCAmelCase		:       Optional[Any]       =    inputs_embeds.size()[:-1]
       else:
         raise ValueError("""You have to specify either input_ids or inputs_embeds"""						)

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

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

       _UpperCAmelCase		:       str       =    attention_mask.unsqueeze(1						).unsqueeze(2						)
       _UpperCAmelCase		:       Tuple       =    extended_attention_mask.to(dtype=self.dtype						)  # fp16 compatibility
       _UpperCAmelCase		:       Tuple       =    (1.0 - extended_attention_mask) * -1_0_0_0_0.0
       _UpperCAmelCase		:       Union[str, Any]       =    self.get_head_mask(A_       ,				self.config.num_hidden_layers						)

       _UpperCAmelCase		:       Dict       =    self.embeddings(
           A_       ,				position_ids=A_       ,				token_type_ids=A_       ,				inputs_embeds=A_						)
       _UpperCAmelCase		:       str       =    embedding_output

       if self.training:
         _UpperCAmelCase		:       Any       =    []
         for i in range(self.config.num_hidden_layers						):
           _UpperCAmelCase		:       Tuple       =    self.encoder.adaptive_forward(
               A_       ,				current_layer=A_       ,				attention_mask=A_       ,				head_mask=A_       ,				)

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

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

           _UpperCAmelCase		:       int       =    logits
           if patient_counter == self.patience:
             break
         _UpperCAmelCase		:       Tuple       =    [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 		_SCREAMING_SNAKE_CASE    (	A    ):


     def __init__(     self       ,				A_						):
       super().__init__(A_						)
       _UpperCAmelCase		:       List[str]       =    config.num_labels

       _UpperCAmelCase		:       List[Any]       =    AlbertModelWithPabee(A_						)
       _UpperCAmelCase		:       Optional[int]       =    nn.Dropout(config.classifier_dropout_prob						)
       _UpperCAmelCase		:       Tuple       =    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(A_						)
     def 					__snake_case(     self       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				):
       _UpperCAmelCase		:       Optional[Any]       =    self.albert(
           input_ids=A_       ,				attention_mask=A_       ,				token_type_ids=A_       ,				position_ids=A_       ,				head_mask=A_       ,				inputs_embeds=A_       ,				output_dropout=self.dropout       ,				output_layers=self.classifiers       ,				regression=self.num_labels == 1       ,				)

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

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

       return outputs









from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class 		_SCREAMING_SNAKE_CASE    (	A    ):
     __SCREAMING_SNAKE_CASE    				=						{
         '''sld_warmup_steps''': 1_5,
         '''sld_guidance_scale''': 2_0,
         '''sld_threshold''': 0.0,
         '''sld_momentum_scale''': 0.0,
         '''sld_mom_beta''': 0.0,
     }
     __SCREAMING_SNAKE_CASE    				=						{
         '''sld_warmup_steps''': 1_0,
         '''sld_guidance_scale''': 1_0_0_0,
         '''sld_threshold''': 0.01,
         '''sld_momentum_scale''': 0.3,
         '''sld_mom_beta''': 0.4,
     }
     __SCREAMING_SNAKE_CASE    				=						{
         '''sld_warmup_steps''': 7,
         '''sld_guidance_scale''': 2_0_0_0,
         '''sld_threshold''': 0.025,
         '''sld_momentum_scale''': 0.5,
         '''sld_mom_beta''': 0.7,
     }
     __SCREAMING_SNAKE_CASE    				=						{
         '''sld_warmup_steps''': 0,
         '''sld_guidance_scale''': 5_0_0_0,
         '''sld_threshold''': 1.0,
         '''sld_momentum_scale''': 0.5,
         '''sld_mom_beta''': 0.7,
     }


@dataclass
class 		_SCREAMING_SNAKE_CASE    (	A    ):
     __SCREAMING_SNAKE_CASE    				=						42
     __SCREAMING_SNAKE_CASE    				=						42
     __SCREAMING_SNAKE_CASE    				=						42
     __SCREAMING_SNAKE_CASE    				=						42


if is_transformers_available() and is_torch_available():
 from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
 from .safety_checker import SafeStableDiffusionSafetyChecker





