'''simple docstring'''





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,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)


def     __lowerCAmelCase						(       a_		)      ->	List[str]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.exp(a_		)
     SCREAMING_SNAKE_CASE					:			Dict    	=       torch.sum(a_       ,      dim=1		)  # sum of exp(x_i)
     SCREAMING_SNAKE_CASE					:			str    	=       torch.sum(x * exp_x       ,      dim=1		)  # sum of x_i * exp(x_i)
     return torch.log(a_		) - B / A






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__ )							-> Optional[Any]:
         super().__init__()
         SCREAMING_SNAKE_CASE					:			int    	=       config.output_attentions
         SCREAMING_SNAKE_CASE					:			List[str]    	=       config.output_hidden_states
         SCREAMING_SNAKE_CASE					:			List[str]    	=       nn.ModuleList([BertLayer(lowercase__ ) for _ in range(config.num_hidden_layers )] )
         SCREAMING_SNAKE_CASE					:			Dict    	=       nn.ModuleList([BertHighway(lowercase__ ) for _ in range(config.num_hidden_layers )] )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [-1 for _ in range(config.num_hidden_layers )]






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Tuple:
         if (type(lowercase__ ) is float) or (type(lowercase__ ) is int):
              for i in range(len(self.early_exit_entropy ) ):
                   SCREAMING_SNAKE_CASE					:			List[Any]    	=       x
         else:
              SCREAMING_SNAKE_CASE					:			Dict    	=       x






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       pooler.state_dict()
         for highway in self.highway:
              for name, param in highway.pooler.state_dict().items():
                   param.copy_(loaded_model[name] )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							)							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			str    	=       ()
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       ()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       ()
         for i, layer_module in enumerate(self.layer ):
              if self.output_hidden_states:
                   SCREAMING_SNAKE_CASE					:			List[Any]    	=       all_hidden_states + (hidden_states,)

              SCREAMING_SNAKE_CASE					:			int    	=       layer_module(
                  lowercase__    ,							lowercase__    ,							head_mask[i]    ,							lowercase__    ,							lowercase__ )
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       layer_outputs[0]

              if self.output_attentions:
                   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       all_attentions + (layer_outputs[1],)

              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       (hidden_states,)
              if self.output_hidden_states:
                   SCREAMING_SNAKE_CASE					:			Any    	=       current_outputs + (all_hidden_states,)
              if self.output_attentions:
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       current_outputs + (all_attentions,)

              SCREAMING_SNAKE_CASE					:			List[str]    	=       self.highway[i](lowercase__ )
              # logits, pooled_output

              if not self.training:
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       highway_exit[0]
                   SCREAMING_SNAKE_CASE					:			List[Any]    	=       entropy(lowercase__ )
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       all_highway_exits + (highway_exit,)

                   if highway_entropy < self.early_exit_entropy[i]:
                        SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
                        raise HighwayException(lowercase__    ,							i + 1 )
              else:
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       all_highway_exits + (highway_exit,)

        # Add last layer
         if self.output_hidden_states:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       all_hidden_states + (hidden_states,)

         SCREAMING_SNAKE_CASE					:			List[str]    	=       (hidden_states,)
         if self.output_hidden_states:
              SCREAMING_SNAKE_CASE					:			str    	=       outputs + (all_hidden_states,)
         if self.output_attentions:
              SCREAMING_SNAKE_CASE					:			Tuple    	=       outputs + (all_attentions,)

         SCREAMING_SNAKE_CASE					:			int    	=       outputs + (all_highway_exits,)
         return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits






@add_start_docstrings(
    "The Bert Model transformer with early exiting (DeeBERT). "   ,    _SCREAMING_SNAKE_CASE   ,    )
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__ )							-> Optional[int]:
         super().__init__(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       config

         SCREAMING_SNAKE_CASE					:			str    	=       BertEmbeddings(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       DeeBertEncoder(lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       BertPooler(lowercase__ )

         self.init_weights()






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         self.encoder.init_highway_pooler(self.pooler )






    def       _UpperCamelCase    (       self )							-> int:
         return self.embeddings.word_embeddings






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       value






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Dict:
         for layer, heads in heads_to_prune.items():
              self.encoder.layer[layer].attention.prune_heads(lowercase__ )






    @add_start_docstrings_to_model_forward(lowercase__ )
    def       _UpperCamelCase    (       self    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							)							-> 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					:			Optional[Any]    	=       input_ids.size()
         elif inputs_embeds is not None:
              SCREAMING_SNAKE_CASE					:			Any    	=       inputs_embeds.size()[:-1]
         else:
              raise ValueError('You have to specify either input_ids or inputs_embeds' )

         SCREAMING_SNAKE_CASE					:			int    	=       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(lowercase__    ,							device=lowercase__ )
         if encoder_attention_mask is None:
              SCREAMING_SNAKE_CASE					:			Any    	=       torch.ones(lowercase__    ,							device=lowercase__ )
         if token_type_ids is None:
              SCREAMING_SNAKE_CASE					:			Any    	=       torch.zeros(lowercase__    ,							dtype=torch.long    ,							device=lowercase__ )

         # 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.
         SCREAMING_SNAKE_CASE					:			torch.Tensor    	=       self.get_extended_attention_mask(lowercase__    ,							lowercase__    ,							lowercase__ )

         # 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 encoder_attention_mask.dim() == 3:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       encoder_attention_mask[:, None, :, :]
         if encoder_attention_mask.dim() == 2:
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       encoder_attention_mask[:, None, None, :]

         SCREAMING_SNAKE_CASE					:			str    	=       encoder_extended_attention_mask.to(
             dtype=next(self.parameters() ).dtype )  # fp16 compatibility
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       (1.0 - encoder_extended_attention_mask) * -1_0_0_0_0.0

         # 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]
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.get_head_mask(lowercase__    ,							self.config.num_hidden_layers )

         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.embeddings(
             input_ids=lowercase__    ,							position_ids=lowercase__    ,							token_type_ids=lowercase__    ,							inputs_embeds=lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.encoder(
             lowercase__    ,							attention_mask=lowercase__    ,							head_mask=lowercase__    ,							encoder_hidden_states=lowercase__    ,							encoder_attention_mask=lowercase__    ,							)
         SCREAMING_SNAKE_CASE					:			str    	=       encoder_outputs[0]
         SCREAMING_SNAKE_CASE					:			Dict    	=       self.pooler(lowercase__ )

         SCREAMING_SNAKE_CASE					:			str    	=       (
             sequence_output,
             pooled_output,
         ) + encoder_outputs[
             1:
         ]  # add hidden_states and attentions if they are here
         return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__ )							-> Any:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       message
         SCREAMING_SNAKE_CASE					:			int    	=       exit_layer  # start from 1!






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__ )							-> Dict:
         super().__init__()
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       BertPooler(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       nn.Dropout(config.hidden_dropout_prob )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       nn.Linear(config.hidden_size    ,							config.num_labels )






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Optional[int]:
         # Pooler
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       encoder_outputs[0]
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.pooler(lowercase__ )
         # "return" pooler_output

         # BertModel
         SCREAMING_SNAKE_CASE					:			int    	=       (pooler_input, pooler_output) + encoder_outputs[1:]
         # "return" bmodel_output

         # Dropout and classification
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       bmodel_output[1]

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.dropout(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.classifier(lowercase__ )

         return logits, pooled_output






@add_start_docstrings(
    "Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. "   ,    _SCREAMING_SNAKE_CASE   ,    )
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__ )							-> Optional[int]:
         super().__init__(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Any    	=       config.num_labels
         SCREAMING_SNAKE_CASE					:			Tuple    	=       config.num_hidden_layers

         SCREAMING_SNAKE_CASE					:			Dict    	=       DeeBertModel(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       nn.Dropout(config.hidden_dropout_prob )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       nn.Linear(config.hidden_size    ,							self.config.num_labels )

         self.init_weights()






    @add_start_docstrings_to_model_forward(lowercase__ )
    def       _UpperCamelCase    (       self    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=-1    ,							lowercase__=False    ,							)							-> int:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.num_layers
         try:
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.bert(
                  lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__    ,							position_ids=lowercase__    ,							head_mask=lowercase__    ,							inputs_embeds=lowercase__    ,							)
              # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

              SCREAMING_SNAKE_CASE					:			Tuple    	=       outputs[1]

              SCREAMING_SNAKE_CASE					:			str    	=       self.dropout(lowercase__ )
              SCREAMING_SNAKE_CASE					:			int    	=       self.classifier(lowercase__ )
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       (logits,) + outputs[2:]  # add hidden states and attention if they are here
         except HighwayException as e:
              SCREAMING_SNAKE_CASE					:			int    	=       e.message
              SCREAMING_SNAKE_CASE					:			List[str]    	=       e.exit_layer
              SCREAMING_SNAKE_CASE					:			str    	=       outputs[0]

         if not self.training:
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       entropy(lowercase__ )
              SCREAMING_SNAKE_CASE					:			str    	=       []
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       []
         if labels is not None:
              if self.num_labels == 1:
                   #  We are doing regression
                   SCREAMING_SNAKE_CASE					:			Dict    	=       MSELoss()
                   SCREAMING_SNAKE_CASE					:			int    	=       loss_fct(logits.view(-1 )    ,							labels.view(-1 ) )
              else:
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       CrossEntropyLoss()
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       loss_fct(logits.view(-1    ,							self.num_labels )    ,							labels.view(-1 ) )

              # work with highway exits
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       []
              for highway_exit in outputs[-1]:
                   SCREAMING_SNAKE_CASE					:			Dict    	=       highway_exit[0]
                   if not self.training:
                        highway_logits_all.append(lowercase__ )
                        highway_entropy.append(highway_exit[2] )
                   if self.num_labels == 1:
                        #  We are doing regression
                        SCREAMING_SNAKE_CASE					:			str    	=       MSELoss()
                        SCREAMING_SNAKE_CASE					:			Any    	=       loss_fct(highway_logits.view(-1 )    ,							labels.view(-1 ) )
                   else:
                        SCREAMING_SNAKE_CASE					:			List[str]    	=       CrossEntropyLoss()
                        SCREAMING_SNAKE_CASE					:			Tuple    	=       loss_fct(highway_logits.view(-1    ,							self.num_labels )    ,							labels.view(-1 ) )
                   highway_losses.append(lowercase__ )

              if train_highway:
                   SCREAMING_SNAKE_CASE					:			List[Any]    	=       (sum(highway_losses[:-1] ),) + outputs
                   # exclude the final highway, of course
              else:
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       (loss,) + outputs
         if not self.training:
              SCREAMING_SNAKE_CASE					:			int    	=       outputs + ((original_entropy, highway_entropy), exit_layer)
              if output_layer >= 0:
                   SCREAMING_SNAKE_CASE					:			int    	=       (
                       (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
                   )  # use the highway of the last layer

         return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)




'''simple docstring'''





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


_lowerCAmelCase  :List[Any]  				=    False






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    pass






@nightly
@require_torch_gpu
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> Any:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Dict    	=       VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion' )
         # remove text_unet
         pipe.remove_unused_weights()
         pipe.to(lowercase__ )
         pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       'A painting of a squirrel eating a burger '
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       pipe(
             prompt=lowercase__    ,							generator=lowercase__    ,							guidance_scale=7.5    ,							num_inference_steps=2    ,							output_type='numpy' ).images

         with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(lowercase__ )
              SCREAMING_SNAKE_CASE					:			int    	=       VersatileDiffusionTextToImagePipeline.from_pretrained(lowercase__ )
         pipe.to(lowercase__ )
         pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[str]    	=       generator.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       pipe(
             prompt=lowercase__    ,							generator=lowercase__    ,							guidance_scale=7.5    ,							num_inference_steps=2    ,							output_type='numpy' ).images

         assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       VersatileDiffusionTextToImagePipeline.from_pretrained(
             'shi-labs/versatile-diffusion'    ,							torch_dtype=torch.floataa )
         pipe.to(lowercase__ )
         pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			int    	=       'A painting of a squirrel eating a burger '
         SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       pipe(
             prompt=lowercase__    ,							generator=lowercase__    ,							guidance_scale=7.5    ,							num_inference_steps=50    ,							output_type='numpy' ).images

         SCREAMING_SNAKE_CASE					:			Dict    	=       image[0, 253:256, 253:256, -1]

         assert image.shape == (1, 512, 512, 3)
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2



