




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    UpperCAmelCase_				( __UpperCAmelCase		:      Optional[Any]				)   ->					Optional[Any]:
      SCREAMING_SNAKE_CASE_      		=     torch.exp(__UpperCAmelCase				)
      SCREAMING_SNAKE_CASE_      		=     torch.sum(__UpperCAmelCase     ,     dim=1				)  # sum of exp(x_i)
      SCREAMING_SNAKE_CASE_      		=     torch.sum(x * exp_x     ,     dim=1				)  # sum of x_i * exp(x_i)
      return torch.log(__UpperCAmelCase				) - B / A





class 			lowerCamelCase_				( nn.Module   ):


 '''simple docstring'''







 def __init__(				self    :						Tuple							,							_lowerCAmelCase    :						List[Any]		):
       super().__init__()
       SCREAMING_SNAKE_CASE_      		=     config.output_attentions
       SCREAMING_SNAKE_CASE_      		=     config.output_hidden_states
       SCREAMING_SNAKE_CASE_      		=     nn.ModuleList([BertLayer(_lowerCAmelCase		) for _ in range(config.num_hidden_layers		)]		)
       SCREAMING_SNAKE_CASE_      		=     nn.ModuleList([BertHighway(_lowerCAmelCase		) for _ in range(config.num_hidden_layers		)]		)

       SCREAMING_SNAKE_CASE_      		=     [-1 for _ in range(config.num_hidden_layers		)]



 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						Optional[Any]		):
       if (type(_lowerCAmelCase		) is float) or (type(_lowerCAmelCase		) is int):
             for i in range(len(self.early_exit_entropy		)		):
                   SCREAMING_SNAKE_CASE_      		=     x
       else:
             SCREAMING_SNAKE_CASE_      		=     x



 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						List[Any]		):
       SCREAMING_SNAKE_CASE_      		=     pooler.state_dict()
       for highway in self.highway:
             for name, param in highway.pooler.state_dict().items():
                   param.copy_(loaded_model[name]		)




 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						Union[str, Any]=None							,							_lowerCAmelCase    :						Tuple=None							,							_lowerCAmelCase    :						List[Any]=None							,							_lowerCAmelCase    :						Optional[Any]=None							,							):
       SCREAMING_SNAKE_CASE_      		=     ()
       SCREAMING_SNAKE_CASE_      		=     ()
       SCREAMING_SNAKE_CASE_      		=     ()
       for i, layer_module in enumerate(self.layer		):
             if self.output_hidden_states:
                   SCREAMING_SNAKE_CASE_      		=     all_hidden_states + (hidden_states,)

             SCREAMING_SNAKE_CASE_      		=     layer_module(
                 _lowerCAmelCase							,							_lowerCAmelCase							,							head_mask[i]							,							_lowerCAmelCase							,							_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     layer_outputs[0]

             if self.output_attentions:
                   SCREAMING_SNAKE_CASE_      		=     all_attentions + (layer_outputs[1],)

             SCREAMING_SNAKE_CASE_      		=     (hidden_states,)
             if self.output_hidden_states:
                   SCREAMING_SNAKE_CASE_      		=     current_outputs + (all_hidden_states,)
             if self.output_attentions:
                   SCREAMING_SNAKE_CASE_      		=     current_outputs + (all_attentions,)

             SCREAMING_SNAKE_CASE_      		=     self.highway[i](_lowerCAmelCase		)
             # logits, pooled_output

             if not self.training:
                   SCREAMING_SNAKE_CASE_      		=     highway_exit[0]
                   SCREAMING_SNAKE_CASE_      		=     entropy(_lowerCAmelCase		)
                   SCREAMING_SNAKE_CASE_      		=     highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
                   SCREAMING_SNAKE_CASE_      		=     all_highway_exits + (highway_exit,)

                   if highway_entropy < self.early_exit_entropy[i]:
                         SCREAMING_SNAKE_CASE_      		=     (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
                         raise HighwayException(_lowerCAmelCase							,							i + 1		)
             else:
                   SCREAMING_SNAKE_CASE_      		=     all_highway_exits + (highway_exit,)

        # Add last layer
       if self.output_hidden_states:
             SCREAMING_SNAKE_CASE_      		=     all_hidden_states + (hidden_states,)

       SCREAMING_SNAKE_CASE_      		=     (hidden_states,)
       if self.output_hidden_states:
             SCREAMING_SNAKE_CASE_      		=     outputs + (all_hidden_states,)
       if self.output_attentions:
             SCREAMING_SNAKE_CASE_      		=     outputs + (all_attentions,)

       SCREAMING_SNAKE_CASE_      		=     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 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 def __init__(				self    :						List[Any]							,							_lowerCAmelCase    :						Tuple		):
       super().__init__(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     config

       SCREAMING_SNAKE_CASE_      		=     BertEmbeddings(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     DeeBertEncoder(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     BertPooler(_lowerCAmelCase		)

       self.init_weights()



 def  lowerCAmelCase_    (				self    :						int		):
       self.encoder.init_highway_pooler(self.pooler		)



 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       return self.embeddings.word_embeddings



 def  lowerCAmelCase_    (				self    :						Optional[Any]							,							_lowerCAmelCase    :						List[Any]		):
       SCREAMING_SNAKE_CASE_      		=     value



 def  lowerCAmelCase_    (				self    :						str							,							_lowerCAmelCase    :						Tuple		):
       for layer, heads in heads_to_prune.items():
             self.encoder.layer[layer].attention.prune_heads(_lowerCAmelCase		)




 @add_start_docstrings_to_model_forward(_lowerCAmelCase		)
 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						str=None							,							_lowerCAmelCase    :						int=None							,							_lowerCAmelCase    :						str=None							,							_lowerCAmelCase    :						str=None							,							_lowerCAmelCase    :						Tuple=None							,							_lowerCAmelCase    :						Dict=None							,							_lowerCAmelCase    :						int=None							,							_lowerCAmelCase    :						Any=None							,							):
       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_      		=     input_ids.size()
       elif inputs_embeds is not None:
             SCREAMING_SNAKE_CASE_      		=     inputs_embeds.size()[:-1]
       else:
             raise ValueError('You have to specify either input_ids or inputs_embeds'		)

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

       if attention_mask is None:
             SCREAMING_SNAKE_CASE_      		=     torch.ones(_lowerCAmelCase							,							device=_lowerCAmelCase		)
       if encoder_attention_mask is None:
             SCREAMING_SNAKE_CASE_      		=     torch.ones(_lowerCAmelCase							,							device=_lowerCAmelCase		)
       if token_type_ids is None:
             SCREAMING_SNAKE_CASE_      		=     torch.zeros(_lowerCAmelCase							,							dtype=torch.long							,							device=_lowerCAmelCase		)

       # 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_      		=     self.get_extended_attention_mask(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)

       # 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_      		=     encoder_attention_mask[:, None, :, :]
       if encoder_attention_mask.dim() == 2:
             SCREAMING_SNAKE_CASE_      		=     encoder_attention_mask[:, None, None, :]

       SCREAMING_SNAKE_CASE_      		=     encoder_extended_attention_mask.to(
           dtype=next(self.parameters()		).dtype		)  # fp16 compatibility
       SCREAMING_SNAKE_CASE_      		=     (1.0 - encoder_extended_attention_mask) * -1_0000.0

       # 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_      		=     self.get_head_mask(_lowerCAmelCase							,							self.config.num_hidden_layers		)

       SCREAMING_SNAKE_CASE_      		=     self.embeddings(
           input_ids=_lowerCAmelCase							,							position_ids=_lowerCAmelCase							,							token_type_ids=_lowerCAmelCase							,							inputs_embeds=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.encoder(
           _lowerCAmelCase							,							attention_mask=_lowerCAmelCase							,							head_mask=_lowerCAmelCase							,							encoder_hidden_states=_lowerCAmelCase							,							encoder_attention_mask=_lowerCAmelCase							,							)
       SCREAMING_SNAKE_CASE_      		=     encoder_outputs[0]
       SCREAMING_SNAKE_CASE_      		=     self.pooler(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     (
           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 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 def __init__(				self    :						Dict							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						str		):
       SCREAMING_SNAKE_CASE_      		=     message
       SCREAMING_SNAKE_CASE_      		=     exit_layer  # start from 1!





class 			lowerCamelCase_				( nn.Module   ):


 '''simple docstring'''







 def __init__(				self    :						List[Any]							,							_lowerCAmelCase    :						int		):
       super().__init__()
       SCREAMING_SNAKE_CASE_      		=     BertPooler(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     nn.Dropout(config.hidden_dropout_prob		)
       SCREAMING_SNAKE_CASE_      		=     nn.Linear(config.hidden_size							,							config.num_labels		)




 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						Optional[Any]		):
       # Pooler
       SCREAMING_SNAKE_CASE_      		=     encoder_outputs[0]
       SCREAMING_SNAKE_CASE_      		=     self.pooler(_lowerCAmelCase		)
       # "return" pooler_output

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

       # Dropout and classification
       SCREAMING_SNAKE_CASE_      		=     bmodel_output[1]

       SCREAMING_SNAKE_CASE_      		=     self.dropout(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.classifier(_lowerCAmelCase		)

       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 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 def __init__(				self    :						Tuple							,							_lowerCAmelCase    :						List[str]		):
       super().__init__(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     config.num_labels
       SCREAMING_SNAKE_CASE_      		=     config.num_hidden_layers

       SCREAMING_SNAKE_CASE_      		=     DeeBertModel(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     nn.Dropout(config.hidden_dropout_prob		)
       SCREAMING_SNAKE_CASE_      		=     nn.Linear(config.hidden_size							,							self.config.num_labels		)

       self.init_weights()




 @add_start_docstrings_to_model_forward(_lowerCAmelCase		)
 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						int=None							,							_lowerCAmelCase    :						List[str]=None							,							_lowerCAmelCase    :						Optional[Any]=None							,							_lowerCAmelCase    :						List[Any]=None							,							_lowerCAmelCase    :						List[str]=None							,							_lowerCAmelCase    :						int=None							,							_lowerCAmelCase    :						List[Any]=None							,							_lowerCAmelCase    :						List[str]=-1							,							_lowerCAmelCase    :						List[str]=False							,							):
       SCREAMING_SNAKE_CASE_      		=     self.num_layers
       try:
             SCREAMING_SNAKE_CASE_      		=     self.bert(
                 _lowerCAmelCase							,							attention_mask=_lowerCAmelCase							,							token_type_ids=_lowerCAmelCase							,							position_ids=_lowerCAmelCase							,							head_mask=_lowerCAmelCase							,							inputs_embeds=_lowerCAmelCase							,							)
             # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

             SCREAMING_SNAKE_CASE_      		=     outputs[1]

             SCREAMING_SNAKE_CASE_      		=     self.dropout(_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     self.classifier(_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     (logits,) + outputs[2:]  # add hidden states and attention if they are here
       except HighwayException as e:
             SCREAMING_SNAKE_CASE_      		=     e.message
             SCREAMING_SNAKE_CASE_      		=     e.exit_layer
             SCREAMING_SNAKE_CASE_      		=     outputs[0]

       if not self.training:
             SCREAMING_SNAKE_CASE_      		=     entropy(_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     []
             SCREAMING_SNAKE_CASE_      		=     []
       if labels is not None:
             if self.num_labels == 1:
                   #  We are doing regression
                   SCREAMING_SNAKE_CASE_      		=     MSELoss()
                   SCREAMING_SNAKE_CASE_      		=     loss_fct(logits.view(-1		)							,							labels.view(-1		)		)
             else:
                   SCREAMING_SNAKE_CASE_      		=     CrossEntropyLoss()
                   SCREAMING_SNAKE_CASE_      		=     loss_fct(logits.view(-1							,							self.num_labels		)							,							labels.view(-1		)		)

             # work with highway exits
             SCREAMING_SNAKE_CASE_      		=     []
             for highway_exit in outputs[-1]:
                   SCREAMING_SNAKE_CASE_      		=     highway_exit[0]
                   if not self.training:
                         highway_logits_all.append(_lowerCAmelCase		)
                         highway_entropy.append(highway_exit[2]		)
                   if self.num_labels == 1:
                         #  We are doing regression
                         SCREAMING_SNAKE_CASE_      		=     MSELoss()
                         SCREAMING_SNAKE_CASE_      		=     loss_fct(highway_logits.view(-1		)							,							labels.view(-1		)		)
                   else:
                         SCREAMING_SNAKE_CASE_      		=     CrossEntropyLoss()
                         SCREAMING_SNAKE_CASE_      		=     loss_fct(highway_logits.view(-1							,							self.num_labels		)							,							labels.view(-1		)		)
                   highway_losses.append(_lowerCAmelCase		)

             if train_highway:
                   SCREAMING_SNAKE_CASE_      		=     (sum(highway_losses[:-1]		),) + outputs
                   # exclude the final highway, of course
             else:
                   SCREAMING_SNAKE_CASE_      		=     (loss,) + outputs
       if not self.training:
             SCREAMING_SNAKE_CASE_      		=     outputs + ((original_entropy, highway_entropy), exit_layer)
             if output_layer >= 0:
                   SCREAMING_SNAKE_CASE_      		=     (
                       (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 os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


__UpperCamelCase					    =	HUGGINGFACE_HUB_CACHE


__UpperCamelCase					    =	"config.json"
__UpperCamelCase					    =	"diffusion_pytorch_model.bin"
__UpperCamelCase					    =	"diffusion_flax_model.msgpack"
__UpperCamelCase					    =	"model.onnx"
__UpperCamelCase					    =	"diffusion_pytorch_model.safetensors"
__UpperCamelCase					    =	"weights.pb"
__UpperCamelCase					    =	"https://huggingface.co"
__UpperCamelCase					    =	default_cache_path
__UpperCamelCase					    =	"diffusers_modules"
__UpperCamelCase					    =	os.getenv("HF_MODULES_CACHE", os.path.join(hf_cache_home, "modules"))
__UpperCamelCase					    =	["fp16", "non-ema"]
__UpperCamelCase					    =	".self_attn"


