




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						(							lowerCamelCase_ :     Optional[int]  )       ->  Optional[int]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Any					  =      torch.exp(_lowercase  )
			SCREAMING_SNAKE_CASE_   : Any					  =      torch.sum(_lowercase  ,							dim=1  )  # sum of exp(x_i)
			SCREAMING_SNAKE_CASE_   : Tuple					  =      torch.sum(x * exp_x  ,							dim=1  )  # sum of x_i * exp(x_i)
			return torch.log(_lowercase  ) - B / A







class 				lowerCAmelCase_       (					nn.Module							):






				def __init__(			self      ,snake_case__ ):
							super().__init__()
							SCREAMING_SNAKE_CASE_   : Dict					  =      config.output_attentions
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      config.output_hidden_states
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      nn.ModuleList([BertLayer(lowerCAmelCase__ ) for _ in range(config.num_hidden_layers )] )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      nn.ModuleList([BertHighway(lowerCAmelCase__ ) for _ in range(config.num_hidden_layers )] )

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






				def 		snake_case	(			self      ,snake_case__ ):
							if (type(lowerCAmelCase__ ) is float) or (type(lowerCAmelCase__ ) is int):
										for i in range(len(self.early_exit_entropy ) ):
													SCREAMING_SNAKE_CASE_   : Any					  =      x
							else:
										SCREAMING_SNAKE_CASE_   : Tuple					  =      x






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






				def 		snake_case	(			self      ,snake_case__      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      ()
							SCREAMING_SNAKE_CASE_   : str					  =      ()
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      ()
							for i, layer_module in enumerate(self.layer ):
										if self.output_hidden_states:
													SCREAMING_SNAKE_CASE_   : str					  =      all_hidden_states + (hidden_states,)

										SCREAMING_SNAKE_CASE_   : List[Any]					  =      layer_module(
										    lowerCAmelCase__      ,lowerCAmelCase__      ,head_mask[i]      ,lowerCAmelCase__      ,lowerCAmelCase__ )
										SCREAMING_SNAKE_CASE_   : str					  =      layer_outputs[0]

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

										SCREAMING_SNAKE_CASE_   : Dict					  =      (hidden_states,)
										if self.output_hidden_states:
													SCREAMING_SNAKE_CASE_   : List[str]					  =      current_outputs + (all_hidden_states,)
										if self.output_attentions:
													SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      current_outputs + (all_attentions,)

										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.highway[i](lowerCAmelCase__ )
										# logits, pooled_output

										if not self.training:
													SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      highway_exit[0]
													SCREAMING_SNAKE_CASE_   : Tuple					  =      entropy(lowerCAmelCase__ )
													SCREAMING_SNAKE_CASE_   : int					  =      highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
													SCREAMING_SNAKE_CASE_   : Dict					  =      all_highway_exits + (highway_exit,)

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

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

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      (hidden_states,)
							if self.output_hidden_states:
										SCREAMING_SNAKE_CASE_   : Any					  =      outputs + (all_hidden_states,)
							if self.output_attentions:
										SCREAMING_SNAKE_CASE_   : List[str]					  =      outputs + (all_attentions,)

							SCREAMING_SNAKE_CASE_   : Dict					  =      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). "						,     a__						,     )
class 				lowerCAmelCase_       (					a__							):






				def __init__(			self      ,snake_case__ ):
							super().__init__(lowerCAmelCase__ )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      config

							SCREAMING_SNAKE_CASE_   : Tuple					  =      BertEmbeddings(lowerCAmelCase__ )
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      DeeBertEncoder(lowerCAmelCase__ )
							SCREAMING_SNAKE_CASE_   : Any					  =      BertPooler(lowerCAmelCase__ )

							self.init_weights()






				def 		snake_case	(			self ):
							self.encoder.init_highway_pooler(self.pooler )






				def 		snake_case	(			self ):
							return self.embeddings.word_embeddings






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      value






				def 		snake_case	(			self      ,snake_case__ ):
							for layer, heads in heads_to_prune.items():
										self.encoder.layer[layer].attention.prune_heads(lowerCAmelCase__ )






				@add_start_docstrings_to_model_forward(lowerCAmelCase__ )
				def 		snake_case	(			self      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=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_   : Optional[Any]					  =      input_ids.size()
							elif inputs_embeds is not None:
										SCREAMING_SNAKE_CASE_   : str					  =      inputs_embeds.size()[:-1]
							else:
										raise ValueError('You have to specify either input_ids or inputs_embeds' )

							SCREAMING_SNAKE_CASE_   : Tuple					  =      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 encoder_attention_mask is None:
										SCREAMING_SNAKE_CASE_   : int					  =      torch.ones(lowerCAmelCase__      ,device=lowerCAmelCase__ )
							if token_type_ids is None:
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      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_   : torch.Tensor					  =      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_   : Optional[Any]					  =      encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.dim() == 2:
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      encoder_attention_mask[:, None, None, :]

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      encoder_extended_attention_mask.to(
							    dtype=next(self.parameters() ).dtype )  # fp16 compatibility
							SCREAMING_SNAKE_CASE_   : List[str]					  =      (1.0 - encoder_extended_attention_mask) * -10000.0

							# 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_   : Optional[int]					  =      self.get_head_mask(lowerCAmelCase__      ,self.config.num_hidden_layers )

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

							SCREAMING_SNAKE_CASE_   : List[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 				lowerCAmelCase_       (					a__							):






				def __init__(			self      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      message
							SCREAMING_SNAKE_CASE_   : int					  =      exit_layer  # start from 1!







class 				lowerCAmelCase_       (					nn.Module							):






				def __init__(			self      ,snake_case__ ):
							super().__init__()
							SCREAMING_SNAKE_CASE_   : Tuple					  =      BertPooler(lowerCAmelCase__ )
							SCREAMING_SNAKE_CASE_   : Any					  =      nn.Dropout(config.hidden_dropout_prob )
							SCREAMING_SNAKE_CASE_   : Any					  =      nn.Linear(config.hidden_size      ,config.num_labels )






				def 		snake_case	(			self      ,snake_case__ ):
							# Pooler
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      encoder_outputs[0]
							SCREAMING_SNAKE_CASE_   : str					  =      self.pooler(lowerCAmelCase__ )
							# "return" pooler_output

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

							# Dropout and classification
							SCREAMING_SNAKE_CASE_   : str					  =      bmodel_output[1]

							SCREAMING_SNAKE_CASE_   : Tuple					  =      self.dropout(lowerCAmelCase__ )
							SCREAMING_SNAKE_CASE_   : List[str]					  =      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. "						,     a__						,     )
class 				lowerCAmelCase_       (					a__							):






				def __init__(			self      ,snake_case__ ):
							super().__init__(lowerCAmelCase__ )
							SCREAMING_SNAKE_CASE_   : str					  =      config.num_labels
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      config.num_hidden_layers

							SCREAMING_SNAKE_CASE_   : str					  =      DeeBertModel(lowerCAmelCase__ )
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      nn.Dropout(config.hidden_dropout_prob )
							SCREAMING_SNAKE_CASE_   : int					  =      nn.Linear(config.hidden_size      ,self.config.num_labels )

							self.init_weights()






				@add_start_docstrings_to_model_forward(lowerCAmelCase__ )
				def 		snake_case	(			self      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=-1      ,snake_case__=False      ,):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.num_layers
							try:
										SCREAMING_SNAKE_CASE_   : List[str]					  =      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_   : Union[str, Any]					  =      outputs[1]

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

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

										# work with highway exits
										SCREAMING_SNAKE_CASE_   : Tuple					  =      []
										for highway_exit in outputs[-1]:
													SCREAMING_SNAKE_CASE_   : Tuple					  =      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_   : Dict					  =      MSELoss()
																SCREAMING_SNAKE_CASE_   : Optional[int]					  =      loss_fct(highway_logits.view(-1 )      ,labels.view(-1 ) )
													else:
																SCREAMING_SNAKE_CASE_   : Tuple					  =      CrossEntropyLoss()
																SCREAMING_SNAKE_CASE_   : Any					  =      loss_fct(highway_logits.view(-1      ,self.num_labels )      ,labels.view(-1 ) )
													highway_losses.append(lowerCAmelCase__ )

										if train_highway:
													SCREAMING_SNAKE_CASE_   : str					  =      (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_   : Tuple					  =      outputs + ((original_entropy, highway_entropy), exit_layer)
										if output_layer >= 0:
													SCREAMING_SNAKE_CASE_   : Tuple					  =      (
													    (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 unittest

from transformers import DonutProcessor


a		:					Optional[int]	      =     '''naver-clova-ix/donut-base'''


class 			__UpperCamelCase							(		unittest.TestCase		):






							def        __a  (  self )      ->					Dict:
								a :						Optional[Any]      =							DonutProcessor.from_pretrained(lowerCAmelCase__ )






							def        __a  (  self )      ->					List[Any]:
								a :						Optional[Any]      =							{
								    "name": "John Doe",
								    "age": "99",
								    "city": "Atlanta",
								    "state": "GA",
								    "zip": "30301",
								    "phone": "123-4567",
								    "nicknames": [{"nickname": "Johnny"}, {"nickname": "JD"}],
								}

								a :						Tuple      =							(
								    "<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>"
								    "<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>"
								    "<s_nicknames><s_nickname>Johnny</s_nickname>"
								    "<sep/><s_nickname>JD</s_nickname></s_nicknames>"
								)
								a :						List[Any]      =							self.processor.tokenajson(lowerCAmelCase__ )

								self.assertDictEqual(lowerCAmelCase__ ,   lowerCAmelCase__ )


