






from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy


@add_start_docstrings(
    "The RoBERTa Model transformer with early exiting (DeeRoBERTa). "    ,  a_    ,  )
class   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       RobertaConfig
							_UpperCAmelCase     =       "roberta"
							def __init__(     self:      str       ,	UpperCamelCase:      Any						)		->		Union[str, Any]:
											super().__init__(UpperCamelCase						)

											snake_case__									=     RobertaEmbeddings(UpperCamelCase						)
											self.init_weights()


@add_start_docstrings(
    "RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n    also takes care of multi-layer training. "    ,  a_    ,  )
class   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       RobertaConfig
							_UpperCAmelCase     =       "roberta"
							def __init__(     self:      Any       ,	UpperCamelCase:      Any						)		->		List[Any]:
											super().__init__(UpperCamelCase						)
											snake_case__									=     config.num_labels
											snake_case__									=     config.num_hidden_layers

											snake_case__									=     DeeRobertaModel(UpperCamelCase						)
											snake_case__									=     nn.Dropout(config.hidden_dropout_prob						)
											snake_case__									=     nn.Linear(config.hidden_size       ,	self.config.num_labels						)


							@add_start_docstrings_to_model_forward(UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[Any]       ,	UpperCamelCase:      List[Any]=None       ,	UpperCamelCase:      Union[str, Any]=None       ,	UpperCamelCase:      Any=None       ,	UpperCamelCase:      Dict=None       ,	UpperCamelCase:      List[str]=None       ,	UpperCamelCase:      List[Any]=None       ,	UpperCamelCase:      Optional[int]=None       ,	UpperCamelCase:      str=-1       ,	UpperCamelCase:      List[Any]=False       ,	)		->		Tuple:
											snake_case__									=     self.num_layers
											try:
															snake_case__									=     self.roberta(
															    UpperCamelCase       ,	attention_mask=UpperCamelCase       ,	token_type_ids=UpperCamelCase       ,	position_ids=UpperCamelCase       ,	head_mask=UpperCamelCase       ,	inputs_embeds=UpperCamelCase       ,	)

															snake_case__									=     outputs[1]

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

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

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

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












from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase					:		Any	  =						logging.get_logger(__name__)

__UpperCamelCase					:		int	  =						{
    """junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json""",
    """junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json""",
    """junnyu/roformer_chinese_char_small""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"""
    ),
    """junnyu/roformer_chinese_char_base""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_discriminator""": (
        """https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_generator""": (
        """https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"""
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}


class   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       "roformer"
							def __init__(     self:      Tuple       ,	UpperCamelCase:      Optional[Any]=5_00_00       ,	UpperCamelCase:      str=None       ,	UpperCamelCase:      Any=7_68       ,	UpperCamelCase:      Dict=12       ,	UpperCamelCase:      List[Any]=12       ,	UpperCamelCase:      List[str]=30_72       ,	UpperCamelCase:      int="gelu"       ,	UpperCamelCase:      str=0.1       ,	UpperCamelCase:      Union[str, Any]=0.1       ,	UpperCamelCase:      Any=15_36       ,	UpperCamelCase:      Dict=2       ,	UpperCamelCase:      Dict=0.02       ,	UpperCamelCase:      List[str]=1e-12       ,	UpperCamelCase:      int=0       ,	UpperCamelCase:      Any=False       ,	UpperCamelCase:      int=True       ,	**UpperCamelCase:      List[Any]       ,	)		->		List[str]:
											super().__init__(pad_token_id=UpperCamelCase       ,	**UpperCamelCase						)

											snake_case__									=     vocab_size
											snake_case__									=     hidden_size if embedding_size is None else embedding_size
											snake_case__									=     hidden_size
											snake_case__									=     num_hidden_layers
											snake_case__									=     num_attention_heads
											snake_case__									=     hidden_act
											snake_case__									=     intermediate_size
											snake_case__									=     hidden_dropout_prob
											snake_case__									=     attention_probs_dropout_prob
											snake_case__									=     max_position_embeddings
											snake_case__									=     type_vocab_size
											snake_case__									=     initializer_range
											snake_case__									=     layer_norm_eps
											snake_case__									=     rotary_value
											snake_case__									=     use_cache


class   __SCREAMING_SNAKE_CASE(	a_	):
							@property
							def lowerCAmelCase_ (     self:      Any						)		->		Mapping[str, Mapping[int, str]]:
											if self.task == "multiple-choice":
															snake_case__									=     {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
															snake_case__									=     {0: 'batch', 1: 'sequence'}
											snake_case__									=     {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											        ('token_type_ids', dynamic_axis),
											    ]						)




