





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). "				,  UpperCamelCase__				,  )
class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					RobertaConfig
							lowerCamelCase_           =					"roberta"


							def __init__(							self    :Optional[Any]							,				__A    :List[Any]		)   ->   Tuple:



									"""simple docstring"""



									super().__init__(__A		)

									SCREAMING_SNAKE_CASE__			=      RobertaEmbeddings(__A		)
									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. "				,  UpperCamelCase__				,  )
class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					RobertaConfig
							lowerCamelCase_           =					"roberta"


							def __init__(							self    :Optional[Any]							,				__A    :Union[str, Any]		)   ->   Any:



									"""simple docstring"""



									super().__init__(__A		)
									SCREAMING_SNAKE_CASE__			=      config.num_labels
									SCREAMING_SNAKE_CASE__			=      config.num_hidden_layers

									SCREAMING_SNAKE_CASE__			=      DeeRobertaModel(__A		)
									SCREAMING_SNAKE_CASE__			=      nn.Dropout(config.hidden_dropout_prob		)
									SCREAMING_SNAKE_CASE__			=      nn.Linear(config.hidden_size							,				self.config.num_labels		)


							@add_start_docstrings_to_model_forward(__A		)
							def      _snake_case  (							self    :Union[str, Any]							,				__A    :int=None							,				__A    :str=None							,				__A    :List[Any]=None							,				__A    :Any=None							,				__A    :List[Any]=None							,				__A    :Optional[int]=None							,				__A    :List[str]=None							,				__A    :Union[str, Any]=-1							,				__A    :int=False							,				)   ->   List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.num_layers
									try:
											SCREAMING_SNAKE_CASE__			=      self.roberta(
											    __A							,				attention_mask=__A							,				token_type_ids=__A							,				position_ids=__A							,				head_mask=__A							,				inputs_embeds=__A							,				)

											SCREAMING_SNAKE_CASE__			=      outputs[1]

											SCREAMING_SNAKE_CASE__			=      self.dropout(__A		)
											SCREAMING_SNAKE_CASE__			=      self.classifier(__A		)
											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(__A		)
											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(__A		)
															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(__A		)

											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), entropy













import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							str					,   UpperCamelCase__:							str					,   UpperCamelCase__:							str    ):
		def get_masked_lm_array(UpperCamelCase__:							str    ):
				SCREAMING_SNAKE_CASE__			=      f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				SCREAMING_SNAKE_CASE__			=      tf.train.load_variable(UpperCamelCase__					,   UpperCamelCase__    )

				if "kernel" in name:
						SCREAMING_SNAKE_CASE__			=      array.transpose()

				return torch.from_numpy(UpperCamelCase__    )

		def get_encoder_array(UpperCamelCase__:							str    ):
				SCREAMING_SNAKE_CASE__			=      f'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				SCREAMING_SNAKE_CASE__			=      tf.train.load_variable(UpperCamelCase__					,   UpperCamelCase__    )

				if "kernel" in name:
						SCREAMING_SNAKE_CASE__			=      array.transpose()

				return torch.from_numpy(UpperCamelCase__    )

		def get_encoder_layer_array(UpperCamelCase__:							int					,   UpperCamelCase__:							str    ):
				SCREAMING_SNAKE_CASE__			=      f'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				SCREAMING_SNAKE_CASE__			=      tf.train.load_variable(UpperCamelCase__					,   UpperCamelCase__    )

				if "kernel" in name:
						SCREAMING_SNAKE_CASE__			=      array.transpose()

				return torch.from_numpy(UpperCamelCase__    )

		def get_encoder_attention_layer_array(UpperCamelCase__:							int					,   UpperCamelCase__:							str					,   UpperCamelCase__:							Any    ):
				SCREAMING_SNAKE_CASE__			=      f'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				SCREAMING_SNAKE_CASE__			=      tf.train.load_variable(UpperCamelCase__					,   UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      array.reshape(UpperCamelCase__    )

				if "kernel" in name:
						SCREAMING_SNAKE_CASE__			=      array.transpose()

				return torch.from_numpy(UpperCamelCase__    )

		print(f'''Loading model based on config from {config_path}...'''    )
		SCREAMING_SNAKE_CASE__			=      BertConfig.from_json_file(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      BertForMaskedLM(UpperCamelCase__    )

		# Layers
		for layer_index in range(0					,   config.num_hidden_layers    ):
				SCREAMING_SNAKE_CASE__			=      model.bert.encoder.layer[layer_index]

				# Self-attention
				SCREAMING_SNAKE_CASE__			=      layer.attention.self

				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_query_dense/kernel"""					,   self_attn.query.weight.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_query_dense/bias"""					,   self_attn.query.bias.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_key_dense/kernel"""					,   self_attn.key.weight.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_key_dense/bias"""					,   self_attn.key.bias.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_value_dense/kernel"""					,   self_attn.value.weight.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_value_dense/bias"""					,   self_attn.value.bias.data.shape    )

				# Self-attention Output
				SCREAMING_SNAKE_CASE__			=      layer.attention.output

				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_output_dense/kernel"""					,   self_output.dense.weight.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_output_dense/bias"""					,   self_output.dense.bias.data.shape    )

				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_attention_layer_norm/gamma"""    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_attention_layer_norm/beta"""    )

				# Intermediate
				SCREAMING_SNAKE_CASE__			=      layer.intermediate

				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_intermediate_dense/kernel"""    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_intermediate_dense/bias"""    )

				# Output
				SCREAMING_SNAKE_CASE__			=      layer.output

				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_output_dense/kernel"""    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_output_dense/bias"""    )

				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_output_layer_norm/gamma"""    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_output_layer_norm/beta"""    )

		# Embeddings
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_position_embedding_layer/embeddings"""    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_type_embedding_layer/embeddings"""    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_embedding_norm_layer/gamma"""    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_embedding_norm_layer/beta"""    )

		# LM Head
		SCREAMING_SNAKE_CASE__			=      model.cls.predictions.transform

		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""dense/kernel"""    )
		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""dense/bias"""    )

		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""layer_norm/gamma"""    )
		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""layer_norm/beta"""    )

		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""embedding_table"""    )

		# Pooling
		SCREAMING_SNAKE_CASE__			=      BertPooler(config=UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_pooler_layer/kernel"""    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_pooler_layer/bias"""    )

		# Export final model
		model.save_pretrained(UpperCamelCase__    )

		# Integration test - should load without any errors ;)
		SCREAMING_SNAKE_CASE__			=      BertForMaskedLM.from_pretrained(UpperCamelCase__    )
		print(new_model.eval()    )

		print("""Model conversion was done sucessfully!"""    )


if __name__ == "__main__":
	_lowerCamelCase   				=			argparse.ArgumentParser()
	parser.add_argument(
	    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow Token Dropping checkpoint path.'
	)
	parser.add_argument(
	    '--bert_config_file',
	    type=str,
	    required=True,
	    help='The config json file corresponding to the BERT model. This specifies the model architecture.',
	)
	parser.add_argument(
	    '--pytorch_dump_path',
	    type=str,
	    required=True,
	    help='Path to the output PyTorch model.',
	)
	_lowerCamelCase   				=			parser.parse_args()
	convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)