




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__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      RobertaConfig
    UpperCAmelCase__							  =      '''roberta'''


    def __init__(			self      :	Any					, UpperCAmelCase__      :	List[str])						->Dict:





           '''simple docstring'''

           super().__init__(UpperCAmelCase__)

           A__       =    RobertaEmbeddings(UpperCAmelCase__)
           self.init_weights()


@add_start_docstrings(
    '''RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. '''		,					UpperCAmelCase__		,					)
class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      RobertaConfig
    UpperCAmelCase__							  =      '''roberta'''


    def __init__(			self      :	Optional[Any]					, UpperCAmelCase__      :	Tuple)						->Dict:





           '''simple docstring'''

           super().__init__(UpperCAmelCase__)
           A__       =    config.num_labels
           A__       =    config.num_hidden_layers

           A__       =    DeeRobertaModel(UpperCAmelCase__)
           A__       =    nn.Dropout(config.hidden_dropout_prob)
           A__       =    nn.Linear(config.hidden_size					, self.config.num_labels)





    @add_start_docstrings_to_model_forward(UpperCAmelCase__)
    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	Tuple=None					, UpperCAmelCase__      :	Union[str, Any]=None					, UpperCAmelCase__      :	Optional[Any]=None					, UpperCAmelCase__      :	Dict=None					, UpperCAmelCase__      :	List[Any]=None					, UpperCAmelCase__      :	Optional[Any]=None					, UpperCAmelCase__      :	int=None					, UpperCAmelCase__      :	Dict=-1					, UpperCAmelCase__      :	Optional[int]=False					, )						->Optional[int]:





           '''simple docstring'''

           A__       =    self.num_layers
           try:
                  A__       =    self.roberta(
                      UpperCAmelCase__					, attention_mask=UpperCAmelCase__					, token_type_ids=UpperCAmelCase__					, position_ids=UpperCAmelCase__					, head_mask=UpperCAmelCase__					, inputs_embeds=UpperCAmelCase__					, )

                  A__       =    outputs[1]

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

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

                  # work with highway exits
                  A__       =    []
                  for highway_exit in outputs[-1]:
                         A__       =    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
                                A__       =    MSELoss()
                                A__       =    loss_fct(highway_logits.view(-1)					, labels.view(-1))
                         else:
                                A__       =    CrossEntropyLoss()
                                A__       =    loss_fct(highway_logits.view(-1					, self.num_labels)					, labels.view(-1))
                         highway_losses.append(UpperCAmelCase__)

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

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()






def        a_		(       __lowercase   :	Dict       ,					__lowercase   :	int       ,					__lowercase   :	Optional[Any]=None ) ->  Any:
			# set parameter of one layer
			assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
			_snake_case		=			nn.Parameter(__lowercase )
			if bias is not None:
						assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
						_snake_case		=			nn.Parameter(__lowercase )






def        a_		(       __lowercase   :	Any       ,					__lowercase   :	Dict       ,					__lowercase   :	Union[str, Any] ) ->  Optional[Any]:
			# set torch weights for 1-to-1 comparison
			_snake_case		=			np.asarray(weights[0] )
			_snake_case		=			np.asarray(weights[1] )
			_snake_case		=			np.asarray(weights[2] )

			set_param(
			    torch_layer.self_attention.query_key       ,					torch.tensor(__lowercase ).transpose(1       ,					2 ).contiguous().view(-1       ,					__lowercase )       ,					)
			set_param(
			    torch_layer.self_attention.value       ,					torch.tensor(__lowercase ).transpose(1       ,					2 ).contiguous().view(-1       ,					__lowercase )       ,					)
			set_param(
			    torch_layer.output.dense       ,					torch.tensor(__lowercase ).view(-1       ,					__lowercase ).contiguous().transpose(0       ,					1 )       ,					)






def        a_		(       __lowercase   :	str       ,					__lowercase   :	Tuple       ,					__lowercase   :	Any ) ->  Optional[Any]:
			# set torch weights for 1-to-1 comparison
			_snake_case		=			np.asarray(weights[0] )
			_snake_case		=			np.asarray(weights[1] )
			_snake_case		=			np.asarray(weights[2] )
			_snake_case		=			np.asarray(weights[3] )

			set_param(
			    torch_layer.self_attention.query       ,					torch.tensor(__lowercase ).transpose(1       ,					2 ).contiguous().view(-1       ,					__lowercase )       ,					)
			set_param(
			    torch_layer.self_attention.key       ,					torch.tensor(__lowercase ).transpose(1       ,					2 ).contiguous().view(-1       ,					__lowercase )       ,					)
			set_param(
			    torch_layer.self_attention.value       ,					torch.tensor(__lowercase ).transpose(1       ,					2 ).contiguous().view(-1       ,					__lowercase )       ,					)
			set_param(
			    torch_layer.output.dense       ,					torch.tensor(__lowercase ).view(-1       ,					__lowercase ).contiguous().transpose(0       ,					1 )       ,					)






def        a_		(       __lowercase   :	Dict       ,					__lowercase   :	List[str]       ,					__lowercase   :	Union[str, Any] ) ->  Optional[Any]:
			# layernorm 1
			_snake_case		=			weights[0][0][0]
			_snake_case		=			np.asarray(layer_norm_a[0] )
			_snake_case		=			np.asarray(layer_norm_a[1] )
			set_param(
			    torch_block.attention.layer_norm       ,					torch.tensor(__lowercase )       ,					torch.tensor(__lowercase )       ,					)

			# lsh weights + output
			_snake_case		=			weights[0][1]
			if len(__lowercase ) < 4:
						set_layer_weights_in_torch_lsh(__lowercase       ,					torch_block.attention       ,					__lowercase )
			else:
						set_layer_weights_in_torch_local(__lowercase       ,					torch_block.attention       ,					__lowercase )

			# intermediate weighs
			_snake_case		=			weights[2][0][1][2]

			# Chunked Feed Forward
			if len(__lowercase ) == 4:
						_snake_case		=			intermediate_weights[2]

			# layernorm 2
			_snake_case		=			np.asarray(intermediate_weights[0][0] )
			_snake_case		=			np.asarray(intermediate_weights[0][1] )
			set_param(
			    torch_block.feed_forward.layer_norm       ,					torch.tensor(__lowercase )       ,					torch.tensor(__lowercase )       ,					)

			# intermediate dense
			_snake_case		=			np.asarray(intermediate_weights[1][0] )
			_snake_case		=			np.asarray(intermediate_weights[1][1] )
			set_param(
			    torch_block.feed_forward.dense.dense       ,					torch.tensor(__lowercase ).transpose(0       ,					1 ).contiguous()       ,					torch.tensor(__lowercase )       ,					)

			# intermediate out
			_snake_case		=			np.asarray(intermediate_weights[4][0] )
			_snake_case		=			np.asarray(intermediate_weights[4][1] )
			set_param(
			    torch_block.feed_forward.output.dense       ,					torch.tensor(__lowercase ).transpose(0       ,					1 ).contiguous()       ,					torch.tensor(__lowercase )       ,					)






def        a_		(       __lowercase   :	Tuple       ,					__lowercase   :	Tuple       ,					__lowercase   :	Dict ) ->  Optional[int]:
			# reformer model
			_snake_case		=			torch_model.reformer

			# word embeds
			_snake_case		=			np.asarray(weights[1] )
			set_param(
			    torch_model_reformer.embeddings.word_embeddings       ,					torch.tensor(__lowercase )       ,					)

			if isinstance(weights[3]       ,					__lowercase ):
						_snake_case		=			torch_model_reformer.embeddings.position_embeddings
						for emb_idx in range(len(position_embeddings.weights ) ):
									_snake_case		=			np.asarray(weights[3][emb_idx][0] )
									assert (
									    position_embeddings.weights[emb_idx].shape == emb_weights.shape
									), f'''{position_embeddings[emb_idx]} emb does not match'''
									_snake_case		=			nn.Parameter(torch.tensor(__lowercase ) )

			_snake_case		=			weights[5]
			assert len(torch_model_reformer.encoder.layers ) * 4 == len(
			    __lowercase ), "HF and trax model do not have the same number of layers"
			for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
						_snake_case		=			trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
						set_block_weights_in_torch(__lowercase       ,					__lowercase       ,					__lowercase )

			# output layer norm
			_snake_case		=			np.asarray(weights[7][0] )
			_snake_case		=			np.asarray(weights[7][1] )
			set_param(
			    torch_model_reformer.encoder.layer_norm       ,					torch.tensor(__lowercase )       ,					torch.tensor(__lowercase )       ,					)

			# output embeddings
			_snake_case		=			np.asarray(weights[9][0] )
			_snake_case		=			np.asarray(weights[9][1] )
			set_param(
			    torch_model.lm_head.decoder       ,					torch.tensor(__lowercase ).transpose(0       ,					1 ).contiguous()       ,					torch.tensor(__lowercase )       ,					)






def        a_		(       __lowercase   :	Optional[Any]       ,					__lowercase   :	Dict       ,					__lowercase   :	List[Any] ) ->  Optional[int]:
			# Initialise PyTorch model
			_snake_case		=			ReformerConfig.from_json_file(__lowercase )
			print(f'''Building PyTorch model from configuration: {config}''' )
			_snake_case		=			ReformerModelWithLMHead(__lowercase )

			with open(__lowercase       ,					'rb' ) as f:
						_snake_case		=			pickle.load(__lowercase )['weights']

			set_model_weights_in_torch(__lowercase       ,					__lowercase       ,					config.hidden_size )

			# Save pytorch-model
			print(f'''Save PyTorch model to {pytorch_dump_path}''' )
			torch.save(model.state_dict()       ,					__lowercase )


if __name__ == "__main__":
			_lowerCamelCase       :				Union[str, Any]       =			argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained Reformer model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			_lowerCamelCase       :				List[Any]       =			parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)