





'''simple docstring'''






import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

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


if version.parse(nlp.__version__) != version.parse('0.8.3'):
      raise Exception('requires gluonnlp == 0.8.3')

if version.parse(mx.__version__) != version.parse('1.5.0'):
      raise Exception('requires mxnet == 1.5.0')

logging.set_verbosity_info()
__lowerCamelCase						: List[str]		=   logging.get_logger(__name__)

__lowerCamelCase						: List[str]		=   'The Nymphenburg Palace is a beautiful palace in Munich!'


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			={
      '''attention_cell''': '''multi_head''',
      '''num_layers''': 4,
      '''units''': 1024,
      '''hidden_size''': 768,
      '''max_length''': 512,
      '''num_heads''': 8,
      '''scaled''': True,
      '''dropout''': 0.1,
      '''use_residual''': True,
      '''embed_size''': 1024,
      '''embed_dropout''': 0.1,
      '''word_embed''': None,
      '''layer_norm_eps''': 1E-5,
      '''token_type_vocab_size''': 2,
  }

  _UpperCamelCase       			=bort_4_8_768_1024_hparams

  # Let's construct the original Bort model here
  # Taken from official BERT implementation, see:
  # https://github.com/alexa/bort/blob/master/bort/bort.py
  _UpperCamelCase       			=BERTEncoder(
      attention_cell=predefined_args['''attention_cell''']  ,      num_layers=predefined_args['''num_layers''']  ,      units=predefined_args['''units''']  ,      hidden_size=predefined_args['''hidden_size''']  ,      max_length=predefined_args['''max_length''']  ,      num_heads=predefined_args['''num_heads''']  ,      scaled=predefined_args['''scaled''']  ,      dropout=predefined_args['''dropout''']  ,      output_attention=__SCREAMING_SNAKE_CASE  ,      output_all_encodings=__SCREAMING_SNAKE_CASE  ,      use_residual=predefined_args['''use_residual''']  ,      activation=predefined_args.get('''activation'''  ,      '''gelu'''   )  ,      layer_norm_eps=predefined_args.get('''layer_norm_eps'''  ,      __SCREAMING_SNAKE_CASE   )  ,      )

  # Vocab information needs to be fetched first
  # It's the same as RoBERTa, so RobertaTokenizer can be used later
  _UpperCamelCase       			='''openwebtext_ccnews_stories_books_cased'''

  # Specify download folder to Gluonnlp's vocab
  _UpperCamelCase       			=os.path.join(get_home_dir()  ,      '''models'''   )
  _UpperCamelCase       			=_load_vocab(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      cls=__SCREAMING_SNAKE_CASE   )

  _UpperCamelCase       			=nlp.model.BERTModel(
      __SCREAMING_SNAKE_CASE  ,      len(__SCREAMING_SNAKE_CASE   )  ,      units=predefined_args['''units''']  ,      embed_size=predefined_args['''embed_size''']  ,      embed_dropout=predefined_args['''embed_dropout''']  ,      word_embed=predefined_args['''word_embed''']  ,      use_pooler=__SCREAMING_SNAKE_CASE  ,      use_token_type_embed=__SCREAMING_SNAKE_CASE  ,      token_type_vocab_size=predefined_args['''token_type_vocab_size''']  ,      use_classifier=__SCREAMING_SNAKE_CASE  ,      use_decoder=__SCREAMING_SNAKE_CASE  ,      )

  original_bort.load_parameters(__SCREAMING_SNAKE_CASE  ,      cast_dtype=__SCREAMING_SNAKE_CASE  ,      ignore_extra=__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=original_bort._collect_params_with_prefix()

  # Build our config 🤗
  _UpperCamelCase       			={
      '''architectures''': ['''BertForMaskedLM'''],
      '''attention_probs_dropout_prob''': predefined_args['''dropout'''],
      '''hidden_act''': '''gelu''',
      '''hidden_dropout_prob''': predefined_args['''dropout'''],
      '''hidden_size''': predefined_args['''embed_size'''],
      '''initializer_range''': 0.0_2,
      '''intermediate_size''': predefined_args['''hidden_size'''],
      '''layer_norm_eps''': predefined_args['''layer_norm_eps'''],
      '''max_position_embeddings''': predefined_args['''max_length'''],
      '''model_type''': '''bort''',
      '''num_attention_heads''': predefined_args['''num_heads'''],
      '''num_hidden_layers''': predefined_args['''num_layers'''],
      '''pad_token_id''': 1,  # 2 = BERT, 1 = RoBERTa
      '''type_vocab_size''': 1,  # 2 = BERT, 1 = RoBERTa
      '''vocab_size''': len(__SCREAMING_SNAKE_CASE   ),
  }

  _UpperCamelCase       			=BertConfig.from_dict(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=BertForMaskedLM(__SCREAMING_SNAKE_CASE   )
  hf_bort_model.eval()

  # Parameter mapping table (Gluonnlp to Transformers)
  # * denotes layer index
  #
  # | Gluon Parameter                                                | Transformers Parameter
  # | -------------------------------------------------------------- | ----------------------
  # | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
  # | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
  # | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
  # | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
  # | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
  # | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
  # | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
  # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
  # | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
  # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
  # | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
  # | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
  # | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
  # | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
  # | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
  # | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
  # | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
  # | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
  # | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
  # | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

  # Helper function to convert MXNET Arrays to PyTorch
  def to_torch(__SCREAMING_SNAKE_CASE   ) -> nn.Parameter:
    return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()   )   )

  # Check param shapes and map new HF param back
  def check_and_map_params(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):
    _UpperCamelCase       			=hf_param.shape

    _UpperCamelCase       			=to_torch(params[gluon_param]   )
    _UpperCamelCase       			=gluon_param.shape

    assert (
        shape_hf == shape_gluon
    ), f'''The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'''

    return gluon_param

  _UpperCamelCase       			=check_and_map_params(
      hf_bort_model.bert.embeddings.word_embeddings.weight  ,      '''word_embed.0.weight'''   )
  _UpperCamelCase       			=check_and_map_params(
      hf_bort_model.bert.embeddings.position_embeddings.weight  ,      '''encoder.position_weight'''   )
  _UpperCamelCase       			=check_and_map_params(
      hf_bort_model.bert.embeddings.LayerNorm.bias  ,      '''encoder.layer_norm.beta'''   )
  _UpperCamelCase       			=check_and_map_params(
      hf_bort_model.bert.embeddings.LayerNorm.weight  ,      '''encoder.layer_norm.gamma'''   )

  # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
  _UpperCamelCase       			=torch.zeros_like(
      hf_bort_model.bert.embeddings.token_type_embeddings.weight.data   )

  for i in range(hf_bort_config.num_hidden_layers   ):
    _UpperCamelCase       			=hf_bort_model.bert.encoder.layer[i]

    # self attention
    _UpperCamelCase       			=layer.attention.self

    _UpperCamelCase       			=check_and_map_params(
        self_attn.key.bias.data  ,      f'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias'''   )

    _UpperCamelCase       			=check_and_map_params(
        self_attn.key.weight.data  ,      f'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight'''   )
    _UpperCamelCase       			=check_and_map_params(
        self_attn.query.bias.data  ,      f'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias'''   )
    _UpperCamelCase       			=check_and_map_params(
        self_attn.query.weight.data  ,      f'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight'''   )
    _UpperCamelCase       			=check_and_map_params(
        self_attn.value.bias.data  ,      f'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias'''   )
    _UpperCamelCase       			=check_and_map_params(
        self_attn.value.weight.data  ,      f'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight'''   )

    # self attention output
    _UpperCamelCase       			=layer.attention.output

    _UpperCamelCase       			=check_and_map_params(
        self_output.dense.bias  ,      f'''encoder.transformer_cells.{i}.proj.bias'''   )
    _UpperCamelCase       			=check_and_map_params(
        self_output.dense.weight  ,      f'''encoder.transformer_cells.{i}.proj.weight'''   )
    _UpperCamelCase       			=check_and_map_params(
        self_output.LayerNorm.bias  ,      f'''encoder.transformer_cells.{i}.layer_norm.beta'''   )
    _UpperCamelCase       			=check_and_map_params(
        self_output.LayerNorm.weight  ,      f'''encoder.transformer_cells.{i}.layer_norm.gamma'''   )

    # intermediate
    _UpperCamelCase       			=layer.intermediate

    _UpperCamelCase       			=check_and_map_params(
        intermediate.dense.bias  ,      f'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''   )
    _UpperCamelCase       			=check_and_map_params(
        intermediate.dense.weight  ,      f'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''   )

    # output
    _UpperCamelCase       			=layer.output

    _UpperCamelCase       			=check_and_map_params(
        bert_output.dense.bias  ,      f'''encoder.transformer_cells.{i}.ffn.ffn_2.bias'''   )
    _UpperCamelCase       			=check_and_map_params(
        bert_output.dense.weight  ,      f'''encoder.transformer_cells.{i}.ffn.ffn_2.weight'''   )
    _UpperCamelCase       			=check_and_map_params(
        bert_output.LayerNorm.bias  ,      f'''encoder.transformer_cells.{i}.ffn.layer_norm.beta'''   )
    _UpperCamelCase       			=check_and_map_params(
        bert_output.LayerNorm.weight  ,      f'''encoder.transformer_cells.{i}.ffn.layer_norm.gamma'''   )

  # Save space and energy 🎄
  hf_bort_model.half()

  # Compare output of both models
  _UpperCamelCase       			=RobertaTokenizer.from_pretrained('''roberta-base'''   )

  _UpperCamelCase       			=tokenizer.encode_plus(__SCREAMING_SNAKE_CASE   )['''input_ids''']

  # Get gluon output
  _UpperCamelCase       			=mx.nd.array([input_ids]   )
  _UpperCamelCase       			=original_bort(inputs=__SCREAMING_SNAKE_CASE  ,      token_types=[]   )

  # Get Transformer output (save and reload model again)
  hf_bort_model.save_pretrained(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=BertModel.from_pretrained(__SCREAMING_SNAKE_CASE   )
  hf_bort_model.eval()

  _UpperCamelCase       			=tokenizer.encode_plus(__SCREAMING_SNAKE_CASE  ,      return_tensors='''pt'''   )
  _UpperCamelCase       			=hf_bort_model(**__SCREAMING_SNAKE_CASE   )[0]

  _UpperCamelCase       			=output_gluon[0].asnumpy()
  _UpperCamelCase       			=output_hf[0].detach().numpy()

  _UpperCamelCase       			=np.max(np.abs(hf_layer - gluon_layer   )   ).item()
  _UpperCamelCase       			=np.allclose(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      atol=1E-3   )

  if success:
    print('''✔️ Both model do output the same tensors'''   )
  else:
    print('''❌ Both model do **NOT** output the same tensors'''   )
    print('''Absolute difference is:'''  ,      __SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      __lowerCamelCase						: List[str]		=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--bort_checkpoint_path', default=None, type=str, required=True, help='Path the official Bort params file.'
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      __lowerCamelCase						: Dict		=   parser.parse_args()
      convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)













'''simple docstring'''






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). """   ,     lowercase_   ,     )
class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				RobertaConfig
  lowerCAmelCase_					=				"""roberta"""


  def __init__(				self    :					Dict							,	UpperCamelCase__    :					List[str]  )     ->						List[Any]:
    super().__init__(UpperCamelCase__  )

    _UpperCamelCase       			=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. """   ,     lowercase_   ,     )
class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				RobertaConfig
  lowerCAmelCase_					=				"""roberta"""


  def __init__(				self    :					Tuple							,	UpperCamelCase__    :					Any  )     ->						Dict:
    super().__init__(UpperCamelCase__  )
    _UpperCamelCase       			=config.num_labels
    _UpperCamelCase       			=config.num_hidden_layers

    _UpperCamelCase       			=DeeRobertaModel(UpperCamelCase__  )
    _UpperCamelCase       			=nn.Dropout(config.hidden_dropout_prob  )
    _UpperCamelCase       			=nn.Linear(config.hidden_size							,	self.config.num_labels  )




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

      _UpperCamelCase       			=outputs[1]

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

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

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

      if train_highway:
        _UpperCamelCase       			=(sum(highway_losses[:-1]  ),) + outputs
        # exclude the final highway, of course
      else:
        _UpperCamelCase       			=(loss,) + outputs
    if not self.training:
      _UpperCamelCase       			=outputs + ((original_entropy, highway_entropy), exit_layer)
      if output_layer >= 0:
        _UpperCamelCase       			=(
            (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
        )  # use the highway of the last layer

    return outputs  # (loss), logits, (hidden_states), (attentions), entropy






