






"""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()
_SCREAMING_SNAKE_CASE					:				Union[str, Any]					    =						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:				Optional[Any]					    =						'''The Nymphenburg Palace is a beautiful palace in Munich!'''







def      lowerCamelCase__       (							_lowerCamelCase       :		List[str]		,				_lowerCamelCase       :		Optional[int] )      ->				int:
  lowerCamelCase_         =						{
      '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,
  }

  lowerCamelCase_         =						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
  lowerCamelCase_         =						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=__A		,				output_all_encodings=__A		,				use_residual=predefined_args['use_residual']		,				activation=predefined_args.get('activation'		,				'gelu' )		,				layer_norm_eps=predefined_args.get('layer_norm_eps'		,				__A )		,				)

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

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

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

  original_bort.load_parameters(__A		,				cast_dtype=__A		,				ignore_extra=__A )
  lowerCamelCase_         =						original_bort._collect_params_with_prefix()

  # Build our config 🤗
  lowerCamelCase_         =						{
      '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': 0.02,
      '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(__A ),
  }

  lowerCamelCase_         =						BertConfig.from_dict(__A )
  lowerCamelCase_         =						BertForMaskedLM(__A )
  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(_lowerCamelCase       :		str ) -> 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(_lowerCamelCase       :		Tuple		,				_lowerCamelCase       :		Any ):
    lowerCamelCase_         =						hf_param.shape

    lowerCamelCase_         =						to_torch(params[gluon_param] )
    lowerCamelCase_         =						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

  lowerCamelCase_         =						check_and_map_params(
      hf_bort_model.bert.embeddings.word_embeddings.weight		,				'word_embed.0.weight' )
  lowerCamelCase_         =						check_and_map_params(
      hf_bort_model.bert.embeddings.position_embeddings.weight		,				'encoder.position_weight' )
  lowerCamelCase_         =						check_and_map_params(
      hf_bort_model.bert.embeddings.LayerNorm.bias		,				'encoder.layer_norm.beta' )
  lowerCamelCase_         =						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)
  lowerCamelCase_         =						torch.zeros_like(
      hf_bort_model.bert.embeddings.token_type_embeddings.weight.data )

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

    # self attention
    lowerCamelCase_         =						layer.attention.self

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

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

    # self attention output
    lowerCamelCase_         =						layer.attention.output

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

    # intermediate
    lowerCamelCase_         =						layer.intermediate

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

    # output
    lowerCamelCase_         =						layer.output

    lowerCamelCase_         =						check_and_map_params(
        bert_output.dense.bias		,				F'''encoder.transformer_cells.{i}.ffn.ffn_2.bias''' )
    lowerCamelCase_         =						check_and_map_params(
        bert_output.dense.weight		,				F'''encoder.transformer_cells.{i}.ffn.ffn_2.weight''' )
    lowerCamelCase_         =						check_and_map_params(
        bert_output.LayerNorm.bias		,				F'''encoder.transformer_cells.{i}.ffn.layer_norm.beta''' )
    lowerCamelCase_         =						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
  lowerCamelCase_         =						RobertaTokenizer.from_pretrained('roberta-base' )

  lowerCamelCase_         =						tokenizer.encode_plus(__A )['input_ids']

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

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

  lowerCamelCase_         =						tokenizer.encode_plus(__A		,				return_tensors='pt' )
  lowerCamelCase_         =						hf_bort_model(**__A )[0]

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

  lowerCamelCase_         =						np.max(np.abs(hf_layer - gluon_layer ) ).item()
  lowerCamelCase_         =						np.allclose(__A		,				__A		,				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:'		,				__A )


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE					:				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.'''
  )
  _SCREAMING_SNAKE_CASE					:				int					    =						parser.parse_args()
  convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''





def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise ValueError('''multiplicative_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''multiplicative_persistence() does not accept negative values''')

     _a      =  0
     _a      =  str(__A)

     while len(__A) != 1:
          _a      =  [int(__A) for i in num_string]

          _a      =  1
          for i in range(0  ,	len(__A)):
               total *= numbers[i]

          _a      =  str(__A)

          steps += 1
     return steps




def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise ValueError('''additive_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''additive_persistence() does not accept negative values''')

     _a      =  0
     _a      =  str(__A)

     while len(__A) != 1:
          _a      =  [int(__A) for i in num_string]

          _a      =  0
          for i in range(0  ,	len(__A)):
               total += numbers[i]

          _a      =  str(__A)

          steps += 1
     return steps


if __name__ == "__main__":
  import doctest

  doctest.testmod()



