



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()
__UpperCAmelCase          =       logging.get_logger(__name__)

__UpperCAmelCase          =       '''The Nymphenburg Palace is a beautiful palace in Munich!'''
def 		UpperCamelCase	(   snake_case__       :    str       ,     snake_case__       :    str	)	->     Union[str, Any]:
   UpperCamelCase    :  Dict							=	{
       '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    :  Optional[Any]							=	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    :  Tuple							=	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=snake_case__       ,     output_all_encodings=snake_case__       ,     use_residual=predefined_args['use_residual']       ,     activation=predefined_args.get('activation'       ,     'gelu'	)       ,     layer_norm_eps=predefined_args.get('layer_norm_eps'       ,     snake_case__	)       ,     )

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

   # Specify download folder to Gluonnlp's vocab
   UpperCamelCase    :  Dict							=	os.path.join(get_home_dir()       ,     'models'	)
   UpperCamelCase    :  Tuple							=	_load_vocab(snake_case__       ,     snake_case__       ,     snake_case__       ,     cls=snake_case__	)

   UpperCamelCase    :  Tuple							=	nlp.model.BERTModel(
       snake_case__       ,     len(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=snake_case__       ,     use_token_type_embed=snake_case__       ,     token_type_vocab_size=predefined_args['token_type_vocab_size']       ,     use_classifier=snake_case__       ,     use_decoder=snake_case__       ,     )

   original_bort.load_parameters(snake_case__       ,     cast_dtype=snake_case__       ,     ignore_extra=snake_case__	)
   UpperCamelCase    :  Any							=	original_bort._collect_params_with_prefix()

   # Build our config 🤗
   UpperCamelCase    :  Dict							=	{
       '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(snake_case__	),
   }

   UpperCamelCase    :  Any							=	BertConfig.from_dict(snake_case__	)
   UpperCamelCase    :  Optional[int]							=	BertForMaskedLM(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(snake_case__       :    List[Any]	) -> 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(snake_case__       :    Union[str, Any]       ,     snake_case__       :    Any	):
      UpperCamelCase    :  Optional[Any]							=	hf_param.shape

      UpperCamelCase    :  List[str]							=	to_torch(params[gluon_param]	)
      UpperCamelCase    :  Tuple							=	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    :  Tuple							=	check_and_map_params(
       hf_bort_model.bert.embeddings.word_embeddings.weight       ,     'word_embed.0.weight'	)
   UpperCamelCase    :  int							=	check_and_map_params(
       hf_bort_model.bert.embeddings.position_embeddings.weight       ,     'encoder.position_weight'	)
   UpperCamelCase    :  Dict							=	check_and_map_params(
       hf_bort_model.bert.embeddings.LayerNorm.bias       ,     'encoder.layer_norm.beta'	)
   UpperCamelCase    :  List[Any]							=	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    :  str							=	torch.zeros_like(
       hf_bort_model.bert.embeddings.token_type_embeddings.weight.data	)

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

      # self attention
      UpperCamelCase    :  BertSelfAttention							=	layer.attention.self

      UpperCamelCase    :  List[str]							=	check_and_map_params(
          self_attn.key.bias.data       ,     F"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""	)

      UpperCamelCase    :  Union[str, Any]							=	check_and_map_params(
          self_attn.key.weight.data       ,     F"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""	)
      UpperCamelCase    :  Union[str, Any]							=	check_and_map_params(
          self_attn.query.bias.data       ,     F"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""	)
      UpperCamelCase    :  Tuple							=	check_and_map_params(
          self_attn.query.weight.data       ,     F"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""	)
      UpperCamelCase    :  int							=	check_and_map_params(
          self_attn.value.bias.data       ,     F"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""	)
      UpperCamelCase    :  int							=	check_and_map_params(
          self_attn.value.weight.data       ,     F"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight"""	)

      # self attention output
      UpperCamelCase    :  BertSelfOutput							=	layer.attention.output

      UpperCamelCase    :  List[Any]							=	check_and_map_params(
          self_output.dense.bias       ,     F"""encoder.transformer_cells.{i}.proj.bias"""	)
      UpperCamelCase    :  Optional[Any]							=	check_and_map_params(
          self_output.dense.weight       ,     F"""encoder.transformer_cells.{i}.proj.weight"""	)
      UpperCamelCase    :  int							=	check_and_map_params(
          self_output.LayerNorm.bias       ,     F"""encoder.transformer_cells.{i}.layer_norm.beta"""	)
      UpperCamelCase    :  Union[str, Any]							=	check_and_map_params(
          self_output.LayerNorm.weight       ,     F"""encoder.transformer_cells.{i}.layer_norm.gamma"""	)

      # intermediate
      UpperCamelCase    :  BertIntermediate							=	layer.intermediate

      UpperCamelCase    :  Any							=	check_and_map_params(
          intermediate.dense.bias       ,     F"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""	)
      UpperCamelCase    :  List[str]							=	check_and_map_params(
          intermediate.dense.weight       ,     F"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""	)

      # output
      UpperCamelCase    :  BertOutput							=	layer.output

      UpperCamelCase    :  List[Any]							=	check_and_map_params(
          bert_output.dense.bias       ,     F"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""	)
      UpperCamelCase    :  int							=	check_and_map_params(
          bert_output.dense.weight       ,     F"""encoder.transformer_cells.{i}.ffn.ffn_2.weight"""	)
      UpperCamelCase    :  str							=	check_and_map_params(
          bert_output.LayerNorm.bias       ,     F"""encoder.transformer_cells.{i}.ffn.layer_norm.beta"""	)
      UpperCamelCase    :  Optional[Any]							=	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    :  Dict							=	RobertaTokenizer.from_pretrained('roberta-base'	)

   UpperCamelCase    :  Dict							=	tokenizer.encode_plus(snake_case__	)['input_ids']

   # Get gluon output
   UpperCamelCase    :  Union[str, Any]							=	mx.nd.array([input_ids]	)
   UpperCamelCase    :  List[Any]							=	original_bort(inputs=snake_case__       ,     token_types=[]	)

   # Get Transformer output (save and reload model again)
   hf_bort_model.save_pretrained(snake_case__	)
   UpperCamelCase    :  Union[str, Any]							=	BertModel.from_pretrained(snake_case__	)
   hf_bort_model.eval()

   UpperCamelCase    :  Optional[Any]							=	tokenizer.encode_plus(snake_case__       ,     return_tensors='pt'	)
   UpperCamelCase    :  Tuple							=	hf_bort_model(**snake_case__	)[0]

   UpperCamelCase    :  Tuple							=	output_gluon[0].asnumpy()
   UpperCamelCase    :  Dict							=	output_hf[0].detach().numpy()

   UpperCamelCase    :  Union[str, Any]							=	np.max(np.abs(hf_layer - gluon_layer	)	).item()
   UpperCamelCase    :  Optional[int]							=	np.allclose(snake_case__       ,     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:'       ,     snake_case__	)


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








import functools
from typing import Any
def 		UpperCamelCase	(   snake_case__       :    str       ,     snake_case__       :    list[str]	)	->     bool:

   # Validation
   if not isinstance(snake_case__       ,     snake_case__	) or len(snake_case__	) == 0:
      raise ValueError('the string should be not empty string'	)

   if not isinstance(snake_case__       ,     snake_case__	) or not all(
       isinstance(snake_case__       ,     snake_case__	) and len(snake_case__	) > 0 for item in words	):
      raise ValueError('the words should be a list of non-empty strings'	)

   # Build trie
   UpperCamelCase    :  dict[str, Any]							=	{}
   UpperCamelCase    :  List[str]							=	'WORD_KEEPER'

   for word in words:
      UpperCamelCase    :  List[str]							=	trie
      for c in word:
         if c not in trie_node:
            UpperCamelCase    :  int							=	{}

         UpperCamelCase    :  str							=	trie_node[c]

      UpperCamelCase    :  Tuple							=	True

   UpperCamelCase    :  List[Any]							=	len(snake_case__	)

   # Dynamic programming method
   @functools.cache
   def is_breakable(snake_case__       :    int	) -> bool:
      if index == len_string:
         return True

      UpperCamelCase    :  Dict							=	trie
      for i in range(snake_case__       ,     snake_case__	):
         UpperCamelCase    :  List[Any]							=	trie_node.get(string[i]       ,     snake_case__	)

         if trie_node is None:
            return False

         if trie_node.get(snake_case__       ,     snake_case__	) and is_breakable(i + 1	):
            return True

      return False

   return is_breakable(0	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()



