




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__ :   Optional[int]						=							logging.get_logger(__name__)

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





def        __UpperCAmelCase						(							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     str  )       ->  int:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : List[str]					  =      {
			    'attention_cell': 'multi_head',
			    'num_layers': 4,
			    'units': 10_24,
			    'hidden_size': 7_68,
			    'max_length': 5_12,
			    'num_heads': 8,
			    'scaled': True,
			    'dropout': 0.1,
			    'use_residual': True,
			    'embed_size': 10_24,
			    'embed_dropout': 0.1,
			    'word_embed': None,
			    'layer_norm_eps': 1E-5,
			    'token_type_vocab_size': 2,
			}

			SCREAMING_SNAKE_CASE_   : Dict					  =      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
			SCREAMING_SNAKE_CASE_   : int					  =      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=lowerCamelCase_  ,							output_all_encodings=lowerCamelCase_  ,							use_residual=predefined_args['use_residual']  ,							activation=predefined_args.get('activation'  ,							'gelu'  )  ,							layer_norm_eps=predefined_args.get('layer_norm_eps'  ,							lowerCamelCase_  )  ,							)

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

			# Specify download folder to Gluonnlp's vocab
			SCREAMING_SNAKE_CASE_   : List[str]					  =      os.path.join(get_home_dir()  ,							'models'  )
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      _load_vocab(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							cls=lowerCamelCase_  )

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

			original_bort.load_parameters(lowerCamelCase_  ,							cast_dtype=lowerCamelCase_  ,							ignore_extra=lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      original_bort._collect_params_with_prefix()

			# Build our config 🤗
			SCREAMING_SNAKE_CASE_   : str					  =      {
			    '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(lowerCamelCase_  ),
			}

			SCREAMING_SNAKE_CASE_   : List[str]					  =      BertConfig.from_dict(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : int					  =      BertForMaskedLM(lowerCamelCase_  )
			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_ :     Optional[int]  ,							lowerCamelCase_ :     Optional[int]  ):
						SCREAMING_SNAKE_CASE_   : List[str]					  =      hf_param.shape

						SCREAMING_SNAKE_CASE_   : List[Any]					  =      to_torch(params[gluon_param]  )
						SCREAMING_SNAKE_CASE_   : int					  =      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

			SCREAMING_SNAKE_CASE_   : Tuple					  =      check_and_map_params(
			    hf_bort_model.bert.embeddings.word_embeddings.weight  ,							'word_embed.0.weight'  )
			SCREAMING_SNAKE_CASE_   : Any					  =      check_and_map_params(
			    hf_bort_model.bert.embeddings.position_embeddings.weight  ,							'encoder.position_weight'  )
			SCREAMING_SNAKE_CASE_   : Tuple					  =      check_and_map_params(
			    hf_bort_model.bert.embeddings.LayerNorm.bias  ,							'encoder.layer_norm.beta'  )
			SCREAMING_SNAKE_CASE_   : 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)
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      torch.zeros_like(
			    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data  )

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

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

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

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

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

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

						# intermediate
						SCREAMING_SNAKE_CASE_   : BertIntermediate					  =      layer.intermediate

						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      check_and_map_params(
						    intermediate.dense.bias  ,							F'encoder.transformer_cells.{i}.ffn.ffn_1.bias'  )
						SCREAMING_SNAKE_CASE_   : Dict					  =      check_and_map_params(
						    intermediate.dense.weight  ,							F'encoder.transformer_cells.{i}.ffn.ffn_1.weight'  )

						# output
						SCREAMING_SNAKE_CASE_   : BertOutput					  =      layer.output

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

			SCREAMING_SNAKE_CASE_   : int					  =      tokenizer.encode_plus(lowerCamelCase_  )['input_ids']

			# Get gluon output
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      mx.nd.array([input_ids]  )
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      original_bort(inputs=lowerCamelCase_  ,							token_types=[]  )

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

			SCREAMING_SNAKE_CASE_   : Any					  =      tokenizer.encode_plus(lowerCamelCase_  ,							return_tensors='pt'  )
			SCREAMING_SNAKE_CASE_   : str					  =      hf_bort_model(**lowerCamelCase_  )[0]

			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      output_gluon[0].asnumpy()
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      output_hf[0].detach().numpy()

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


if __name__ == "__main__":
						UpperCamelCase__ :   Union[str, Any]						=							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__ :   Optional[int]						=							parser.parse_args()
						convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)









def        __UpperCAmelCase						(							lowerCamelCase_ :     str  )       ->  bool:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Any					  =      0
			for ch in input_str:
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      ord(lowerCamelCase_  )
						SCREAMING_SNAKE_CASE_   : Tuple					  =      pow(2  ,							lowerCamelCase_  )

						# If we already turned on bit for current character's unicode
						if bitmap >> ch_unicode & 1 == 1:
									return False
						bitmap |= ch_bit_index_on
			return True


if __name__ == "__main__":
						import doctest

						doctest.testmod()



