



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

__snake_case     :Dict			       ='The Nymphenburg Palace is a beautiful palace in Munich!'






def  lowerCamelCase_	(      lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	str				)       ->							List[Any]:

							'''simple docstring'''






							A      						=    {
							    '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,
							}

							A      						=    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
							A      						=    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
							A      						=    'openwebtext_ccnews_stories_books_cased'

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

							A      						=    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__				)
							A      						=    original_bort._collect_params_with_prefix()

							# Build our config 🤗
							A      						=    {
							    '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(lowerCAmelCase__				),
							}

							A      						=    BertConfig.from_dict(lowerCAmelCase__				)
							A      						=    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__	:	Optional[int]				) -> 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__	:	int				):
														A      						=    hf_param.shape

														A      						=    to_torch(params[gluon_param]				)
														A      						=    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

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

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

														# self attention
														A      						=    layer.attention.self

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

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

														# self attention output
														A      						=    layer.attention.output

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

														# intermediate
														A      						=    layer.intermediate

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

														# output
														A      						=    layer.output

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

							A      						=    tokenizer.encode_plus(lowerCAmelCase__				)['input_ids']

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

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

							A      						=    tokenizer.encode_plus(lowerCAmelCase__			,	return_tensors='pt'				)
							A      						=    hf_bort_model(**lowerCAmelCase__				)[0]

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

							A      						=    np.max(np.abs(hf_layer - gluon_layer				)				).item()
							A      						=    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__":
				__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.'
				)
				__snake_case     :Any			       =parser.parse_args()
				convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)







import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   # Initialise PyTorch model
   UpperCamelCase_						=  AlbertConfig.from_json_file(__lowercase)
   print(f"""Building PyTorch model from configuration: {config}""")
   UpperCamelCase_						=  AlbertForPreTraining(__lowercase)

   # Load weights from tf checkpoint
   load_tf_weights_in_albert(__lowercase     ,   __lowercase     ,   __lowercase)

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


if __name__ == "__main__":
 snake_case__   :						Tuple													=   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
 )
 parser.add_argument(
     """--albert_config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained ALBERT 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."""
 )
 snake_case__   :						str													=   parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





