






"""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						:						Optional[Any]					   =       logging.get_logger(__name__)

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



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				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': 0.0_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__				),
				}

				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__				) -> 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__       ,lowerCAmelCase__				):
								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__":
					SCREAMING_SNAKE_CASE						:						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.'''
					)
					SCREAMING_SNAKE_CASE						:						Union[str, Any]					   =       parser.parse_args()
					convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)










"""simple docstring"""






from typing import Union

import fire
import torch
from tqdm import tqdm



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__ = "cpu"       ,lowerCAmelCase__ = None				):
				A__							 =       torch.load(lowerCAmelCase__       ,map_location=lowerCAmelCase__				)
				for k, v in tqdm(state_dict.items()				):
								if not isinstance(lowerCAmelCase__       ,torch.Tensor				):
												raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'				)
								A__							 =       v.half()
				if save_path is None:  # overwrite src_path
								A__							 =       src_path
				torch.save(lowerCAmelCase__       ,lowerCAmelCase__				)


if __name__ == "__main__":
					fire.Fire(convert)


