



"""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()
__snake_case							:     Tuple   =					logging.get_logger(__name__)

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


def     _lowercase       (						__snake_case      ,__snake_case			)		->     str:
			__lowerCAmelCase   :						Tuple				= {
			    "attention_cell": "multi_head",
			    "num_layers": 4,
			    "units": 1_024,
			    "hidden_size": 768,
			    "max_length": 512,
			    "num_heads": 8,
			    "scaled": True,
			    "dropout": 0.1,
			    "use_residual": True,
			    "embed_size": 1_024,
			    "embed_dropout": 0.1,
			    "word_embed": None,
			    "layer_norm_eps": 1e-5,
			    "token_type_vocab_size": 2,
			}

			__lowerCAmelCase   :						str				= 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   :						Any				= 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
			__lowerCAmelCase   :						Tuple				= "openwebtext_ccnews_stories_books_cased"

			# Specify download folder to Gluonnlp's vocab
			__lowerCAmelCase   :						Optional[Any]				= os.path.join(get_home_dir()      ,"models"			)
			__lowerCAmelCase   :						Dict				= _load_vocab(__snake_case      ,__snake_case      ,__snake_case      ,cls=__snake_case			)

			__lowerCAmelCase   :						Optional[Any]				= 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			)
			__lowerCAmelCase   :						List[str]				= original_bort._collect_params_with_prefix()

			# Build our config 🤗
			__lowerCAmelCase   :						int				= {
			    "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			),
			}

			__lowerCAmelCase   :						str				= BertConfig.from_dict(__snake_case			)
			__lowerCAmelCase   :						List[str]				= 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			) -> 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      ,__snake_case			):
						__lowerCAmelCase   :						List[str]				= hf_param.shape

						__lowerCAmelCase   :						Any				= to_torch(params[gluon_param]			)
						__lowerCAmelCase   :						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

			__lowerCAmelCase   :						Tuple				= check_and_map_params(
			    hf_bort_model.bert.embeddings.word_embeddings.weight      ,"word_embed.0.weight"			)
			__lowerCAmelCase   :						Tuple				= check_and_map_params(
			    hf_bort_model.bert.embeddings.position_embeddings.weight      ,"encoder.position_weight"			)
			__lowerCAmelCase   :						List[str]				= check_and_map_params(
			    hf_bort_model.bert.embeddings.LayerNorm.bias      ,"encoder.layer_norm.beta"			)
			__lowerCAmelCase   :						str				= 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   :						int				= torch.zeros_like(
			    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data			)

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

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

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

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

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

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

						# intermediate
						__lowerCAmelCase   :						BertIntermediate				= layer.intermediate

						__lowerCAmelCase   :						Tuple				= check_and_map_params(
						    intermediate.dense.bias      ,F"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""			)
						__lowerCAmelCase   :						Optional[Any]				= check_and_map_params(
						    intermediate.dense.weight      ,F"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""			)

						# output
						__lowerCAmelCase   :						BertOutput				= layer.output

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

			__lowerCAmelCase   :						Any				= tokenizer.encode_plus(__snake_case			)["input_ids"]

			# Get gluon output
			__lowerCAmelCase   :						Tuple				= mx.nd.array([input_ids]			)
			__lowerCAmelCase   :						Tuple				= original_bort(inputs=__snake_case      ,token_types=[]			)

			# Get Transformer output (save and reload model again)
			hf_bort_model.save_pretrained(__snake_case			)
			__lowerCAmelCase   :						str				= BertModel.from_pretrained(__snake_case			)
			hf_bort_model.eval()

			__lowerCAmelCase   :						str				= tokenizer.encode_plus(__snake_case      ,return_tensors="pt"			)
			__lowerCAmelCase   :						Optional[Any]				= hf_bort_model(**__snake_case			)[0]

			__lowerCAmelCase   :						Any				= output_gluon[0].asnumpy()
			__lowerCAmelCase   :						List[Any]				= output_hf[0].detach().numpy()

			__lowerCAmelCase   :						Union[str, Any]				= np.max(np.abs(hf_layer - gluon_layer			)			).item()
			__lowerCAmelCase   :						Tuple				= 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__":
	__snake_case							:     Optional[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.'
	)
	__snake_case							:     str   =					parser.parse_args()
	convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




"""simple docstring"""






import logging

from transformers.configuration_utils import PretrainedConfig


__snake_case							:     Tuple   =					logging.getLogger(__name__)



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			'masked_bert'

	def __init__(  self:	str ,    _SCREAMING_SNAKE_CASE:	Tuple=3_0522 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=768 ,    _SCREAMING_SNAKE_CASE:	List[str]=12 ,    _SCREAMING_SNAKE_CASE:	Any=12 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=3072 ,    _SCREAMING_SNAKE_CASE:	List[str]="gelu" ,    _SCREAMING_SNAKE_CASE:	List[str]=0.1 ,    _SCREAMING_SNAKE_CASE:	str=0.1 ,    _SCREAMING_SNAKE_CASE:	Dict=512 ,    _SCREAMING_SNAKE_CASE:	List[Any]=2 ,    _SCREAMING_SNAKE_CASE:	Tuple=0.02 ,    _SCREAMING_SNAKE_CASE:	int=1e-12 ,    _SCREAMING_SNAKE_CASE:	int=0 ,    _SCREAMING_SNAKE_CASE:	Tuple="topK" ,    _SCREAMING_SNAKE_CASE:	Any="constant" ,    _SCREAMING_SNAKE_CASE:	int=0.0 ,    **_SCREAMING_SNAKE_CASE:	Any ,    )       -> List[str]:

				"""simple docstring"""

				super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Union[str, Any]				= vocab_size
				__lowerCAmelCase   :						List[str]				= hidden_size
				__lowerCAmelCase   :						Any				= num_hidden_layers
				__lowerCAmelCase   :						Dict				= num_attention_heads
				__lowerCAmelCase   :						Union[str, Any]				= hidden_act
				__lowerCAmelCase   :						Optional[Any]				= intermediate_size
				__lowerCAmelCase   :						int				= hidden_dropout_prob
				__lowerCAmelCase   :						List[Any]				= attention_probs_dropout_prob
				__lowerCAmelCase   :						int				= max_position_embeddings
				__lowerCAmelCase   :						str				= type_vocab_size
				__lowerCAmelCase   :						Any				= initializer_range
				__lowerCAmelCase   :						Optional[int]				= layer_norm_eps
				__lowerCAmelCase   :						Tuple				= pruning_method
				__lowerCAmelCase   :						Union[str, Any]				= mask_init
				__lowerCAmelCase   :						str				= mask_scale