






"""simple docstring"""

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()






def _lowerCAmelCase	(  __lowerCamelCase:Union[str, Any] ,						__lowerCamelCase:Optional[Any] ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     BertConfig.from_json_file(__lowerCamelCase   )
						print(f'''Building PyTorch model from configuration: {config}'''   )
						__magic_name__            =     BertForPreTraining(__lowerCamelCase   )

						# Load weights from tf checkpoint
						load_tf_weights_in_bert(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   )

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


if __name__ == "__main__":
			lowercase    			=       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(
			    '''--bert_config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained BERT 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.'''
			)
			lowercase    			=       parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)











"""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()
lowercase    			=       logging.get_logger(__name__)

lowercase    			=       '''The Nymphenburg Palace is a beautiful palace in Munich!'''






def _lowerCAmelCase	(  __lowerCamelCase:str ,						__lowerCamelCase:str   ):

						'''simple docstring'''






						__magic_name__            =     {
						    "attention_cell": "multi_head",
						    "num_layers": 4,
						    "units": 1_0_2_4,
						    "hidden_size": 7_6_8,
						    "max_length": 5_1_2,
						    "num_heads": 8,
						    "scaled": True,
						    "dropout": 0.1,
						    "use_residual": True,
						    "embed_size": 1_0_2_4,
						    "embed_dropout": 0.1,
						    "word_embed": None,
						    "layer_norm_eps": 1E-5,
						    "token_type_vocab_size": 2,
						}

						__magic_name__            =     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
						__magic_name__            =     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
						__magic_name__            =     "openwebtext_ccnews_stories_books_cased"

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

						__magic_name__            =     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   )
						__magic_name__            =     original_bort._collect_params_with_prefix()

						# Build our config 🤗
						__magic_name__            =     {
						    "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   ),
						}

						__magic_name__            =     BertConfig.from_dict(__lowerCamelCase   )
						__magic_name__            =     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:Tuple   ) -> 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:str ,						__lowerCamelCase:Union[str, Any]   ):
												__magic_name__            =     hf_param.shape

												__magic_name__            =     to_torch(params[gluon_param]   )
												__magic_name__            =     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

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

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

												# self attention
												__magic_name__            =     layer.attention.self

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

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

												# self attention output
												__magic_name__            =     layer.attention.output

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

												# intermediate
												__magic_name__            =     layer.intermediate

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

												# output
												__magic_name__            =     layer.output

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

						__magic_name__            =     tokenizer.encode_plus(__lowerCamelCase   )["input_ids"]

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

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

						__magic_name__            =     tokenizer.encode_plus(__lowerCamelCase ,						return_tensors="pt"   )
						__magic_name__            =     hf_bort_model(**__lowerCamelCase   )[0]

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

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



