






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

_A     :      Tuple   					='''The Nymphenburg Palace is a beautiful palace in Munich!'''





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	Union[str, Any]:
							lowerCamelCase__ :     Dict	  =	{
							    """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,
							}

							lowerCamelCase__ :     Optional[int]	  =	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__ :     str	  =	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=UpperCamelCase    ,      output_all_encodings=UpperCamelCase    ,      use_residual=predefined_args["""use_residual"""]    ,      activation=predefined_args.get("""activation"""    ,      """gelu"""				)    ,      layer_norm_eps=predefined_args.get("""layer_norm_eps"""    ,      UpperCamelCase				)    ,      )

							# 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__ :     List[Any]	  =	os.path.join(get_home_dir()    ,      """models"""				)
							lowerCamelCase__ :     str	  =	_load_vocab(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      cls=UpperCamelCase				)

							lowerCamelCase__ :     Union[str, Any]	  =	nlp.model.BERTModel(
							    UpperCamelCase    ,      len(UpperCamelCase				)    ,      units=predefined_args["""units"""]    ,      embed_size=predefined_args["""embed_size"""]    ,      embed_dropout=predefined_args["""embed_dropout"""]    ,      word_embed=predefined_args["""word_embed"""]    ,      use_pooler=UpperCamelCase    ,      use_token_type_embed=UpperCamelCase    ,      token_type_vocab_size=predefined_args["""token_type_vocab_size"""]    ,      use_classifier=UpperCamelCase    ,      use_decoder=UpperCamelCase    ,      )

							original_bort.load_parameters(UpperCamelCase    ,      cast_dtype=UpperCamelCase    ,      ignore_extra=UpperCamelCase				)
							lowerCamelCase__ :     str	  =	original_bort._collect_params_with_prefix()

							# Build our config 🤗
							lowerCamelCase__ :     Any	  =	{
							    """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(UpperCamelCase				),
							}

							lowerCamelCase__ :     Optional[Any]	  =	BertConfig.from_dict(UpperCamelCase				)
							lowerCamelCase__ :     List[Any]	  =	BertForMaskedLM(UpperCamelCase				)
							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(UpperCamelCase				) -> 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(UpperCamelCase    ,      UpperCamelCase				):
														lowerCamelCase__ :     Optional[int]	  =	hf_param.shape

														lowerCamelCase__ :     List[str]	  =	to_torch(params[gluon_param]				)
														lowerCamelCase__ :     List[Any]	  =	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__ :     Any	  =	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__ :     List[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__ :     List[Any]	  =	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__ :     Dict	  =	check_and_map_params(
														    self_attn.key.weight.data    ,      f'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight'''				)
														lowerCamelCase__ :     Dict	  =	check_and_map_params(
														    self_attn.query.bias.data    ,      f'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias'''				)
														lowerCamelCase__ :     str	  =	check_and_map_params(
														    self_attn.query.weight.data    ,      f'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight'''				)
														lowerCamelCase__ :     List[str]	  =	check_and_map_params(
														    self_attn.value.bias.data    ,      f'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias'''				)
														lowerCamelCase__ :     Dict	  =	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__ :     Dict	  =	check_and_map_params(
														    self_output.dense.bias    ,      f'''encoder.transformer_cells.{i}.proj.bias'''				)
														lowerCamelCase__ :     List[str]	  =	check_and_map_params(
														    self_output.dense.weight    ,      f'''encoder.transformer_cells.{i}.proj.weight'''				)
														lowerCamelCase__ :     List[str]	  =	check_and_map_params(
														    self_output.LayerNorm.bias    ,      f'''encoder.transformer_cells.{i}.layer_norm.beta'''				)
														lowerCamelCase__ :     Tuple	  =	check_and_map_params(
														    self_output.LayerNorm.weight    ,      f'''encoder.transformer_cells.{i}.layer_norm.gamma'''				)

														# intermediate
														lowerCamelCase__ :     BertIntermediate	  =	layer.intermediate

														lowerCamelCase__ :     List[str]	  =	check_and_map_params(
														    intermediate.dense.bias    ,      f'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''				)
														lowerCamelCase__ :     int	  =	check_and_map_params(
														    intermediate.dense.weight    ,      f'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''				)

														# output
														lowerCamelCase__ :     BertOutput	  =	layer.output

														lowerCamelCase__ :     int	  =	check_and_map_params(
														    bert_output.dense.bias    ,      f'''encoder.transformer_cells.{i}.ffn.ffn_2.bias'''				)
														lowerCamelCase__ :     Any	  =	check_and_map_params(
														    bert_output.dense.weight    ,      f'''encoder.transformer_cells.{i}.ffn.ffn_2.weight'''				)
														lowerCamelCase__ :     Union[str, Any]	  =	check_and_map_params(
														    bert_output.LayerNorm.bias    ,      f'''encoder.transformer_cells.{i}.ffn.layer_norm.beta'''				)
														lowerCamelCase__ :     Optional[int]	  =	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__ :     List[str]	  =	RobertaTokenizer.from_pretrained("""roberta-base"""				)

							lowerCamelCase__ :     int	  =	tokenizer.encode_plus(UpperCamelCase				)["""input_ids"""]

							# Get gluon output
							lowerCamelCase__ :     List[str]	  =	mx.nd.array([input_ids]				)
							lowerCamelCase__ :     Dict	  =	original_bort(inputs=UpperCamelCase    ,      token_types=[]				)

							# Get Transformer output (save and reload model again)
							hf_bort_model.save_pretrained(UpperCamelCase				)
							lowerCamelCase__ :     Tuple	  =	BertModel.from_pretrained(UpperCamelCase				)
							hf_bort_model.eval()

							lowerCamelCase__ :     Dict	  =	tokenizer.encode_plus(UpperCamelCase    ,      return_tensors="""pt"""				)
							lowerCamelCase__ :     List[str]	  =	hf_bort_model(**UpperCamelCase				)[0]

							lowerCamelCase__ :     str	  =	output_gluon[0].asnumpy()
							lowerCamelCase__ :     Dict	  =	output_hf[0].detach().numpy()

							lowerCamelCase__ :     List[str]	  =	np.max(np.abs(hf_layer - gluon_layer				)				).item()
							lowerCamelCase__ :     Union[str, Any]	  =	np.allclose(UpperCamelCase    ,      UpperCamelCase    ,      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:"""    ,      UpperCamelCase				)


if __name__ == "__main__":
			_A     :      List[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.'''
			)
			_A     :      List[str]   					=parser.parse_args()
			convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)













'''simple docstring'''







from torch import nn





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Dict:
							if act_fn in ["swish", "silu"]:
														return nn.SiLU()
							elif act_fn == "mish":
														return nn.Mish()
							elif act_fn == "gelu":
														return nn.GELU()
							else:
														raise ValueError(f'''Unsupported activation function: {act_fn}'''				)

