




"""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    =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =	"""The Nymphenburg Palace is a beautiful palace in Munich!"""







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		Tuple:
 A__	=    {
     "attention_cell": "multi_head",
     "num_layers": 4,
     "units": 10_24,
     "hidden_size": 7_68,
     "max_length": 5_12,
     "num_heads": 8,
     "scaled": True,
     "dropout": 0.1,
     "use_residual": True,
     "embed_size": 10_24,
     "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=lowercase_      ,			output_all_encodings=lowercase_      ,			use_residual=predefined_args["use_residual"]      ,			activation=predefined_args.get("activation"      ,			"gelu"						)      ,			layer_norm_eps=predefined_args.get("layer_norm_eps"      ,			lowercase_						)      ,			)

 # 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(lowercase_      ,			lowercase_      ,			lowercase_      ,			cls=lowercase_						)

 A__	=    nlp.model.BERTModel(
     lowercase_      ,			len(lowercase_						)      ,			units=predefined_args["units"]      ,			embed_size=predefined_args["embed_size"]      ,			embed_dropout=predefined_args["embed_dropout"]      ,			word_embed=predefined_args["word_embed"]      ,			use_pooler=lowercase_      ,			use_token_type_embed=lowercase_      ,			token_type_vocab_size=predefined_args["token_type_vocab_size"]      ,			use_classifier=lowercase_      ,			use_decoder=lowercase_      ,			)

 original_bort.load_parameters(lowercase_      ,			cast_dtype=lowercase_      ,			ignore_extra=lowercase_						)
 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(lowercase_						),
 }

 A__	=    BertConfig.from_dict(lowercase_						)
 A__	=    BertForMaskedLM(lowercase_						)
 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(lowercase_						) -> 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(lowercase_      ,			lowercase_						):
  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(lowercase_						)["input_ids"]

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

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

 A__	=    tokenizer.encode_plus(lowercase_      ,			return_tensors="pt"						)
 A__	=    hf_bort_model(**lowercase_						)[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(lowercase_      ,			lowercase_      ,			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:"      ,			lowercase_						)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	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    =	parser.parse_args()
      convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)











"""simple docstring"""



SCREAMING_SNAKE_CASE    =	"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

SCREAMING_SNAKE_CASE    =	[{"type": "code", "content": INSTALL_CONTENT}]
SCREAMING_SNAKE_CASE    =	{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





