





'''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             =       logging.get_logger(__name__)

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





def   lowerCAmelCase_				( __a			,					__a	)  ->	Union[str, Any]:







  """simple docstring"""





  lowerCamelCase__:   str    						={
      '''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__:   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__:   Dict    						=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
  lowerCamelCase__:   int    						='''openwebtext_ccnews_stories_books_cased'''

  # Specify download folder to Gluonnlp's vocab
  lowerCamelCase__:   str    						=os.path.join(get_home_dir()			,					"models"	)
  lowerCamelCase__:   Optional[int]    						=_load_vocab(lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_			,					cls=lowerCAmelCase_	)

  lowerCamelCase__:   List[str]    						=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_	)
  lowerCamelCase__:   List[Any]    						=original_bort._collect_params_with_prefix()

  # Build our config 🤗
  lowerCamelCase__:   Union[str, 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''': 0.0_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_	),
  }

  lowerCamelCase__:   Union[str, Any]    						=BertConfig.from_dict(lowerCAmelCase_	)
  lowerCamelCase__:   List[str]    						=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(__a	) -> 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(__a			,					__a	):
    lowerCamelCase__:   Dict    						=hf_param.shape

    lowerCamelCase__:   Union[str, Any]    						=to_torch(params[gluon_param]	)
    lowerCamelCase__:   str    						=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__:   Union[str, Any]    						=check_and_map_params(
      hf_bort_model.bert.embeddings.word_embeddings.weight			,					"word_embed.0.weight"	)
  lowerCamelCase__:   Union[str, Any]    						=check_and_map_params(
      hf_bort_model.bert.embeddings.position_embeddings.weight			,					"encoder.position_weight"	)
  lowerCamelCase__:   int    						=check_and_map_params(
      hf_bort_model.bert.embeddings.LayerNorm.bias			,					"encoder.layer_norm.beta"	)
  lowerCamelCase__:   int    						=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__:   Tuple    						=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__:   List[str]    						=check_and_map_params(
        self_attn.key.bias.data			,					F"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""	)

    lowerCamelCase__:   List[str]    						=check_and_map_params(
        self_attn.key.weight.data			,					F"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""	)
    lowerCamelCase__:   int    						=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__:   List[str]    						=check_and_map_params(
        self_attn.value.bias.data			,					F"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""	)
    lowerCamelCase__:   Union[str, 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__:   Union[str, Any]    						=check_and_map_params(
        self_output.dense.bias			,					F"""encoder.transformer_cells.{i}.proj.bias"""	)
    lowerCamelCase__:   str    						=check_and_map_params(
        self_output.dense.weight			,					F"""encoder.transformer_cells.{i}.proj.weight"""	)
    lowerCamelCase__:   Any    						=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__:   Tuple    						=check_and_map_params(
        intermediate.dense.weight			,					F"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""	)

    # output
    lowerCamelCase__:   BertOutput    						=layer.output

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

  lowerCamelCase__:   Union[str, Any]    						=tokenizer.encode_plus(lowerCAmelCase_	)['''input_ids''']

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

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

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

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

  lowerCamelCase__:   Dict    						=np.max(np.abs(hf_layer - gluon_layer	)	).item()
  lowerCamelCase__:   int    						=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__":
     __A             =       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             =       parser.parse_args()
     convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)








def   lowerCAmelCase_				( __a			,					__a	)  ->	float:







  """simple docstring"""





  if density <= 0:
    raise ValueError("Impossible fluid density"	)
  if bulk_modulus <= 0:
    raise ValueError("Impossible bulk modulus"	)

  return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
     import doctest

     doctest.testmod()

