





'''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()
__lowerCAmelCase							:     List[Any]		     =logging.get_logger(__name__)

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





def 						UpperCamelCase    (							_lowerCamelCase : str						,	_lowerCamelCase : str	):
    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=_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
    A__    =      "openwebtext_ccnews_stories_books_cased"

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

    A__    =      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	)
    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(_lowerCamelCase	),
    }

    A__    =      BertConfig.from_dict(_lowerCamelCase	)
    A__    =      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 : List[Any]	) -> 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 : Union[str, Any]						,	_lowerCamelCase : Dict	):
        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(_lowerCamelCase	)["input_ids"]

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

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

    A__    =      tokenizer.encode_plus(_lowerCamelCase						,	return_tensors="pt"	)
    A__    =      hf_bort_model(**_lowerCamelCase	)[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(_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__":
  __lowerCAmelCase							:     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."
  )
  __lowerCAmelCase							:     Tuple		     =parser.parse_args()
  convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)










'''simple docstring'''

import unittest

import numpy as np





def 						UpperCamelCase    (							_lowerCamelCase : np.ndarray						,	_lowerCamelCase : np.ndarray						,	_lowerCamelCase : np.ndarray						,	_lowerCamelCase : np.ndarray | None = None						,	):
    A__    =      np.shape(_lowerCamelCase	)
    A__    =      np.shape(_lowerCamelCase	)
    A__    =      np.shape(_lowerCamelCase	)

    if shape_a[0] != shape_b[0]:
        A__    =      (
            "Expected the same number of rows for A and B. "
            F"Instead found A of size {shape_a} and B of size {shape_b}"
        )
        raise ValueError(_lowerCamelCase	)

    if shape_b[1] != shape_c[1]:
        A__    =      (
            "Expected the same number of columns for B and C. "
            F"Instead found B of size {shape_b} and C of size {shape_c}"
        )
        raise ValueError(_lowerCamelCase	)

    A__    =      pseudo_inv
    if a_inv is None:
        try:
            A__    =      np.linalg.inv(_lowerCamelCase	)
        except np.linalg.LinAlgError:
            raise ValueError(
                "Input matrix A is not invertible. Cannot compute Schur complement."	)

    return mat_c - mat_b.T @ a_inv @ mat_b






class        UpperCAmelCase     (	unittest.TestCase    ):
 def 					UpperCAmelCase_							(    self				:Any							)->							None:
     A__    =      np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
     A__    =      np.array([[0, 3], [3, 0], [2, 3]]							)
     A__    =      np.array([[2, 1], [6, 3]]							)

     A__    =      schur_complement(lowercase_						,	lowercase_						,	lowercase_							)

     A__    =      np.block([[a, b], [b.T, c]]							)

     A__    =      np.linalg.det(lowercase_							)
     A__    =      np.linalg.det(lowercase_							)
     A__    =      np.linalg.det(lowercase_							)

     self.assertAlmostEqual(lowercase_						,	det_a * det_s							)
 def 					UpperCAmelCase_							(    self				:Dict							)->							None:
     A__    =      np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
     A__    =      np.array([[0, 3], [3, 0], [2, 3]]							)
     A__    =      np.array([[2, 1], [6, 3]]							)

     with self.assertRaises(lowercase_							):
         schur_complement(lowercase_						,	lowercase_						,	lowercase_							)



 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							None:
     A__    =      np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
     A__    =      np.array([[0, 3], [3, 0], [2, 3]]							)
     A__    =      np.array([[2, 1, 3], [6, 3, 5]]							)

     with self.assertRaises(lowercase_							):
         schur_complement(lowercase_						,	lowercase_						,	lowercase_							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  unittest.main()



