


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   :					Optional[int]	=      logging.get_logger(__name__)

A   :					int	=      "The Nymphenburg Palace is a beautiful palace in Munich!"

def    lowercase_	(	_A    :					str					,      _A    :					str		):

      """simple docstring"""




      lowerCamelCase__	:       Optional[Any]					=						{
          "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__	:       List[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__	:       List[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=_A					,      output_all_encodings=_A					,      use_residual=predefined_args["use_residual"]					,      activation=predefined_args.get("activation"					,      "gelu"		)					,      layer_norm_eps=predefined_args.get("layer_norm_eps"					,      _A		)					,      )

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

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

      original_bort.load_parameters(_A					,      cast_dtype=_A					,      ignore_extra=_A		)
      lowerCamelCase__	:       List[Any]					=						original_bort._collect_params_with_prefix()

      # Build our config 🤗
      lowerCamelCase__	:       int					=						{
          "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(_A		),
      }

      lowerCamelCase__	:       Optional[Any]					=						BertConfig.from_dict(_A		)
      lowerCamelCase__	:       Optional[Any]					=						BertForMaskedLM(_A		)
      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    :					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(_A    :					Any					,      _A    :					str		):
            lowerCamelCase__	:       List[Any]					=						hf_param.shape

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

            lowerCamelCase__	:       int					=						check_and_map_params(
                self_attn.key.weight.data					,      F"encoder.transformer_cells.{i}.attention_cell.proj_key.weight"		)
            lowerCamelCase__	:       Union[str, Any]					=						check_and_map_params(
                self_attn.query.bias.data					,      F"encoder.transformer_cells.{i}.attention_cell.proj_query.bias"		)
            lowerCamelCase__	:       Optional[int]					=						check_and_map_params(
                self_attn.query.weight.data					,      F"encoder.transformer_cells.{i}.attention_cell.proj_query.weight"		)
            lowerCamelCase__	:       List[Any]					=						check_and_map_params(
                self_attn.value.bias.data					,      F"encoder.transformer_cells.{i}.attention_cell.proj_value.bias"		)
            lowerCamelCase__	:       List[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__	:       Dict					=						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__	:       Tuple					=						check_and_map_params(
                self_output.LayerNorm.bias					,      F"encoder.transformer_cells.{i}.layer_norm.beta"		)
            lowerCamelCase__	:       Optional[int]					=						check_and_map_params(
                self_output.LayerNorm.weight					,      F"encoder.transformer_cells.{i}.layer_norm.gamma"		)

            # intermediate
            lowerCamelCase__	:       BertIntermediate					=						layer.intermediate

            lowerCamelCase__	:       Union[str, Any]					=						check_and_map_params(
                intermediate.dense.bias					,      F"encoder.transformer_cells.{i}.ffn.ffn_1.bias"		)
            lowerCamelCase__	:       Optional[int]					=						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__	:       Tuple					=						check_and_map_params(
                bert_output.dense.weight					,      F"encoder.transformer_cells.{i}.ffn.ffn_2.weight"		)
            lowerCamelCase__	:       Tuple					=						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__	:       List[str]					=						RobertaTokenizer.from_pretrained("roberta-base"		)

      lowerCamelCase__	:       Tuple					=						tokenizer.encode_plus(_A		)["input_ids"]

      # Get gluon output
      lowerCamelCase__	:       Union[str, Any]					=						mx.nd.array([input_ids]		)
      lowerCamelCase__	:       Optional[Any]					=						original_bort(inputs=_A					,      token_types=[]		)

      # Get Transformer output (save and reload model again)
      hf_bort_model.save_pretrained(_A		)
      lowerCamelCase__	:       List[Any]					=						BertModel.from_pretrained(_A		)
      hf_bort_model.eval()

      lowerCamelCase__	:       int					=						tokenizer.encode_plus(_A					,      return_tensors="pt"		)
      lowerCamelCase__	:       Any					=						hf_bort_model(**_A		)[0]

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

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


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






def    lowercase_	(	_A    :					int					,      _A    :					int		):

      """simple docstring"""




      if a < 0 or b < 0:
            raise ValueError("the value of both inputs must be positive"		)

      lowerCamelCase__	:       List[str]					=						str(bin(_A		)		)[2:]  # remove the leading "0b"
      lowerCamelCase__	:       List[Any]					=						str(bin(_A		)		)[2:]  # remove the leading "0b"

      lowerCamelCase__	:       List[Any]					=						max(len(_A		)					,      len(_A		)		)

      return "0b" + "".join(
          str(int(char_a != char_b		)		)
          for char_a, char_b in zip(a_binary.zfill(_A		)					,      b_binary.zfill(_A		)		)		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


