



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()
snake_case    :						int  =						logging.get_logger(__name__)

snake_case    :						Optional[int]  =						'The Nymphenburg Palace is a beautiful palace in Munich!'
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		Optional[int]:







      """simple docstring"""

      A__	:							Dict						    = {
          "attention_cell": "multi_head",
          "num_layers": 4,
          "units": 1_0_2_4,
          "hidden_size": 7_6_8,
          "max_length": 5_1_2,
          "num_heads": 8,
          "scaled": True,
          "dropout": 0.1,
          "use_residual": True,
          "embed_size": 1_0_2_4,
          "embed_dropout": 0.1,
          "word_embed": None,
          "layer_norm_eps": 1E-5,
          "token_type_vocab_size": 2,
      }

      A__	:							Any						    = 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__	:							int						    = 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__	:							Any						    = "openwebtext_ccnews_stories_books_cased"

      # Specify download folder to Gluonnlp's vocab
      A__	:							List[Any]						    = os.path.join(get_home_dir()							,     "models"       )
      A__	:							int						    = _load_vocab(__lowercase							,     __lowercase							,     __lowercase							,     cls=__lowercase       )

      A__	:							str						    = 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__	:							Optional[int]						    = original_bort._collect_params_with_prefix()

      # Build our config 🤗
      A__	:							Optional[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(__lowercase       ),
      }

      A__	:							Optional[int]						    = BertConfig.from_dict(__lowercase       )
      A__	:							Union[str, Any]						    = 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__	:							str						    = hf_param.shape

            A__	:							int						    = to_torch(params[gluon_param]       )
            A__	:							Optional[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

      A__	:							Dict						    = check_and_map_params(
          hf_bort_model.bert.embeddings.word_embeddings.weight							,     "word_embed.0.weight"       )
      A__	:							List[Any]						    = check_and_map_params(
          hf_bort_model.bert.embeddings.position_embeddings.weight							,     "encoder.position_weight"       )
      A__	:							Optional[int]						    = check_and_map_params(
          hf_bort_model.bert.embeddings.LayerNorm.bias							,     "encoder.layer_norm.beta"       )
      A__	:							Union[str, 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)
      A__	:							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       ):
            A__	:							BertLayer						    = hf_bort_model.bert.encoder.layer[i]

            # self attention
            A__	:							BertSelfAttention						    = layer.attention.self

            A__	:							List[Any]						    = check_and_map_params(
                self_attn.key.bias.data							,     F'encoder.transformer_cells.{i}.attention_cell.proj_key.bias'       )

            A__	:							Optional[int]						    = check_and_map_params(
                self_attn.key.weight.data							,     F'encoder.transformer_cells.{i}.attention_cell.proj_key.weight'       )
            A__	:							Union[str, Any]						    = check_and_map_params(
                self_attn.query.bias.data							,     F'encoder.transformer_cells.{i}.attention_cell.proj_query.bias'       )
            A__	:							Any						    = check_and_map_params(
                self_attn.query.weight.data							,     F'encoder.transformer_cells.{i}.attention_cell.proj_query.weight'       )
            A__	:							int						    = check_and_map_params(
                self_attn.value.bias.data							,     F'encoder.transformer_cells.{i}.attention_cell.proj_value.bias'       )
            A__	:							Dict						    = check_and_map_params(
                self_attn.value.weight.data							,     F'encoder.transformer_cells.{i}.attention_cell.proj_value.weight'       )

            # self attention output
            A__	:							BertSelfOutput						    = layer.attention.output

            A__	:							Union[str, Any]						    = check_and_map_params(
                self_output.dense.bias							,     F'encoder.transformer_cells.{i}.proj.bias'       )
            A__	:							Optional[int]						    = check_and_map_params(
                self_output.dense.weight							,     F'encoder.transformer_cells.{i}.proj.weight'       )
            A__	:							Optional[int]						    = check_and_map_params(
                self_output.LayerNorm.bias							,     F'encoder.transformer_cells.{i}.layer_norm.beta'       )
            A__	:							int						    = check_and_map_params(
                self_output.LayerNorm.weight							,     F'encoder.transformer_cells.{i}.layer_norm.gamma'       )

            # intermediate
            A__	:							BertIntermediate						    = layer.intermediate

            A__	:							Union[str, Any]						    = check_and_map_params(
                intermediate.dense.bias							,     F'encoder.transformer_cells.{i}.ffn.ffn_1.bias'       )
            A__	:							Union[str, Any]						    = check_and_map_params(
                intermediate.dense.weight							,     F'encoder.transformer_cells.{i}.ffn.ffn_1.weight'       )

            # output
            A__	:							BertOutput						    = layer.output

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

      A__	:							Optional[Any]						    = tokenizer.encode_plus(__lowercase       )["input_ids"]

      # Get gluon output
      A__	:							Union[str, Any]						    = mx.nd.array([input_ids]       )
      A__	:							List[str]						    = original_bort(inputs=__lowercase							,     token_types=[]       )

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

      A__	:							List[Any]						    = tokenizer.encode_plus(__lowercase							,     return_tensors="pt"       )
      A__	:							Optional[Any]						    = hf_bort_model(**__lowercase       )[0]

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

      A__	:							Tuple						    = np.max(np.abs(hf_layer - gluon_layer       )       ).item()
      A__	:							int						    = 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__":
  snake_case    :						Dict  =						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.'
  )
  snake_case    :						Optional[int]  =						parser.parse_args()
  convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




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







						"""simple docstring"""

						A__	:							Tuple						    = []
						A__	:							Optional[int]						    = 1

						while len(__lowercase       ) < 1E6:
												constant.append(str(__lowercase       )       )
												i += 1

						A__	:							Any						    = "".join(__lowercase       )

						return (
						    int(constant[0]       )
						    * int(constant[9]       )
						    * int(constant[9_9]       )
						    * int(constant[9_9_9]       )
						    * int(constant[9_9_9_9]       )
						    * int(constant[9_9_9_9_9]       )
						    * int(constant[9_9_9_9_9_9]       )
						)


if __name__ == "__main__":
		print(solution())