





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()
UpperCAmelCase					:		Union[str, Any]		=							logging.get_logger(__name__)

UpperCAmelCase					:		Tuple		=							'The Nymphenburg Palace is a beautiful palace in Munich!'




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	str					     =     {
          '''attention_cell''': '''multi_head''',
          '''num_layers''': 4,
          '''units''': 1_024,
          '''hidden_size''': 768,
          '''max_length''': 512,
          '''num_heads''': 8,
          '''scaled''': True,
          '''dropout''': 0.1,
          '''use_residual''': True,
          '''embed_size''': 1_024,
          '''embed_dropout''': 0.1,
          '''word_embed''': None,
          '''layer_norm_eps''': 1E-5,
          '''token_type_vocab_size''': 2,
      }

      _snake_case		:	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
      _snake_case		:	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
      _snake_case		:	int					     =     '''openwebtext_ccnews_stories_books_cased'''

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

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

      # Build our config 🤗
      _snake_case		:	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''': 0.02,
          '''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(lowerCAmelCase_	),
      }

      _snake_case		:	Union[str, Any]					     =     BertConfig.from_dict(lowerCAmelCase_	)
      _snake_case		:	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(lowerCAmelCase_	) -> 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_			,      lowerCAmelCase_	):
            _snake_case		:	Dict					     =     hf_param.shape

            _snake_case		:	Union[str, Any]					     =     to_torch(params[gluon_param]	)
            _snake_case		:	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

      _snake_case		:	Union[str, Any]					     =     check_and_map_params(
          hf_bort_model.bert.embeddings.word_embeddings.weight			,      '''word_embed.0.weight'''	)
      _snake_case		:	Union[str, Any]					     =     check_and_map_params(
          hf_bort_model.bert.embeddings.position_embeddings.weight			,      '''encoder.position_weight'''	)
      _snake_case		:	int					     =     check_and_map_params(
          hf_bort_model.bert.embeddings.LayerNorm.bias			,      '''encoder.layer_norm.beta'''	)
      _snake_case		:	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)
      _snake_case		:	Tuple					     =     torch.zeros_like(
          hf_bort_model.bert.embeddings.token_type_embeddings.weight.data	)

      for i in range(hf_bort_config.num_hidden_layers	):
            _snake_case		:	BertLayer					     =     hf_bort_model.bert.encoder.layer[i]

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

            _snake_case		:	List[str]					     =     check_and_map_params(
                self_attn.key.bias.data			,      f'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias'''	)

            _snake_case		:	List[str]					     =     check_and_map_params(
                self_attn.key.weight.data			,      f'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight'''	)
            _snake_case		:	int					     =     check_and_map_params(
                self_attn.query.bias.data			,      f'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias'''	)
            _snake_case		:	List[Any]					     =     check_and_map_params(
                self_attn.query.weight.data			,      f'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight'''	)
            _snake_case		:	List[str]					     =     check_and_map_params(
                self_attn.value.bias.data			,      f'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias'''	)
            _snake_case		:	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
            _snake_case		:	BertSelfOutput					     =     layer.attention.output

            _snake_case		:	Union[str, Any]					     =     check_and_map_params(
                self_output.dense.bias			,      f'''encoder.transformer_cells.{i}.proj.bias'''	)
            _snake_case		:	str					     =     check_and_map_params(
                self_output.dense.weight			,      f'''encoder.transformer_cells.{i}.proj.weight'''	)
            _snake_case		:	Any					     =     check_and_map_params(
                self_output.LayerNorm.bias			,      f'''encoder.transformer_cells.{i}.layer_norm.beta'''	)
            _snake_case		:	Tuple					     =     check_and_map_params(
                self_output.LayerNorm.weight			,      f'''encoder.transformer_cells.{i}.layer_norm.gamma'''	)

            # intermediate
            _snake_case		:	BertIntermediate					     =     layer.intermediate

            _snake_case		:	List[str]					     =     check_and_map_params(
                intermediate.dense.bias			,      f'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''	)
            _snake_case		:	Tuple					     =     check_and_map_params(
                intermediate.dense.weight			,      f'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''	)

            # output
            _snake_case		:	BertOutput					     =     layer.output

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

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

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

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

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

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

      _snake_case		:	Dict					     =     np.max(np.abs(hf_layer - gluon_layer	)	).item()
      _snake_case		:	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__":
   UpperCAmelCase					:		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.'
   )
   UpperCAmelCase					:		Tuple		=							parser.parse_args()
   convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''

from collections.abc import Generator




def    _a    (							):





      """simple docstring"""



      _snake_case     ,							_snake_case		:	Union[str, Any]					     =     0, 1
      while True:
            _snake_case     ,							_snake_case		:	List[str]					     =     b, a + b
            yield b




def    _a    (							lowerCAmelCase_ = 1_000	):





      """simple docstring"""



      _snake_case		:	List[str]					     =     1
      _snake_case		:	Dict					     =     fibonacci_generator()
      while len(str(next(lowerCAmelCase_	)	)	) < n:
            answer += 1
      return answer + 1


if __name__ == "__main__":
   print(solution(int(str(input()).strip())))


