
"""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()
_snake_case          =						logging.get_logger(__name__)

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






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					Union[str, Any]						):






       """simple docstring"""



       _lowerCAmelCase  	=			{
           '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  	=			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  	=			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
       _lowerCAmelCase  	=			'openwebtext_ccnews_stories_books_cased'

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

       _lowerCAmelCase  	=			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__						)
       _lowerCAmelCase  	=			original_bort._collect_params_with_prefix()

       # Build our config 🤗
       _lowerCAmelCase  	=			{
           '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__						),
       }

       _lowerCAmelCase  	=			BertConfig.from_dict(lowercase__						)
       _lowerCAmelCase  	=			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(SCREAMING_SNAKE_CASE:					int						) -> 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(SCREAMING_SNAKE_CASE:					Dict     ,  SCREAMING_SNAKE_CASE:					str						):
              _lowerCAmelCase  	=			hf_param.shape

              _lowerCAmelCase  	=			to_torch(params[gluon_param]						)
              _lowerCAmelCase  	=			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  	=			check_and_map_params(
           hf_bort_model.bert.embeddings.word_embeddings.weight     ,  'word_embed.0.weight'						)
       _lowerCAmelCase  	=			check_and_map_params(
           hf_bort_model.bert.embeddings.position_embeddings.weight     ,  'encoder.position_weight'						)
       _lowerCAmelCase  	=			check_and_map_params(
           hf_bort_model.bert.embeddings.LayerNorm.bias     ,  'encoder.layer_norm.beta'						)
       _lowerCAmelCase  	=			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  	=			torch.zeros_like(
           hf_bort_model.bert.embeddings.token_type_embeddings.weight.data						)

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

              # self attention
              _lowerCAmelCase  	=			layer.attention.self

              _lowerCAmelCase  	=			check_and_map_params(
                  self_attn.key.bias.data     ,  f"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""						)

              _lowerCAmelCase  	=			check_and_map_params(
                  self_attn.key.weight.data     ,  f"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""						)
              _lowerCAmelCase  	=			check_and_map_params(
                  self_attn.query.bias.data     ,  f"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""						)
              _lowerCAmelCase  	=			check_and_map_params(
                  self_attn.query.weight.data     ,  f"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""						)
              _lowerCAmelCase  	=			check_and_map_params(
                  self_attn.value.bias.data     ,  f"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""						)
              _lowerCAmelCase  	=			check_and_map_params(
                  self_attn.value.weight.data     ,  f"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight"""						)

              # self attention output
              _lowerCAmelCase  	=			layer.attention.output

              _lowerCAmelCase  	=			check_and_map_params(
                  self_output.dense.bias     ,  f"""encoder.transformer_cells.{i}.proj.bias"""						)
              _lowerCAmelCase  	=			check_and_map_params(
                  self_output.dense.weight     ,  f"""encoder.transformer_cells.{i}.proj.weight"""						)
              _lowerCAmelCase  	=			check_and_map_params(
                  self_output.LayerNorm.bias     ,  f"""encoder.transformer_cells.{i}.layer_norm.beta"""						)
              _lowerCAmelCase  	=			check_and_map_params(
                  self_output.LayerNorm.weight     ,  f"""encoder.transformer_cells.{i}.layer_norm.gamma"""						)

              # intermediate
              _lowerCAmelCase  	=			layer.intermediate

              _lowerCAmelCase  	=			check_and_map_params(
                  intermediate.dense.bias     ,  f"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""						)
              _lowerCAmelCase  	=			check_and_map_params(
                  intermediate.dense.weight     ,  f"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""						)

              # output
              _lowerCAmelCase  	=			layer.output

              _lowerCAmelCase  	=			check_and_map_params(
                  bert_output.dense.bias     ,  f"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""						)
              _lowerCAmelCase  	=			check_and_map_params(
                  bert_output.dense.weight     ,  f"""encoder.transformer_cells.{i}.ffn.ffn_2.weight"""						)
              _lowerCAmelCase  	=			check_and_map_params(
                  bert_output.LayerNorm.bias     ,  f"""encoder.transformer_cells.{i}.ffn.layer_norm.beta"""						)
              _lowerCAmelCase  	=			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  	=			RobertaTokenizer.from_pretrained('roberta-base'						)

       _lowerCAmelCase  	=			tokenizer.encode_plus(lowercase__						)['input_ids']

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

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

       _lowerCAmelCase  	=			tokenizer.encode_plus(lowercase__     ,  return_tensors='pt'						)
       _lowerCAmelCase  	=			hf_bort_model(**lowercase__						)[0]

       _lowerCAmelCase  	=			output_gluon[0].asnumpy()
       _lowerCAmelCase  	=			output_hf[0].detach().numpy()

       _lowerCAmelCase  	=			np.max(np.abs(hf_layer - gluon_layer						)						).item()
       _lowerCAmelCase  	=			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          =						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          =						parser.parse_args()
 convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)








"""simple docstring"""



def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[int(SCREAMING_SNAKE_CASE						) for i in ip_va_address.split('.'						) if i.isdigit()]
							return len(SCREAMING_SNAKE_CASE						) == 4 and all(0 <= int(SCREAMING_SNAKE_CASE						) <= 254 for octet in octets						)


if __name__ == "__main__":
	_snake_case          =						input().strip()
	_snake_case          =						'''valid''' if is_ip_va_address_valid(ip) else '''invalid'''
	print(f'{ip} is a {valid_or_invalid} IP v4 address.')






