
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()
lowercase__          =logging.get_logger(__name__)

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




def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    str  ):
 __a			:  Tuple		    =  {
     '''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			:  Tuple		    =  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			:  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
 __a			:  int		    =  '''openwebtext_ccnews_stories_books_cased'''

 # Specify download folder to Gluonnlp's vocab
 __a			:  List[str]		    =  os.path.join(get_home_dir()  ,       '''models'''  )
 __a			:  Dict		    =  _load_vocab(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       cls=lowerCAmelCase__  )

 __a			:  List[Any]		    =  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			:  str		    =  original_bort._collect_params_with_prefix()

 # Build our config 🤗
 __a			:  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__  ),
 }

 __a			:  List[str]		    =  BertConfig.from_dict(lowerCAmelCase__  )
 __a			:  Tuple		    =  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[str]  ) -> 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__							:    Optional[Any]  ,       lowerCAmelCase__							:    Union[str, Any]  ):
  __a			:  Any		    =  hf_param.shape

  __a			:  Tuple		    =  to_torch(params[gluon_param]  )
  __a			:  List[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			:  List[Any]		    =  check_and_map_params(
     hf_bort_model.bert.embeddings.word_embeddings.weight  ,       '''word_embed.0.weight'''  )
 __a			:  List[str]		    =  check_and_map_params(
     hf_bort_model.bert.embeddings.position_embeddings.weight  ,       '''encoder.position_weight'''  )
 __a			:  str		    =  check_and_map_params(
     hf_bort_model.bert.embeddings.LayerNorm.bias  ,       '''encoder.layer_norm.beta'''  )
 __a			:  Optional[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			:  int		    =  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			:  str		    =  check_and_map_params(
      self_attn.key.bias.data  ,       f"encoder.transformer_cells.{i}.attention_cell.proj_key.bias"  )

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

  # intermediate
  __a			:  BertIntermediate		    =  layer.intermediate

  __a			:  List[Any]		    =  check_and_map_params(
      intermediate.dense.bias  ,       f"encoder.transformer_cells.{i}.ffn.ffn_1.bias"  )
  __a			:  List[str]		    =  check_and_map_params(
      intermediate.dense.weight  ,       f"encoder.transformer_cells.{i}.ffn.ffn_1.weight"  )

  # output
  __a			:  BertOutput		    =  layer.output

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

 __a			:  List[Any]		    =  tokenizer.encode_plus(lowerCAmelCase__  )['''input_ids''']

 # Get gluon output
 __a			:  Optional[int]		    =  mx.nd.array([input_ids]  )
 __a			:  int		    =  original_bort(inputs=lowerCAmelCase__  ,       token_types=[]  )

 # Get Transformer output (save and reload model again)
 hf_bort_model.save_pretrained(lowerCAmelCase__  )
 __a			:  str		    =  BertModel.from_pretrained(lowerCAmelCase__  )
 hf_bort_model.eval()

 __a			:  Union[str, Any]		    =  tokenizer.encode_plus(lowerCAmelCase__  ,       return_tensors='''pt'''  )
 __a			:  Any		    =  hf_bort_model(**lowerCAmelCase__  )[0]

 __a			:  Any		    =  output_gluon[0].asnumpy()
 __a			:  Optional[int]		    =  output_hf[0].detach().numpy()

 __a			:  Union[str, Any]		    =  np.max(np.abs(hf_layer - gluon_layer  )  ).item()
 __a			:  Dict		    =  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__":
      lowercase__          =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.'
      )
      lowercase__          =parser.parse_args()
      convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)


def        __UpperCamelCase					(    lowerCAmelCase__							:    int = 1_0_0_0  ):
 __a			:  int		    =  -1
 __a			:  Any		    =  0
 for a in range(1  ,       n // 3  ):
  # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
  __a			:  List[Any]		    =  (n * n - 2 * a * n) // (2 * n - 2 * a)
  __a			:  Union[str, Any]		    =  n - a - b
  if c * c == (a * a + b * b):
   __a			:  Union[str, Any]		    =  a * b * c
   if candidate >= product:
    __a			:  List[Any]		    =  candidate
 return product


if __name__ == "__main__":
      print(F"""{solution() = }""")
