


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 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''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 :					Dict    =				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=SCREAMING_SNAKE_CASE_      ,						output_all_encodings=SCREAMING_SNAKE_CASE_      ,						use_residual=predefined_args["use_residual"]      ,						activation=predefined_args.get("activation"      ,						"gelu"	)      ,						layer_norm_eps=predefined_args.get("layer_norm_eps"      ,						SCREAMING_SNAKE_CASE_	)      ,						)

  # Vocab information needs to be fetched first
  # It's the same as RoBERTa, so RobertaTokenizer can be used later
  lowerCamelCase :					Tuple    =				"openwebtext_ccnews_stories_books_cased"

  # Specify download folder to Gluonnlp's vocab
  lowerCamelCase :					int    =				os.path.join(get_home_dir()      ,						"models"	)
  lowerCamelCase :					Dict    =				_load_vocab(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						cls=SCREAMING_SNAKE_CASE_	)

  lowerCamelCase :					Optional[Any]    =				nlp.model.BERTModel(
      SCREAMING_SNAKE_CASE_      ,						len(SCREAMING_SNAKE_CASE_	)      ,						units=predefined_args["units"]      ,						embed_size=predefined_args["embed_size"]      ,						embed_dropout=predefined_args["embed_dropout"]      ,						word_embed=predefined_args["word_embed"]      ,						use_pooler=SCREAMING_SNAKE_CASE_      ,						use_token_type_embed=SCREAMING_SNAKE_CASE_      ,						token_type_vocab_size=predefined_args["token_type_vocab_size"]      ,						use_classifier=SCREAMING_SNAKE_CASE_      ,						use_decoder=SCREAMING_SNAKE_CASE_      ,						)

  original_bort.load_parameters(SCREAMING_SNAKE_CASE_      ,						cast_dtype=SCREAMING_SNAKE_CASE_      ,						ignore_extra=SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					str    =				original_bort._collect_params_with_prefix()

  # Build our config 🤗
  lowerCamelCase :					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(SCREAMING_SNAKE_CASE_	),
  }

  lowerCamelCase :					Union[str, Any]    =				BertConfig.from_dict(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					List[Any]    =				BertForMaskedLM(SCREAMING_SNAKE_CASE_	)
  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_	) -> 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_      ,						SCREAMING_SNAKE_CASE_	):
    lowerCamelCase :					Union[str, Any]    =				hf_param.shape

    lowerCamelCase :					Tuple    =				to_torch(params[gluon_param]	)
    lowerCamelCase :					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

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

    lowerCamelCase :					Tuple    =				check_and_map_params(
        self_attn.key.weight.data      ,						f"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""	)
    lowerCamelCase :					Optional[int]    =				check_and_map_params(
        self_attn.query.bias.data      ,						f"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""	)
    lowerCamelCase :					Dict    =				check_and_map_params(
        self_attn.query.weight.data      ,						f"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""	)
    lowerCamelCase :					int    =				check_and_map_params(
        self_attn.value.bias.data      ,						f"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""	)
    lowerCamelCase :					Optional[int]    =				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 :					List[str]    =				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 :					Optional[int]    =				check_and_map_params(
        self_output.LayerNorm.bias      ,						f"""encoder.transformer_cells.{i}.layer_norm.beta"""	)
    lowerCamelCase :					List[str]    =				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 :					Any    =				check_and_map_params(
        intermediate.dense.weight      ,						f"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""	)

    # output
    lowerCamelCase :					BertOutput    =				layer.output

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

  lowerCamelCase :					Tuple    =				tokenizer.encode_plus(SCREAMING_SNAKE_CASE_	)["input_ids"]

  # Get gluon output
  lowerCamelCase :					List[str]    =				mx.nd.array([input_ids]	)
  lowerCamelCase :					Optional[int]    =				original_bort(inputs=SCREAMING_SNAKE_CASE_      ,						token_types=[]	)

  # Get Transformer output (save and reload model again)
  hf_bort_model.save_pretrained(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Dict    =				BertModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
  hf_bort_model.eval()

  lowerCamelCase :					Union[str, Any]    =				tokenizer.encode_plus(SCREAMING_SNAKE_CASE_      ,						return_tensors="pt"	)
  lowerCamelCase :					Dict    =				hf_bort_model(**SCREAMING_SNAKE_CASE_	)[0]

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

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


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)







def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if divisor % 5 == 0 or divisor % 2 == 0:
    return 0
  lowerCamelCase :					List[Any]    =				1
  lowerCamelCase :					Union[str, Any]    =				1
  while repunit:
    lowerCamelCase :					Union[str, Any]    =				(10 * repunit + 1) % divisor
    repunit_index += 1
  return repunit_index






def 		lowercase_(  SCREAMING_SNAKE_CASE_ = 1000000	):


  '''simple docstring'''

  lowerCamelCase :					List[str]    =				limit - 1
  if divisor % 2 == 0:
    divisor += 1
  while least_divisible_repunit(SCREAMING_SNAKE_CASE_	) <= limit:
    divisor += 2
  return divisor


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



