
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()
lowerCamelCase      =logging.get_logger(__name__)

lowerCamelCase      ="The Nymphenburg Palace is a beautiful palace in Munich!"


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   Any					=				{
          '''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,
      }

      UpperCamelCase__    :   Union[str, 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
      UpperCamelCase__    :   List[Any]					=				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=UpperCamelCase__ ,		output_all_encodings=UpperCamelCase__ ,		use_residual=predefined_args['''use_residual'''] ,		activation=predefined_args.get('''activation''' ,		'''gelu'''		) ,		layer_norm_eps=predefined_args.get('''layer_norm_eps''' ,		UpperCamelCase__		) ,		)

      # Vocab information needs to be fetched first
      # It's the same as RoBERTa, so RobertaTokenizer can be used later
      UpperCamelCase__    :   List[Any]					=				'''openwebtext_ccnews_stories_books_cased'''

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

      UpperCamelCase__    :   int					=				nlp.model.BERTModel(
          UpperCamelCase__ ,		len(UpperCamelCase__		) ,		units=predefined_args['''units'''] ,		embed_size=predefined_args['''embed_size'''] ,		embed_dropout=predefined_args['''embed_dropout'''] ,		word_embed=predefined_args['''word_embed'''] ,		use_pooler=UpperCamelCase__ ,		use_token_type_embed=UpperCamelCase__ ,		token_type_vocab_size=predefined_args['''token_type_vocab_size'''] ,		use_classifier=UpperCamelCase__ ,		use_decoder=UpperCamelCase__ ,		)

      original_bort.load_parameters(UpperCamelCase__ ,		cast_dtype=UpperCamelCase__ ,		ignore_extra=UpperCamelCase__		)
      UpperCamelCase__    :   Optional[int]					=				original_bort._collect_params_with_prefix()

      # Build our config 🤗
      UpperCamelCase__    :   List[str]					=				{
          '''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(UpperCamelCase__		),
      }

      UpperCamelCase__    :   Any					=				BertConfig.from_dict(UpperCamelCase__		)
      UpperCamelCase__    :   List[Any]					=				BertForMaskedLM(UpperCamelCase__		)
      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(UpperCamelCase__		) -> 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(UpperCamelCase__ ,		UpperCamelCase__		):
            UpperCamelCase__    :   str					=				hf_param.shape

            UpperCamelCase__    :   Optional[int]					=				to_torch(params[gluon_param]		)
            UpperCamelCase__    :   List[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

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

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

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

            UpperCamelCase__    :   Any					=				check_and_map_params(
                self_attn.key.bias.data ,		f'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias'''		)

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

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

            UpperCamelCase__    :   List[str]					=				check_and_map_params(
                self_output.dense.bias ,		f'''encoder.transformer_cells.{i}.proj.bias'''		)
            UpperCamelCase__    :   Optional[int]					=				check_and_map_params(
                self_output.dense.weight ,		f'''encoder.transformer_cells.{i}.proj.weight'''		)
            UpperCamelCase__    :   Dict					=				check_and_map_params(
                self_output.LayerNorm.bias ,		f'''encoder.transformer_cells.{i}.layer_norm.beta'''		)
            UpperCamelCase__    :   Tuple					=				check_and_map_params(
                self_output.LayerNorm.weight ,		f'''encoder.transformer_cells.{i}.layer_norm.gamma'''		)

            # intermediate
            UpperCamelCase__    :   BertIntermediate					=				layer.intermediate

            UpperCamelCase__    :   str					=				check_and_map_params(
                intermediate.dense.bias ,		f'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''		)
            UpperCamelCase__    :   Dict					=				check_and_map_params(
                intermediate.dense.weight ,		f'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''		)

            # output
            UpperCamelCase__    :   BertOutput					=				layer.output

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

      UpperCamelCase__    :   Optional[int]					=				tokenizer.encode_plus(UpperCamelCase__		)['''input_ids''']

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

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

      UpperCamelCase__    :   str					=				tokenizer.encode_plus(UpperCamelCase__ ,		return_tensors='''pt'''		)
      UpperCamelCase__    :   str					=				hf_bort_model(**UpperCamelCase__		)[0]

      UpperCamelCase__    :   Optional[Any]					=				output_gluon[0].asnumpy()
      UpperCamelCase__    :   Dict					=				output_hf[0].detach().numpy()

      UpperCamelCase__    :   int					=				np.max(np.abs(hf_layer - gluon_layer		)		).item()
      UpperCamelCase__    :   Optional[Any]					=				np.allclose(UpperCamelCase__ ,		UpperCamelCase__ ,		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:''' ,		UpperCamelCase__		)


if __name__ == "__main__":
    lowerCamelCase      =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."
    )
    lowerCamelCase      =parser.parse_args()
    convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ = 5_0_0_0_0_0_0_0		):
      UpperCamelCase__    :   Any					=				set()
      UpperCamelCase__    :   Any					=				int((limit - 2_4) ** (1 / 2)		)

      UpperCamelCase__    :   Dict					=				set(range(3 ,		prime_square_limit + 1 ,		2		)		)
      primes.add(2		)
      for p in range(3 ,		prime_square_limit + 1 ,		2		):
            if p not in primes:
                  continue
            primes.difference_update(set(range(p * p ,		prime_square_limit + 1 ,		UpperCamelCase__		)		)		)

      for primea in primes:
            UpperCamelCase__    :   Dict					=				primea * primea
            for primea in primes:
                  UpperCamelCase__    :   str					=				primea * primea * primea
                  if square + cube >= limit - 1_6:
                        break
                  for primea in primes:
                        UpperCamelCase__    :   Dict					=				primea * primea * primea * primea
                        UpperCamelCase__    :   Tuple					=				square + cube + tetr
                        if total >= limit:
                              break
                        ret.add(UpperCamelCase__		)

      return len(UpperCamelCase__		)


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


