




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      :						Dict							=  logging.get_logger(__name__)

_lowerCamelCase      :						int							=  """The Nymphenburg Palace is a beautiful palace in Munich!"""







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			Optional[int]:



       """simple docstring"""

       A__ 					=       {
           '''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,
       }

       A__ 					=       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__ 					=       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
       A__ 					=       '''openwebtext_ccnews_stories_books_cased'''

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

       A__ 					=       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_		)
       A__ 					=       original_bort._collect_params_with_prefix()

       # Build our config 🤗
       A__ 					=       {
           '''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_		),
       }

       A__ 					=       BertConfig.from_dict(lowercase_		)
       A__ 					=       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(lowercase_		) -> 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(lowercase_					,							lowercase_		):
              A__ 					=       hf_param.shape

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

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

              # self attention
              A__ 					=       layer.attention.self

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

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

              # self attention output
              A__ 					=       layer.attention.output

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

              # intermediate
              A__ 					=       layer.intermediate

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

              # output
              A__ 					=       layer.output

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

       A__ 					=       tokenizer.encode_plus(lowercase_		)['''input_ids''']

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

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

       A__ 					=       tokenizer.encode_plus(lowercase_					,							return_tensors='''pt'''		)
       A__ 					=       hf_bort_model(**lowercase_		)[0]

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

       A__ 					=       np.max(np.abs(hf_layer - gluon_layer		)		).item()
       A__ 					=       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__":
  _lowerCamelCase      :						Optional[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."""
  )
  _lowerCamelCase      :						str							=  parser.parse_args()
  convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''



from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


_UpperCamelCase						:  str							       =				{
    "configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"],
    "tokenization_gpt_neox_japanese": ["GPTNeoXJapaneseTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase						:  Dict							       =				[
         "GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GPTNeoXJapaneseForCausalLM",
         "GPTNeoXJapaneseLayer",
         "GPTNeoXJapaneseModel",
         "GPTNeoXJapanesePreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
     from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpt_neox_japanese import (
              GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTNeoXJapaneseForCausalLM,
              GPTNeoXJapaneseLayer,
              GPTNeoXJapaneseModel,
              GPTNeoXJapanesePreTrainedModel,
          )


else:
     import sys

     _UpperCamelCase						:  List[Any]							       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





