
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  : Optional[int]						   =							logging.get_logger(__name__)

lowercase  : Optional[int]						   =							'The Nymphenburg Palace is a beautiful palace in Munich!'







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[int]:
       lowercase							:					List[Any]	     =				{
           """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,
       }

       lowercase							:					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
       lowercase							:					Union[str, 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=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
       lowercase							:					List[Any]	     =				"""openwebtext_ccnews_stories_books_cased"""

       # Specify download folder to Gluonnlp's vocab
       lowercase							:					Optional[int]	     =				os.path.join(get_home_dir() ,     """models"""    )
       lowercase							:					Any	     =				_load_vocab(lowercase__ ,     lowercase__ ,     lowercase__ ,     cls=lowercase__    )

       lowercase							:					int	     =				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__    )
       lowercase							:					List[str]	     =				original_bort._collect_params_with_prefix()

       # Build our config 🤗
       lowercase							:					Dict	     =				{
           """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__    ),
       }

       lowercase							:					Optional[int]	     =				BertConfig.from_dict(lowercase__    )
       lowercase							:					Any	     =				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__    ) -> 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__    ):
              lowercase							:					Optional[int]	     =				hf_param.shape

              lowercase							:					Dict	     =				to_torch(params[gluon_param]    )
              lowercase							:					Dict	     =				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

       lowercase							:					Dict	     =				check_and_map_params(
           hf_bort_model.bert.embeddings.word_embeddings.weight ,     """word_embed.0.weight"""    )
       lowercase							:					List[Any]	     =				check_and_map_params(
           hf_bort_model.bert.embeddings.position_embeddings.weight ,     """encoder.position_weight"""    )
       lowercase							:					Optional[Any]	     =				check_and_map_params(
           hf_bort_model.bert.embeddings.LayerNorm.bias ,     """encoder.layer_norm.beta"""    )
       lowercase							:					List[str]	     =				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)
       lowercase							:					Union[str, Any]	     =				torch.zeros_like(
           hf_bort_model.bert.embeddings.token_type_embeddings.weight.data    )

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

              # self attention
              lowercase							:					int	     =				layer.attention.self

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

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

              # self attention output
              lowercase							:					Optional[Any]	     =				layer.attention.output

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

              # intermediate
              lowercase							:					Union[str, Any]	     =				layer.intermediate

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

              # output
              lowercase							:					Optional[Any]	     =				layer.output

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

       lowercase							:					int	     =				tokenizer.encode_plus(lowercase__    )["""input_ids"""]

       # Get gluon output
       lowercase							:					Optional[int]	     =				mx.nd.array([input_ids]    )
       lowercase							:					Union[str, Any]	     =				original_bort(inputs=lowercase__ ,     token_types=[]    )

       # Get Transformer output (save and reload model again)
       hf_bort_model.save_pretrained(lowercase__    )
       lowercase							:					List[str]	     =				BertModel.from_pretrained(lowercase__    )
       hf_bort_model.eval()

       lowercase							:					int	     =				tokenizer.encode_plus(lowercase__ ,     return_tensors="""pt"""    )
       lowercase							:					Dict	     =				hf_bort_model(**lowercase__    )[0]

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

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


from collections.abc import Callable

import numpy as np







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   np.array:
       lowercase							:					Optional[int]	     =				int(np.ceil((x_end - xa) / step_size    )    )
       lowercase							:					List[Any]	     =				np.zeros((n + 1,)    )
       lowercase							:					Optional[int]	     =				ya
       lowercase							:					Optional[int]	     =				xa

       for k in range(SCREAMING_SNAKE_CASE__    ):
              lowercase							:					str	     =				y[k] + step_size * ode_func(SCREAMING_SNAKE_CASE__ ,     y[k]    )
              lowercase							:					Union[str, Any]	     =				y[k] + (
                  (step_size / 2) * (ode_func(SCREAMING_SNAKE_CASE__ ,     y[k]    ) + ode_func(x + step_size ,     SCREAMING_SNAKE_CASE__    ))
              )
              x += step_size

       return y


if __name__ == "__main__":
  import doctest

  doctest.testmod()
