


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()
__a 					=   logging.get_logger(__name__)

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




def       __lowercase						(    _UpperCamelCase,     _UpperCamelCase  )     ->Tuple:







      """simple docstring"""

      lowercase      : 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,
      }

      lowercase      : int        =		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      : Optional[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
      lowercase      : Any        =		'''openwebtext_ccnews_stories_books_cased'''

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

      lowercase      : List[Any]        =		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  )
      lowercase      : Optional[Any]        =		original_bort._collect_params_with_prefix()

      # Build our config 🤗
      lowercase      : List[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''': 0.0_2,
          '''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  ),
      }

      lowercase      : List[str]        =		BertConfig.from_dict(_UpperCamelCase  )
      lowercase      : str        =		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  ):
            lowercase      : Optional[int]        =		hf_param.shape

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

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

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

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

            lowercase      : str        =		check_and_map_params(
                self_attn.key.weight.data,     f"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""  )
            lowercase      : int        =		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      : Optional[int]        =		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      : Tuple        =		layer.attention.output

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

            # intermediate
            lowercase      : Tuple        =		layer.intermediate

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

            # output
            lowercase      : str        =		layer.output

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

      lowercase      : Any        =		tokenizer.encode_plus(_UpperCamelCase  )['''input_ids''']

      # Get gluon output
      lowercase      : Tuple        =		mx.nd.array([input_ids]  )
      lowercase      : List[Any]        =		original_bort(inputs=_UpperCamelCase,     token_types=[]  )

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

      lowercase      : Tuple        =		tokenizer.encode_plus(_UpperCamelCase,     return_tensors='''pt'''  )
      lowercase      : Union[str, Any]        =		hf_bort_model(**_UpperCamelCase  )[0]

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

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







'''simple docstring'''


import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
    '''split_dict'''					,		[
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''					,		num_bytes=1337					,		num_examples=42					,		dataset_name='''my_dataset''' )} ),
        SplitDict({'''train''': SplitInfo(name='''train'''					,		num_bytes=1337					,		num_examples=42 )} ),
        SplitDict({'''train''': SplitInfo()} ),
    ]					,		)
def 						snake_case_       (UpperCamelCase			:    SplitDict ):





							'''simple docstring'''


							_a      	=							split_dict._to_yaml_list()
							assert len(UpperCamelCase ) == len(UpperCamelCase )
							_a      	=							SplitDict._from_yaml_list(UpperCamelCase )
							for split_name, split_info in split_dict.items():
														# dataset_name field is deprecated, and is therefore not part of the YAML dump
														_a      	=							None
														# the split name of split_dict takes over the name of the split info object
														_a      	=							split_name
							assert split_dict == reloaded

@pytest.mark.parametrize(
    '''split_info'''					,		[SplitInfo(), SplitInfo(dataset_name=UpperCamelCase ), SplitInfo(dataset_name='''my_dataset''' )] )
def 						snake_case_       (UpperCamelCase			:    List[str] ):





							'''simple docstring'''


							_a      	=							asdict(SplitDict({'''train''': split_info} ) )
							assert "dataset_name" in split_dict_asdict["train"]
							assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name

