



"""simple docstring"""




import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class       UpperCAmelCase_    (							_a			,     unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:  int						=					PhobertTokenizer
      __SCREAMING_SNAKE_CASE		:  int						=					False



      def 		snake_case_   (	self	:      Optional[Any]		):
           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           _UpperCAmelCase     :      Union[str, Any]														=       ["T@@", "i", "I", "R@@", "r", "e@@"]
           _UpperCAmelCase     :      Tuple														=       dict(zip(__lowerCamelCase	,      range(len(__lowerCamelCase		)		)		)		)
           _UpperCAmelCase     :      Dict														=       ["#version: 0.2", "l à</w>"]
           _UpperCAmelCase     :      Dict														=       {"unk_token": "<unk>"}

           _UpperCAmelCase     :      List[str]														=       os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES["vocab_file"]		)
           _UpperCAmelCase     :      str														=       os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES["merges_file"]		)

           with open(self.vocab_file	,      "w"	,      encoding="utf-8"		) as fp:
                for token in vocab_tokens:
                     fp.write(f'{token} {vocab_tokens[token]}\n'		)
           with open(self.merges_file	,      "w"	,      encoding="utf-8"		) as fp:
                fp.write("\n".join(__lowerCamelCase		)		)



      def 		snake_case_   (	self	:      Any	,      **A	:      Tuple		):
           kwargs.update(self.special_tokens_map		)
           return PhobertTokenizer.from_pretrained(self.tmpdirname	,      **__lowerCamelCase		)



      def 		snake_case_   (	self	:      Optional[Any]	,      A	:      Tuple		):
           _UpperCAmelCase     :      List[str]														=       "Tôi là VinAI Research"
           _UpperCAmelCase     :      Tuple														=       "T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
           return input_text, output_text



      def 		snake_case_   (	self	:      Tuple		):
           _UpperCAmelCase     :      str														=       PhobertTokenizer(self.vocab_file	,      self.merges_file	,      **self.special_tokens_map		)
           _UpperCAmelCase     :      Tuple														=       "Tôi là VinAI Research"
           _UpperCAmelCase     :      Dict														=       "T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
           _UpperCAmelCase     :      List[str]														=       tokenizer.tokenize(__lowerCamelCase		)
           print(__lowerCamelCase		)
           self.assertListEqual(__lowerCamelCase	,      __lowerCamelCase		)

           _UpperCAmelCase     :      List[Any]														=       tokens + [tokenizer.unk_token]

           _UpperCAmelCase     :      Any														=       [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase		)	,      __lowerCamelCase		)






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()
__magic_name__  :  Tuple					=       logging.get_logger(__name__)

__magic_name__  :  Optional[Any]					=       """The Nymphenburg Palace is a beautiful palace in Munich!"""
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      {
      '''attention_cell''': '''multi_head''',
      '''num_layers''': 4,
      '''units''': 10_24,
      '''hidden_size''': 7_68,
      '''max_length''': 5_12,
      '''num_heads''': 8,
      '''scaled''': True,
      '''dropout''': 0.1,
      '''use_residual''': True,
      '''embed_size''': 10_24,
      '''embed_dropout''': 0.1,
      '''word_embed''': None,
      '''layer_norm_eps''': 1E-5,
      '''token_type_vocab_size''': 2,
  }

  lowerCAmelCase__										=      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__										=      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=__lowerCAmelCase   , output_all_encodings=__lowerCAmelCase   , use_residual=predefined_args['''use_residual''']   , activation=predefined_args.get('''activation'''   , '''gelu'''				)   , layer_norm_eps=predefined_args.get('''layer_norm_eps'''   , __lowerCAmelCase				)   , )

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

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

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

  original_bort.load_parameters(__lowerCAmelCase   , cast_dtype=__lowerCAmelCase   , ignore_extra=__lowerCAmelCase				)
  lowerCAmelCase__										=      original_bort._collect_params_with_prefix()

  # Build our config 🤗
  lowerCAmelCase__										=      {
      '''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(__lowerCAmelCase				),
  }

  lowerCAmelCase__										=      BertConfig.from_dict(__lowerCAmelCase				)
  lowerCAmelCase__										=      BertForMaskedLM(__lowerCAmelCase				)
  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(__lowerCAmelCase				) -> 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(__lowerCAmelCase   , __lowerCAmelCase				):
    lowerCAmelCase__										=      hf_param.shape

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

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

    # self attention
    lowerCAmelCase__										=      layer.attention.self

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

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

    # self attention output
    lowerCAmelCase__										=      layer.attention.output

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

    # intermediate
    lowerCAmelCase__										=      layer.intermediate

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

    # output
    lowerCAmelCase__										=      layer.output

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

  lowerCAmelCase__										=      tokenizer.encode_plus(__lowerCAmelCase				)['''input_ids''']

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

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

  lowerCAmelCase__										=      tokenizer.encode_plus(__lowerCAmelCase   , return_tensors='''pt'''				)
  lowerCAmelCase__										=      hf_bort_model(**__lowerCAmelCase				)[0]

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

  lowerCAmelCase__										=      np.max(np.abs(hf_layer - gluon_layer				)				).item()
  lowerCAmelCase__										=      np.allclose(__lowerCAmelCase   , __lowerCAmelCase   , 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:'''   , __lowerCAmelCase				)


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

