



'''simple docstring'''





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_		       =  logging.get_logger(__name__)

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




def lowerCAmelCase  (__A  ,	__A):



     """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=__A  ,	output_all_encodings=__A  ,	use_residual=predefined_args['''use_residual''']  ,	activation=predefined_args.get('''activation'''  ,	'''gelu''')  ,	layer_norm_eps=predefined_args.get('''layer_norm_eps'''  ,	__A)  ,	)

     # 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(__A  ,	__A  ,	__A  ,	cls=__A)

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

     original_bort.load_parameters(__A  ,	cast_dtype=__A  ,	ignore_extra=__A)
     _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(__A),
     }

     _a      =  BertConfig.from_dict(__A)
     _a      =  BertForMaskedLM(__A)
     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(__A) -> 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(__A  ,	__A):
          _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(__A)['''input_ids''']

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

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

     _a      =  tokenizer.encode_plus(__A  ,	return_tensors='''pt''')
     _a      =  hf_bort_model(**__A)[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(__A  ,	__A  ,	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:'''  ,	__A)


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








'''simple docstring'''





import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Optional[Any]          =       'MCTCTFeatureExtractor'
 __lowerCamelCase    :       Optional[Any]          =       'AutoTokenizer'



 def __init__(self ,   A ,   A       )    ->     Dict:


      """simple docstring"""



      super().__init__(A ,   A       )
      _a      =  self.feature_extractor
      _a      =  False



 def __call__(self ,   *A ,   **A       )    ->     Optional[int]:


      """simple docstring"""



      if self._in_target_context_manager:
           return self.current_processor(*A ,   **A       )

      if "raw_speech" in kwargs:
           warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''       )
           _a      =  kwargs.pop('''raw_speech'''       )
      else:
           _a      =  kwargs.pop('''audio''' ,   A       )
      _a      =  kwargs.pop('''sampling_rate''' ,   A       )
      _a      =  kwargs.pop('''text''' ,   A       )
      if len(A       ) > 0:
           _a      =  args[0]
           _a      =  args[1:]

      if audio is None and text is None:
           raise ValueError('''You need to specify either an `audio` or `text` input to process.'''       )

      if audio is not None:
           _a      =  self.feature_extractor(A ,   *A ,   sampling_rate=A ,   **A       )
      if text is not None:
           _a      =  self.tokenizer(A ,   **A       )

      if text is None:
           return inputs
      elif audio is None:
           return encodings
      else:
           _a      =  encodings['''input_ids''']
           return inputs



 def 					a__  (self ,   *A ,   **A       )    ->     int:


      """simple docstring"""



      return self.tokenizer.batch_decode(*A ,   **A       )



 def 					a__  (self ,   *A ,   **A       )    ->     Tuple:


      """simple docstring"""



      if self._in_target_context_manager:
           return self.current_processor.pad(*A ,   **A       )

      _a      =  kwargs.pop('''input_features''' ,   A       )
      _a      =  kwargs.pop('''labels''' ,   A       )
      if len(A       ) > 0:
           _a      =  args[0]
           _a      =  args[1:]

      if input_features is not None:
           _a      =  self.feature_extractor.pad(A ,   *A ,   **A       )
      if labels is not None:
           _a      =  self.tokenizer.pad(A ,   **A       )

      if labels is None:
           return input_features
      elif input_features is None:
           return labels
      else:
           _a      =  labels['''input_ids''']
           return input_features



 def 					a__  (self ,   *A ,   **A       )    ->     List[str]:


      """simple docstring"""



      return self.tokenizer.decode(*A ,   **A       )



 @contextmanager
 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      warnings.warn(
          '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
          '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
          '''your audio inputs, or in a separate call.'''       )
      _a      =  True
      _a      =  self.tokenizer
      yield
      _a      =  self.feature_extractor
      _a      =  False



