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()
UpperCAmelCase_           =				logging.get_logger(__name__)

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




def    lowerCAmelCase_  (		lowercase:				str	,       lowercase:				str		)     ->					Any:







 '''simple docstring'''
 _UpperCamelCase:  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,
 }

 _UpperCamelCase:  Optional[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
 _UpperCamelCase:  Optional[int]    						=			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
 _UpperCamelCase:  str    						=			'''openwebtext_ccnews_stories_books_cased'''

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

 _UpperCamelCase:  Union[str, Any]    						=			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		)
 _UpperCamelCase:  Any    						=			original_bort._collect_params_with_prefix()

 # Build our config 🤗
 _UpperCamelCase:  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''': 0.02,
     '''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		),
 }

 _UpperCamelCase:  Optional[int]    						=			BertConfig.from_dict(lowercase		)
 _UpperCamelCase:  Dict    						=			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:				Optional[int]		) -> 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:				Dict	,       lowercase:				int		):
  _UpperCamelCase:  List[Any]    						=			hf_param.shape

  _UpperCamelCase:  List[str]    						=			to_torch(params[gluon_param]		)
  _UpperCamelCase:  str    						=			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

 _UpperCamelCase:  Optional[Any]    						=			check_and_map_params(
     hf_bort_model.bert.embeddings.word_embeddings.weight	,       '''word_embed.0.weight'''		)
 _UpperCamelCase:  Tuple    						=			check_and_map_params(
     hf_bort_model.bert.embeddings.position_embeddings.weight	,       '''encoder.position_weight'''		)
 _UpperCamelCase:  str    						=			check_and_map_params(
     hf_bort_model.bert.embeddings.LayerNorm.bias	,       '''encoder.layer_norm.beta'''		)
 _UpperCamelCase:  Optional[int]    						=			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)
 _UpperCamelCase:  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		):
  _UpperCamelCase:  BertLayer    						=			hf_bort_model.bert.encoder.layer[i]

  # self attention
  _UpperCamelCase:  BertSelfAttention    						=			layer.attention.self

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

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

  # self attention output
  _UpperCamelCase:  BertSelfOutput    						=			layer.attention.output

  _UpperCamelCase:  Tuple    						=			check_and_map_params(
      self_output.dense.bias	,       F"""encoder.transformer_cells.{i}.proj.bias"""		)
  _UpperCamelCase:  Dict    						=			check_and_map_params(
      self_output.dense.weight	,       F"""encoder.transformer_cells.{i}.proj.weight"""		)
  _UpperCamelCase:  str    						=			check_and_map_params(
      self_output.LayerNorm.bias	,       F"""encoder.transformer_cells.{i}.layer_norm.beta"""		)
  _UpperCamelCase:  Any    						=			check_and_map_params(
      self_output.LayerNorm.weight	,       F"""encoder.transformer_cells.{i}.layer_norm.gamma"""		)

  # intermediate
  _UpperCamelCase:  BertIntermediate    						=			layer.intermediate

  _UpperCamelCase:  Optional[int]    						=			check_and_map_params(
      intermediate.dense.bias	,       F"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""		)
  _UpperCamelCase:  int    						=			check_and_map_params(
      intermediate.dense.weight	,       F"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""		)

  # output
  _UpperCamelCase:  BertOutput    						=			layer.output

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

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

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

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

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

 _UpperCamelCase:  Tuple    						=			output_gluon[0].asnumpy()
 _UpperCamelCase:  Optional[int]    						=			output_hf[0].detach().numpy()

 _UpperCamelCase:  Any    						=			np.max(np.abs(hf_layer - gluon_layer		)		).item()
 _UpperCamelCase:  Optional[Any]    						=			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__":
  UpperCAmelCase_           =				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.'''
  )
  UpperCAmelCase_           =				parser.parse_args()
  convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)
import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


UpperCAmelCase_           =				logging.getLogger(__name__)




def    lowerCAmelCase_  (		)     ->					Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:  List[str]    						=			argparse.ArgumentParser(
     description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'''		)
 parser.add_argument(
     '''--dataset_name'''	,       type=lowercase	,       default='''wikitext'''	,       help='''Name of the training. Explore datasets at: hf.co/datasets.'''	,       )
 parser.add_argument(
     '''--dataset_config'''	,       type=lowercase	,       default='''wikitext-103-raw-v1'''	,       help='''Configuration name of the dataset.'''		)
 parser.add_argument(
     '''--tokenizer_name_or_path'''	,       type=lowercase	,       default='''sayakpaul/unigram-tokenizer-wikitext'''	,       help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.'''	,       )
 parser.add_argument(
     '''--shard_size'''	,       type=lowercase	,       default=1_000	,       help='''Number of entries to go in a single shard.'''	,       )
 parser.add_argument('''--split'''	,       type=lowercase	,       default='''train'''	,       choices=['''train''', '''test''', '''validation''']		)
 parser.add_argument(
     '''--limit'''	,       default=lowercase	,       type=lowercase	,       help='''Limit the number of shards (used for debugging).'''	,       )
 parser.add_argument(
     '''--max_length'''	,       type=lowercase	,       default=512	,       help='''Maximum sequence length. For training on TPUs, it helps to have a maximum'''
     ''' sequence length that is a multiple of 8.'''	,       )
 parser.add_argument(
     '''--output_dir'''	,       default='''tf-tpu'''	,       type=lowercase	,       help='''Output directory where the TFRecord shards will be saved. If the'''
     ''' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'''
     ''' shards will be directly saved to a Google Cloud Storage bucket.'''	,       )

 _UpperCamelCase:  str    						=			parser.parse_args()
 return args




def    lowerCAmelCase_  (		lowercase:				Optional[int]		)     ->					Optional[Any]:







 '''simple docstring'''
 def fn(lowercase:				List[Any]		):
  return tokenizer(examples['''text''']		)

 return fn




def    lowerCAmelCase_  (		lowercase:				List[str]		)     ->					Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:  int    						=			[]
 for i in range(len(tokenized_data['''input_ids''']		)		):
  _UpperCamelCase:  Tuple    						=			{
      '''input_ids''': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['''input_ids'''][i]		)		),
      '''attention_mask''': tf.train.Feature(
          intaa_list=tf.train.IntaaList(value=tokenized_data['''attention_mask'''][i]		)		),
  }
  _UpperCamelCase:  Optional[int]    						=			tf.train.Features(feature=lowercase		)
  _UpperCamelCase:  Optional[int]    						=			tf.train.Example(features=lowercase		)
  _UpperCamelCase:  List[Any]    						=			example.SerializeToString()
  records.append(lowercase		)
 return records




def    lowerCAmelCase_  (		lowercase:				List[str]		)     ->					Tuple:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			datasets.load_dataset(args.dataset_name	,       args.dataset_config	,       split=args.split		)

 if args.limit is not None:
  _UpperCamelCase:  str    						=			min(len(lowercase		)	,       args.limit		)
  _UpperCamelCase:  Optional[Any]    						=			dataset.select(range(lowercase		)		)
  print(F"""Limiting the dataset to {args.limit} entries."""		)

 _UpperCamelCase:  List[Any]    						=			AutoTokenizer.from_pretrained(args.tokenizer_name_or_path		)

 # Handle output directory creation.
 # For serializing into a Google Cloud Storage Bucket, one needs to first
 # create a bucket.
 if "gs" not in args.output_dir:
  if not os.path.exists(args.output_dir		):
   os.makedirs(args.output_dir		)
  _UpperCamelCase:  Any    						=			os.path.join(args.output_dir	,       args.split		)
  if not os.path.exists(lowercase		):
   os.makedirs(lowercase		)
 else:
  _UpperCamelCase:  str    						=			os.path.join(args.output_dir	,       args.split		)

 # Tokenize the whole dataset at once.
 _UpperCamelCase:  Union[str, Any]    						=			tokenize_function(lowercase		)
 _UpperCamelCase:  List[Any]    						=			dataset.map(lowercase	,       batched=lowercase	,       num_proc=4	,       remove_columns=['''text''']		)

 # We need to concatenate all our texts together, and then split the result
 # into chunks of a fixed size, which we will call block_size. To do this, we
 # will use the map method again, with the option batched=True. When we use batched=True,
 # the function we pass to map() will be passed multiple inputs at once, allowing us
 # to group them into more or fewer examples than we had in the input.
 # This allows us to create our new fixed-length samples. The advantage of this
 # method is that we don't lose a whole lot of content from the dataset compared to the
 # case where we simply tokenize with a pre-defined max_length.

 def group_texts(lowercase:				int		):
  # Concatenate all texts.
  _UpperCamelCase:  List[str]    						=			{k: sum(examples[k]	,       []		) for k in examples.keys()}
  _UpperCamelCase:  Any    						=			len(concatenated_examples[list(examples.keys()		)[0]]		)
  # We drop the small remainder, though you could add padding instead if the model supports it
  # In this, as in all things, we advise you to follow your heart 🫀
  _UpperCamelCase:  Optional[int]    						=			(total_length // args.max_length) * args.max_length
  # Split by chunks of max_len.
  _UpperCamelCase:  Tuple    						=			{
      k: [t[i : i + args.max_length] for i in range(0	,       lowercase	,       args.max_length		)]
      for k, t in concatenated_examples.items()
  }
  return result

 _UpperCamelCase:  Dict    						=			dataset_tokenized.map(lowercase	,       batched=lowercase	,       batch_size=1_000	,       num_proc=4		)

 _UpperCamelCase:  List[str]    						=			0
 _UpperCamelCase:  Tuple    						=			0
 for shard in range(0	,       len(lowercase		)	,       args.shard_size		):
  _UpperCamelCase:  List[str]    						=			grouped_dataset[shard : shard + args.shard_size]
  _UpperCamelCase:  Optional[Any]    						=			len(dataset_snapshot['''input_ids''']		)
  _UpperCamelCase:  str    						=			os.path.join(lowercase	,       F"""dataset-{shard_count}-{records_containing}.tfrecord"""		)
  _UpperCamelCase:  Any    						=			get_serialized_examples(lowercase		)

  with tf.io.TFRecordWriter(lowercase		) as out_file:
   for i in range(len(lowercase		)		):
    _UpperCamelCase:  List[Any]    						=			serialized_examples[i]
    out_file.write(lowercase		)
   print('''Wrote file {} containing {} records'''.format(lowercase	,       lowercase		)		)

  shard_count += 1
  total_records += records_containing

 with open(F"""split-{args.split}-records-count.txt"""	,       '''w'''		) as f:
  print(F"""Total {args.split} records: {total_records}"""	,       file=lowercase		)


if __name__ == "__main__":
  UpperCAmelCase_           =				parse_args()
  main(args)