




from typing import Union

import fire
import torch
from tqdm import tqdm





def __UpperCamelCase			(       _A ,  _A = "cpu" ,  _A = None  ):
						lowerCAmelCase_      			=  torch.load(lowerCAmelCase__ ,  map_location=lowerCAmelCase__  )
						for k, v in tqdm(state_dict.items()  ):
												if not isinstance(lowerCAmelCase__ ,  torch.Tensor  ):
																		raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'''  )
												lowerCAmelCase_      			=  v.half()
						if save_path is None:  # overwrite src_path
												lowerCAmelCase_      			=  src_path
						torch.save(lowerCAmelCase__ ,  lowerCAmelCase__  )


if __name__ == "__main__":
						fire.Fire(convert)









"""simple docstring"""







import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


lowercase__		:				List[Any]          =	logging.getLogger(__name__)

def 				UpperCamelCase_    (    )						->   Dict:

  """simple docstring"""




  lowerCAmelCase_   :				Tuple								= argparse.ArgumentParser(
      description='Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'						)
  parser.add_argument(
      '--dataset_name'    ,   type=lowerCAmelCase__    ,   default='wikitext'    ,   help='Name of the training. Explore datasets at: hf.co/datasets.'    ,   )
  parser.add_argument(
      '--dataset_config'    ,   type=lowerCAmelCase__    ,   default='wikitext-103-raw-v1'    ,   help='Configuration name of the dataset.'						)
  parser.add_argument(
      '--tokenizer_name_or_path'    ,   type=lowerCAmelCase__    ,   default='sayakpaul/unigram-tokenizer-wikitext'    ,   help='Tokenizer identifier. Can be a local filepath or a Hub identifier.'    ,   )
  parser.add_argument(
      '--shard_size'    ,   type=lowerCAmelCase__    ,   default=1000    ,   help='Number of entries to go in a single shard.'    ,   )
  parser.add_argument('--split'    ,   type=lowerCAmelCase__    ,   default='train'    ,   choices=['train', 'test', 'validation']						)
  parser.add_argument(
      '--limit'    ,   default=lowerCAmelCase__    ,   type=lowerCAmelCase__    ,   help='Limit the number of shards (used for debugging).'    ,   )
  parser.add_argument(
      '--max_length'    ,   type=lowerCAmelCase__    ,   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=lowerCAmelCase__    ,   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.'    ,   )

  lowerCAmelCase_   :				List[Any]								= parser.parse_args()
  return args

def 				UpperCamelCase_    (    lowerCAmelCase__      :				int						)						->   Union[str, Any]:

  """simple docstring"""




  def fn(lowerCAmelCase__      :				Optional[Any]						):
    return tokenizer(examples['text']						)

  return fn

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Tuple						)						->   Dict:

  """simple docstring"""




  lowerCAmelCase_   :				int								= []
  for i in range(len(tokenized_data['input_ids']						)						):
    lowerCAmelCase_   :				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]						)						),
    }
    lowerCAmelCase_   :				Union[str, Any]								= tf.train.Features(feature=lowerCAmelCase__						)
    lowerCAmelCase_   :				Dict								= tf.train.Example(features=lowerCAmelCase__						)
    lowerCAmelCase_   :				Optional[int]								= example.SerializeToString()
    records.append(lowerCAmelCase__						)
  return records

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[Any]						)						->   Optional[int]:

  """simple docstring"""




  lowerCAmelCase_   :				str								= datasets.load_dataset(args.dataset_name    ,   args.dataset_config    ,   split=args.split						)

  if args.limit is not None:
    lowerCAmelCase_   :				Tuple								= min(len(lowerCAmelCase__						)    ,   args.limit						)
    lowerCAmelCase_   :				Any								= dataset.select(range(lowerCAmelCase__						)						)
    print(f"Limiting the dataset to {args.limit} entries."						)

  lowerCAmelCase_   :				List[str]								= 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						)
    lowerCAmelCase_   :				int								= os.path.join(args.output_dir    ,   args.split						)
    if not os.path.exists(lowerCAmelCase__						):
      os.makedirs(lowerCAmelCase__						)
  else:
    lowerCAmelCase_   :				Dict								= os.path.join(args.output_dir    ,   args.split						)

  # Tokenize the whole dataset at once.
  lowerCAmelCase_   :				Dict								= tokenize_function(lowerCAmelCase__						)
  lowerCAmelCase_   :				List[Any]								= dataset.map(lowerCAmelCase__    ,   batched=lowerCAmelCase__    ,   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(lowerCAmelCase__      :				List[Any]						):
    # Concatenate all texts.
    lowerCAmelCase_   :				int								= {k: sum(examples[k]    ,   []						) for k in examples.keys()}
    lowerCAmelCase_   :				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 🫀
    lowerCAmelCase_   :				Union[str, Any]								= (total_length // args.max_length) * args.max_length
    # Split by chunks of max_len.
    lowerCAmelCase_   :				Optional[Any]								= {
        k: [t[i : i + args.max_length] for i in range(0    ,   lowerCAmelCase__    ,   args.max_length						)]
        for k, t in concatenated_examples.items()
    }
    return result

  lowerCAmelCase_   :				Optional[int]								= dataset_tokenized.map(lowerCAmelCase__    ,   batched=lowerCAmelCase__    ,   batch_size=1000    ,   num_proc=4						)

  lowerCAmelCase_   :				Optional[int]								= 0
  lowerCAmelCase_   :				Optional[Any]								= 0
  for shard in range(0    ,   len(lowerCAmelCase__						)    ,   args.shard_size						):
    lowerCAmelCase_   :				Dict								= grouped_dataset[shard : shard + args.shard_size]
    lowerCAmelCase_   :				Tuple								= len(dataset_snapshot['input_ids']						)
    lowerCAmelCase_   :				Optional[Any]								= os.path.join(lowerCAmelCase__    ,   f"dataset-{shard_count}-{records_containing}.tfrecord"						)
    lowerCAmelCase_   :				Tuple								= get_serialized_examples(lowerCAmelCase__						)

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

    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=lowerCAmelCase__						)


if __name__ == "__main__":
     lowercase__		:				int          =	parse_args()
     main(args)





