





import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_lowerCamelCase   				=			['small', 'medium', 'large']

_lowerCamelCase   				=			'lm_head.decoder.weight'
_lowerCamelCase   				=			'lm_head.weight'






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							str					,   UpperCamelCase__:							str    ):
		SCREAMING_SNAKE_CASE__			=      torch.load(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      d.pop(UpperCamelCase__    )
		os.makedirs(UpperCamelCase__					,   exist_ok=UpperCamelCase__    )
		torch.save(UpperCamelCase__					,   os.path.join(UpperCamelCase__					,   UpperCamelCase__    )    )


if __name__ == "__main__":
	_lowerCamelCase   				=			argparse.ArgumentParser()
	parser.add_argument('--dialogpt_path', default='.', type=str)
	_lowerCamelCase   				=			parser.parse_args()
	for MODEL in DIALOGPT_MODELS:
		_lowerCamelCase   				=			os.path.join(args.dialogpt_path, F'''{MODEL}_ft.pkl''')
		_lowerCamelCase   				=			F'''./DialoGPT-{MODEL}'''
		convert_dialogpt_checkpoint(
		    checkpoint_path,
		    pytorch_dump_folder_path,
		)






import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


_lowerCamelCase   				=			logging.getLogger(__name__)






def 				SCREAMING_SNAKE_CASE__							(       ):
		SCREAMING_SNAKE_CASE__			=      argparse.ArgumentParser(
		    description="""Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."""    )
		parser.add_argument(
		    """--dataset_name"""					,   type=UpperCamelCase__					,   default="""wikitext"""					,   help="""Name of the training. Explore datasets at: hf.co/datasets."""					,   )
		parser.add_argument(
		    """--dataset_config"""					,   type=UpperCamelCase__					,   default="""wikitext-103-raw-v1"""					,   help="""Configuration name of the dataset."""    )
		parser.add_argument(
		    """--tokenizer_name_or_path"""					,   type=UpperCamelCase__					,   default="""sayakpaul/unigram-tokenizer-wikitext"""					,   help="""Tokenizer identifier. Can be a local filepath or a Hub identifier."""					,   )
		parser.add_argument(
		    """--shard_size"""					,   type=UpperCamelCase__					,   default=1_000					,   help="""Number of entries to go in a single shard."""					,   )
		parser.add_argument("""--split"""					,   type=UpperCamelCase__					,   default="""train"""					,   choices=["""train""", """test""", """validation"""]    )
		parser.add_argument(
		    """--limit"""					,   default=UpperCamelCase__					,   type=UpperCamelCase__					,   help="""Limit the number of shards (used for debugging)."""					,   )
		parser.add_argument(
		    """--max_length"""					,   type=UpperCamelCase__					,   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=UpperCamelCase__					,   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."""					,   )

		SCREAMING_SNAKE_CASE__			=      parser.parse_args()
		return args






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[Any]    ):
		def fn(UpperCamelCase__:							Any    ):
				return tokenizer(examples["""text"""]    )

		return fn






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Any    ):
		SCREAMING_SNAKE_CASE__			=      []
		for i in range(len(tokenized_data["""input_ids"""]    )    ):
				SCREAMING_SNAKE_CASE__			=      {
				    """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]    )    ),
				}
				SCREAMING_SNAKE_CASE__			=      tf.train.Features(feature=UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      tf.train.Example(features=UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      example.SerializeToString()
				records.append(UpperCamelCase__    )
		return records






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]    ):
		SCREAMING_SNAKE_CASE__			=      datasets.load_dataset(args.dataset_name					,   args.dataset_config					,   split=args.split    )

		if args.limit is not None:
				SCREAMING_SNAKE_CASE__			=      min(len(UpperCamelCase__    )					,   args.limit    )
				SCREAMING_SNAKE_CASE__			=      dataset.select(range(UpperCamelCase__    )    )
				print(f'''Limiting the dataset to {args.limit} entries.'''    )

		SCREAMING_SNAKE_CASE__			=      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    )
				SCREAMING_SNAKE_CASE__			=      os.path.join(args.output_dir					,   args.split    )
				if not os.path.exists(UpperCamelCase__    ):
						os.makedirs(UpperCamelCase__    )
		else:
				SCREAMING_SNAKE_CASE__			=      os.path.join(args.output_dir					,   args.split    )

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

		SCREAMING_SNAKE_CASE__			=      dataset_tokenized.map(UpperCamelCase__					,   batched=UpperCamelCase__					,   batch_size=1_000					,   num_proc=4    )

		SCREAMING_SNAKE_CASE__			=      0
		SCREAMING_SNAKE_CASE__			=      0
		for shard in range(0					,   len(UpperCamelCase__    )					,   args.shard_size    ):
				SCREAMING_SNAKE_CASE__			=      grouped_dataset[shard : shard + args.shard_size]
				SCREAMING_SNAKE_CASE__			=      len(dataset_snapshot["""input_ids"""]    )
				SCREAMING_SNAKE_CASE__			=      os.path.join(UpperCamelCase__					,   f'''dataset-{shard_count}-{records_containing}.tfrecord'''    )
				SCREAMING_SNAKE_CASE__			=      get_serialized_examples(UpperCamelCase__    )

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

				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=UpperCamelCase__    )


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