
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

__A							='''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
__A							=[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
__A							=dict(zip(vocab, range(len(vocab))))
__A							=['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
			__A							=Path(tmpdirname)
			__A							=build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
			__A							=build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
			__A							=build_dir / VOCAB_FILES_NAMES['''merges_file''']
			with open(src_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(tgt_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(merges_file, '''w''') as fp:
						fp.write('''\n'''.join(merges))

			__A							=FSMTTokenizer(
			    langs=['''en''', '''ru'''],
			    src_vocab_size=len(vocab),
			    tgt_vocab_size=len(vocab),
			    src_vocab_file=src_vocab_file,
			    tgt_vocab_file=tgt_vocab_file,
			    merges_file=merges_file,
			)

__A							=FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

__A							=FSMTForConditionalGeneration(config)
print(F"""num of params {tiny_model.num_parameters()}""")

# Test
__A							=tokenizer(['''Making tiny model'''], return_tensors='''pt''')
__A							=tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-ru




import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


__A							=logging.getLogger(__name__)





def      lowerCamelCase_					(							):
				lowerCamelCase_         =     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=1_0_0_0      ,					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=5_1_2      ,					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_         =     parser.parse_args()
				return args





def      lowerCamelCase_					(							lowerCamelCase__       ):
				def fn(lowerCamelCase__       ):
								return tokenizer(examples["text"]       )

				return fn





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     []
				for i in range(len(tokenized_data["input_ids"]       )       ):
								lowerCamelCase_         =     {
								    "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_         =     tf.train.Features(feature=lowerCamelCase__       )
								lowerCamelCase_         =     tf.train.Example(features=lowerCamelCase__       )
								lowerCamelCase_         =     example.SerializeToString()
								records.append(lowerCamelCase__       )
				return records





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     datasets.load_dataset(args.dataset_name      ,					args.dataset_config      ,					split=args.split       )

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

				lowerCamelCase_         =     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_         =     os.path.join(args.output_dir      ,					args.split       )
								if not os.path.exists(lowerCamelCase__       ):
												os.makedirs(lowerCamelCase__       )
				else:
								lowerCamelCase_         =     os.path.join(args.output_dir      ,					args.split       )

				# Tokenize the whole dataset at once.
				lowerCamelCase_         =     tokenize_function(lowerCamelCase__       )
				lowerCamelCase_         =     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__       ):
								# Concatenate all texts.
								lowerCamelCase_         =     {k: sum(examples[k]      ,					[]       ) for k in examples.keys()}
								lowerCamelCase_         =     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_         =     (total_length // args.max_length) * args.max_length
								# Split by chunks of max_len.
								lowerCamelCase_         =     {
								    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_         =     dataset_tokenized.map(lowerCamelCase__      ,					batched=lowerCamelCase__      ,					batch_size=1_0_0_0      ,					num_proc=4       )

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

								with tf.io.TFRecordWriter(lowerCamelCase__       ) as out_file:
												for i in range(len(lowerCamelCase__       )       ):
																lowerCamelCase_         =     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__":
			__A							=parse_args()
			main(args)


