



"""simple docstring"""

# 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 model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

lowercase__       =	"facebook/wmt19-en-de"
lowercase__       =	FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
lowercase__       =	FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        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,
    )
)

lowercase__       =	FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

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

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

# Save
lowercase__       =	"tiny-wmt19-en-de"
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-de








import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_lowerCamelCase 					=      logging.getLogger(__name__)

if __name__ == "__main__":
	_lowerCamelCase 					=      argparse.ArgumentParser(
	    description='Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'
	)
	parser.add_argument(
	    '--data_file', type=str, default='data/dump.bert-base-uncased.pickle', help='The binarized dataset.'
	)
	parser.add_argument(
	    '--token_counts_dump', type=str, default='data/token_counts.bert-base-uncased.pickle', help='The dump file.'
	)
	parser.add_argument('--vocab_size', default=30522, type=int)
	_lowerCamelCase 					=      parser.parse_args()

	logger.info(F'''Loading data from {args.data_file}''')
	with open(args.data_file, 'rb') as fp:
		_lowerCamelCase 					=      pickle.load(fp)

	logger.info('Counting occurrences for MLM.')
	_lowerCamelCase 					=      Counter()
	for tk_ids in data:
		counter.update(tk_ids)
	_lowerCamelCase 					=      [0] * args.vocab_size
	for k, v in counter.items():
		_lowerCamelCase 					=      v

	logger.info(F'''Dump to {args.token_counts_dump}''')
	with open(args.token_counts_dump, 'wb') as handle:
		pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)