




# 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

__lowerCAmelCase		:				Optional[Any]       			=		'tiny-wmt19-en-ru'

# Build

# borrowed from a test
__lowerCAmelCase		:				Union[str, Any]       			=		[
    '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>',
]
__lowerCAmelCase		:				Dict       			=		dict(zip(vocab, range(len(vocab))))
__lowerCAmelCase		:				Optional[Any]       			=		['l o 123', 'lo w 1456', 'e r</w> 1789', '']

with tempfile.TemporaryDirectory() as tmpdirname:
		__lowerCAmelCase		:				int       			=		Path(tmpdirname)
		__lowerCAmelCase		:				Dict       			=		build_dir / VOCAB_FILES_NAMES['src_vocab_file']
		__lowerCAmelCase		:				Dict       			=		build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
		__lowerCAmelCase		:				Tuple       			=		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))

		__lowerCAmelCase		:				Optional[int]       			=		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,
		)

__lowerCAmelCase		:				List[Any]       			=		FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    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,
)

__lowerCAmelCase		:				List[Any]       			=		FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
__lowerCAmelCase		:				int       			=		tokenizer(['Making tiny model'], return_tensors='pt')
__lowerCAmelCase		:				List[Any]       			=		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












from __future__ import annotations




def 			__magic_name__				( A		:					list   ):






			'''simple docstring'''







			if len(A   ) == 0:
						return []
			a    ,		a    =			min(A   ), max(A   )
			a    =			int(max_value - min_value   ) + 1
			a    =			[[] for _ in range(A   )]

			for i in my_list:
						buckets[int(i - min_value   )].append(A   )

			return [v for bucket in buckets for v in sorted(A   )]


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
		assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]






