
"""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 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

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

# Build

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

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

					UpperCAmelCase__      :    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,
					)

UpperCAmelCase__      :    List[Any]    							= 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,
)

UpperCAmelCase__      :    List[Any]    							= FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
UpperCAmelCase__      :    int    							= tokenizer(['Making tiny model'], return_tensors='pt')
UpperCAmelCase__      :    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 bisect import bisect
from itertools import accumulate




def 			__magic_name__				( A		:					Optional[Any],  A		:					List[str],  A		:					Tuple,  A		:					Optional[Any]   ):






			'''simple docstring'''







			a    =			sorted(zip(A,  A   ),  key=lambda A   : x[0] / x[1],  reverse=A   )
			a    ,		a    =			[i[0] for i in r], [i[1] for i in r]
			a    =			list(accumulate(A   )   )
			a    =			bisect(A,  A   )
			return (
			    0
			    if k == 0
			    else sum(vl[:k]   ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
			    if k != n
			    else sum(vl[:k]   )
			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()






