



from pathlib import Path
import json
import tempfile

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

lowercase_						=       'tiny-wmt19-en-ru'

# Build

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

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

       lowercase_						=       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,
       )

lowercase_						=       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,
)

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
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 graphs.minimum_spanning_tree_kruskal import kruskal



def __lowerCAmelCase	(       ):
			lowercase__   					=			9
			lowercase__   					=			[
			    [0, 1, 4],
			    [0, 7, 8],
			    [1, 2, 8],
			    [7, 8, 7],
			    [7, 6, 1],
			    [2, 8, 2],
			    [8, 6, 6],
			    [2, 3, 7],
			    [2, 5, 4],
			    [6, 5, 2],
			    [3, 5, 14],
			    [3, 4, 9],
			    [5, 4, 10],
			    [1, 7, 11],
			]

			lowercase__   					=			kruskal(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

			lowercase__   					=			[
			    [7, 6, 1],
			    [2, 8, 2],
			    [6, 5, 2],
			    [0, 1, 4],
			    [2, 5, 4],
			    [2, 3, 7],
			    [0, 7, 8],
			    [3, 4, 9],
			]

			assert sorted(SCREAMING_SNAKE_CASE_					) == sorted(SCREAMING_SNAKE_CASE_					)




