



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=10_00,
    tgt_vocab_size=10_00,
    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


'''simple docstring'''







from collections import defaultdict
from math import gcd







def 				SCREAMING_SNAKE_CASE__	(   __A = 1_500_000	)       ->	int:
	_snake_case   						=		defaultdict(__A	)
	_snake_case   						=		2
	while 2 * euclid_m * (euclid_m + 1) <= limit:
		for euclid_n in range((euclid_m % 2) + 1			,				__A			,				2	):
			if gcd(__A			,				__A	) > 1:
				continue
			_snake_case   						=		2 * euclid_m * (euclid_m + euclid_n)
			for perimeter in range(__A			,				limit + 1			,				__A	):
				frequencies[perimeter] += 1
		euclid_m += 1
	return sum(1 for frequency in frequencies.values() if frequency == 1	)


if __name__ == "__main__":
					print(F'''{solution() = }''')

