

'''simple docstring'''






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__      		=			"tiny-wmt19-en-ru"

# Build

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

with tempfile.TemporaryDirectory() as tmpdirname:
 lowerCAmelCase__      		=			Path(tmpdirname)
 lowerCAmelCase__      		=			build_dir / VOCAB_FILES_NAMES["src_vocab_file"]
 lowerCAmelCase__      		=			build_dir / VOCAB_FILES_NAMES["tgt_vocab_file"]
 lowerCAmelCase__      		=			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__      		=			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__      		=			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__      		=			FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
lowerCAmelCase__      		=			tokenizer(['''Making tiny model'''], return_tensors='''pt''')
lowerCAmelCase__      		=			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'''






def 	_A  (					A__ = 1000000   ):






 """simple docstring"""



 __lowercase													=				set(range(3  ,       A__  ,       2   )   )
 primes.add(2   )
 for p in range(3  ,       A__  ,       2   ):
  if p not in primes:
   continue
  primes.difference_update(set(range(p * p  ,       A__  ,       A__   )   )   )

 __lowercase													=				[float(A__   ) for n in range(limit + 1   )]

 for p in primes:
  for n in range(A__  ,       limit + 1  ,       A__   ):
   phi[n] *= 1 - 1 / p

 return int(sum(phi[2:]   )   )


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

