"""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

__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=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,
)

__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"""





import math
def  __A   (_SCREAMING_SNAKE_CASE			)					->int:





  """simple docstring"""


  if not isinstance(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			):
    lowerCAmelCase__			:Dict											=							F"Input value of [number={number}] must be an integer"
    raise TypeError(_SCREAMING_SNAKE_CASE			)

  if number < 1:
    lowerCAmelCase__			:Dict											=							F"Input value of [number={number}] must be > 0"
    raise ValueError(_SCREAMING_SNAKE_CASE			)
  elif number == 1:
    return 3
  elif number == 2:
    return 5
  else:
    lowerCAmelCase__			:Union[str, Any]											=							int(math.log(number // 3     ,   2			)			) + 2

    lowerCAmelCase__			:Optional[Any]											=							[3, 5]
    lowerCAmelCase__			:Optional[Any]											=							2
    lowerCAmelCase__			:List[str]											=							3
    for block in range(1     ,   _SCREAMING_SNAKE_CASE			):
      for _ in range(_SCREAMING_SNAKE_CASE			):
        proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1]			)
        proth_index += 1
      increment *= 2

  return proth_list[number - 1]


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  for number in range(11):
    __A											=		0
    try:
      __A											=		proth(number)
    except ValueError:
      print(F'''ValueError: there is no {number}th Proth number''')
      continue

    print(F'''The {number}th Proth number: {value}''')
