




"""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

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"""


def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]    ):
  """simple docstring"""

  stooge(snake_case__		,   0		,   len(snake_case__    ) - 1    )
  return arr


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					Any		,   snake_case__							:					int    ):
  """simple docstring"""

  if i >= h:
    return

  # If first element is smaller than the last then swap them
  if arr[i] > arr[h]:
    _snake_case		,					_snake_case							:		Tuple								=  arr[h], arr[i]

  # If there are more than 2 elements in the array
  if h - i + 1 > 2:
    _snake_case							:		Dict								=  (int)((h - i + 1) / 3    )

    # Recursively sort first 2/3 elements
    stooge(snake_case__		,   snake_case__		,   (h - t)    )

    # Recursively sort last 2/3 elements
    stooge(snake_case__		,   i + t		,   (snake_case__)    )

    # Recursively sort first 2/3 elements
    stooge(snake_case__		,   snake_case__		,   (h - t)    )


if __name__ == "__main__":
     A_						=				input('''Enter numbers separated by a comma:\n''').strip()
     A_						=				[int(item) for item in user_input.split(''',''')]
     print(stooge_sort(unsorted))


