






'''simple docstring'''







def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       Optional[int]:
							stooge(UpperCamelCase				,			0				,			len(UpperCamelCase      ) - 1      )
							return arr





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       Any:
							if i >= h:
														return

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

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

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

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

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


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









'''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	:			Dict         ='''tiny-wmt19-en-ru'''

# Build

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

with tempfile.TemporaryDirectory() as tmpdirname:
			_A	:			Union[str, Any]         =Path(tmpdirname)
			_A	:			str         =build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
			_A	:			int         =build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
			_A	:			List[Any]         =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	:			int         =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	:			List[str]         =FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    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	:			Union[str, Any]         =FSMTForConditionalGeneration(config)
print(F'num of params {tiny_model.num_parameters()}')

# Test
_A	:			List[str]         =tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_A	:			Tuple         =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

