



'''simple docstring'''

def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				str:
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'  )

		_a					:				Tuple				=							str(bin(lowerCAmelCase_  )  )
		binary_number += "0" * shift_amount
		return binary_number



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				str:
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'  )

		_a					:				int				=							str(bin(lowerCAmelCase_  )  )[2:]
		if shift_amount >= len(lowerCAmelCase_  ):
				return "0b0"
		_a					:				Union[str, Any]				=							binary_number[: len(lowerCAmelCase_  ) - shift_amount]
		return "0b" + shifted_binary_number



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				str:
		if number >= 0:  # Get binary representation of positive number
				_a					:				Optional[Any]				=							'0' + str(bin(lowerCAmelCase_  )  ).strip('-'  )[2:]
		else:  # Get binary (2's complement) representation of negative number
				_a					:				List[Any]				=							len(bin(lowerCAmelCase_  )[3:]  )  # Find 2's complement of number
				_a					:				Optional[int]				=							bin(abs(lowerCAmelCase_  ) - (1 << binary_number_length)  )[3:]
				_a					:				int				=							(
				    '1' + '0' * (binary_number_length - len(lowerCAmelCase_  )) + binary_number
				)

		if shift_amount >= len(lowerCAmelCase_  ):
				return "0b" + binary_number[0] * len(lowerCAmelCase_  )
		return (
		    "0b"
		    + binary_number[0] * shift_amount
		    + binary_number[: len(lowerCAmelCase_  ) - shift_amount]
		)


if __name__ == "__main__":
							import doctest

							doctest.testmod()









'''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

__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=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,
)

__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




