

class 							A_ :


     def __init__(							self			:		Dict			,  UpperCAmelCase			:		int			,  UpperCAmelCase			:		Dict			,  UpperCAmelCase			:		List[Any]					)							->	Optional[int]:
      __lowerCAmelCase:   Dict   	=      name
      __lowerCAmelCase:   Union[str, Any]   	=      value
      __lowerCAmelCase:   str   	=      weight


     def __repr__(							self			:		Dict					)							->	Any:
      return F'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''


     def   UpperCAmelCase  (							self			:		int					)							->	int:
      return self.value


     def   UpperCAmelCase  (							self			:		Tuple					)							->	Optional[Any]:
      return self.name


     def   UpperCAmelCase  (							self			:		str					)							->	Optional[Any]:
      return self.weight


     def   UpperCAmelCase  (							self			:		Any					)							->	List[str]:
      return self.value / self.weight







def _a (       SCREAMING_SNAKE_CASE		:			List[Any]    ,					SCREAMING_SNAKE_CASE		:			str    ,					SCREAMING_SNAKE_CASE		:			Optional[Any]		)    ->    Tuple:

 """simple docstring"""






 __lowerCAmelCase:   List[str]   	=      []
 for i in range(len(SCREAMING_SNAKE_CASE		)		):
  menu.append(Things(name[i]    ,					value[i]    ,					weight[i]		)		)
 return menu







def _a (       SCREAMING_SNAKE_CASE		:			Dict    ,					SCREAMING_SNAKE_CASE		:			int    ,					SCREAMING_SNAKE_CASE		:			Union[str, Any]		)    ->    Union[str, Any]:

 """simple docstring"""






 __lowerCAmelCase:   List[Any]   	=      sorted(SCREAMING_SNAKE_CASE    ,					key=SCREAMING_SNAKE_CASE    ,					reverse=SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:   Optional[int]   	=      []
 __lowerCAmelCase:   Dict   	=      0.0, 0.0
 for i in range(len(SCREAMING_SNAKE_CASE		)		):
  if (total_cost + items_copy[i].get_weight()) <= max_cost:
   result.append(items_copy[i]		)
   total_cost += items_copy[i].get_weight()
   total_value += items_copy[i].get_value()
 return (result, total_value)







def _a (       )    ->    Tuple:

 """simple docstring"""






 pass


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

_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

