





"""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

__UpperCamelCase     :						List[Any]						       =  '''tiny-wmt19-en-ru'''

# Build

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

with tempfile.TemporaryDirectory() as tmpdirname:
       __UpperCamelCase     :						int						       =  Path(tmpdirname)
       __UpperCamelCase     :						str						       =  build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
       __UpperCamelCase     :						str						       =  build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
       __UpperCamelCase     :						Union[str, 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))

       __UpperCamelCase     :						Any						       =  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,
       )

__UpperCamelCase     :						str						       =  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,
)

__UpperCamelCase     :						int						       =  FSMTForConditionalGeneration(config)
print(f'''num of params {tiny_model.num_parameters()}''')

# Test
__UpperCamelCase     :						Any						       =  tokenizer(['''Making tiny model'''], return_tensors='''pt''')
__UpperCamelCase     :						List[Any]						       =  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"""






from ..utils import DummyObject, requires_backends



class     UpperCAmelCase    (	metaclass=__SCREAMING_SNAKE_CASE  ):
   A__		:	Optional[int]           =  ['''torch''', '''scipy''']



   def __init__(       self    :	Any							,       *__lowerCamelCase    :	List[Any]							,       **__lowerCamelCase    :	Any				):



     """simple docstring"""
     requires_backends(self							,       ['''torch''', '''scipy''']				)



   @classmethod
   def    __UpperCAmelCase    (       cls    :	Dict							,       *__lowerCamelCase    :	List[str]							,       **__lowerCamelCase    :	Tuple				):



     """simple docstring"""
     requires_backends(cls							,       ['''torch''', '''scipy''']				)





   @classmethod
   def    __UpperCAmelCase    (       cls    :	int							,       *__lowerCamelCase    :	Union[str, Any]							,       **__lowerCamelCase    :	Tuple				):



     """simple docstring"""
     requires_backends(cls							,       ['''torch''', '''scipy''']				)





