





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		:  Optional[Any]         =	'''tiny-wmt19-en-ru'''

# Build

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

with tempfile.TemporaryDirectory() as tmpdirname:
				UpperCAmelCase		:  Any         =	Path(tmpdirname)
				UpperCAmelCase		:  Optional[int]         =	build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
				UpperCAmelCase		:  Tuple         =	build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
				UpperCAmelCase		:  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))

				UpperCAmelCase		:  Dict         =	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		:  List[Any]         =	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,
)

UpperCAmelCase		:  Optional[int]         =	FSMTForConditionalGeneration(config)
print(F"""num of params {tiny_model.num_parameters()}""")

# Test
UpperCAmelCase		:  Union[str, Any]         =	tokenizer(['''Making tiny model'''], return_tensors='''pt''')
UpperCAmelCase		:  Dict         =	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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase     :						Dict						       =  {'''processing_layoutxlm''': ['''LayoutXLMProcessor''']}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase     :						int						       =  ['''LayoutXLMTokenizer''']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase     :						Any						       =  ['''LayoutXLMTokenizerFast''']

if TYPE_CHECKING:
       from .processing_layoutxlm import LayoutXLMProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm import LayoutXLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
       import sys

       __UpperCamelCase     :						Dict						       =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





