'''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

SCREAMING_SNAKE_CASE_:					Union[str, Any]  ='tiny-wmt19-en-ru'

# Build

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

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

    SCREAMING_SNAKE_CASE_:					List[str]  =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,
    )

SCREAMING_SNAKE_CASE_:					int  =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,
)

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

# Test
SCREAMING_SNAKE_CASE_:					int  =tokenizer(['Making tiny model'], return_tensors='pt')
SCREAMING_SNAKE_CASE_:					Optional[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'''





import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout





def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any]      )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_	   =       FileLock(str(tmpdir / "foo.lock"      )      )
     UpperCAmelCase_	   =       FileLock(str(tmpdir / "foo.lock"      )      )
     UpperCAmelCase_	   =       0.01
     with locka.acquire():
          with pytest.raises(snake_case_      ):
               UpperCAmelCase_	   =       time.time()
               locka.acquire(snake_case_      )
               assert time.time() - _start > timeout






def      lowerCAmelCase_    ( snake_case_	:			int      )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_	   =       "a" * 10_00 + ".lock"
     UpperCAmelCase_	   =       FileLock(str(tmpdir / filename      )      )
     assert locka._lock_file.endswith(".lock"      )
     assert not locka._lock_file.endswith(snake_case_      )
     assert len(os.path.basename(locka._lock_file      )      ) <= 2_55
     UpperCAmelCase_	   =       FileLock(tmpdir / filename      )
     with locka.acquire():
          with pytest.raises(snake_case_      ):
               locka.acquire(0      )




