






import pytest

import datasets


# Import fixture modules as plugins
lowerCamelCase      :       Dict    =						['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     # Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
     for item in items:
          if any(marker in item.keywords for marker in ["integration", "unit"]							):
               continue
          item.add_marker(pytest.mark.unit							)





def    __lowerCAmelCase   (    __snake_case							):
     config.addinivalue_line("markers"       ,			"torchaudio_latest: mark test to run with torchaudio>=0.12"							)





@pytest.fixture(autouse=__snake_case							)
def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     # test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
     __lowerCAmelCase             =       tmp_path_factory.getbasetemp() / "cache"
     __lowerCAmelCase             =       test_hf_cache_home / "datasets"
     __lowerCAmelCase             =       test_hf_cache_home / "metrics"
     __lowerCAmelCase             =       test_hf_cache_home / "modules"
     monkeypatch.setattr("datasets.config.HF_DATASETS_CACHE"       ,			str(__snake_case							)							)
     monkeypatch.setattr("datasets.config.HF_METRICS_CACHE"       ,			str(__snake_case							)							)
     monkeypatch.setattr("datasets.config.HF_MODULES_CACHE"       ,			str(__snake_case							)							)
     __lowerCAmelCase             =       test_hf_datasets_cache / "downloads"
     monkeypatch.setattr("datasets.config.DOWNLOADED_DATASETS_PATH"       ,			str(__snake_case							)							)
     __lowerCAmelCase             =       test_hf_datasets_cache / "downloads" / "extracted"
     monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_PATH"       ,			str(__snake_case							)							)





@pytest.fixture(autouse=__snake_case       ,			scope="session"							)
def    __lowerCAmelCase   (    ):
     datasets.disable_progress_bar()





@pytest.fixture(autouse=__snake_case							)
def    __lowerCAmelCase   (    __snake_case							):
     # don't take tests into account when counting downloads
     monkeypatch.setattr("datasets.config.HF_UPDATE_DOWNLOAD_COUNTS"       ,			__snake_case							)





@pytest.fixture
def    __lowerCAmelCase   (    __snake_case							):
     # Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
     # To be removed once SQLAlchemy 2.0 supported
     monkeypatch.setattr("sqlalchemy.util.deprecations.SILENCE_UBER_WARNING"       ,			__snake_case							)














# 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      :       List[str]    =						'''tiny-wmt19-en-ru'''

# Build

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

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

 lowerCamelCase      :       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,
 )

lowerCamelCase      :       int    =						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,
)

lowerCamelCase      :       Any    =						FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

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






