






import pytest

from datasets import inspect_metric, list_metrics, load_metric







@pytest.fixture
def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   Dict:

						"""simple docstring"""



						monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings'    ,       set()		)







@pytest.fixture
def 	_UpperCamelCase   (    UpperCamelCase_       :					Union[str, Any]		)  ->   Optional[int]:

						"""simple docstring"""



						class     __SCREAMING_SNAKE_CASE					:






												def __init__(	self     ,						_UpperCamelCase   ):






																		"""simple docstring"""
																		lowerCAmelCase__    						=						metric_id




						class     __SCREAMING_SNAKE_CASE					:
												_SCREAMING_SNAKE_CASE	:       Optional[int]   =					[MetricMock(__lowercase) for metric_id in ['''accuracy''', '''mse''', '''precision''', '''codeparrot/apps_metric''']]






												def       UpperCamelCase__      (	self   ):






																		"""simple docstring"""
																		return self._metrics




						monkeypatch.setattr('datasets.inspect.huggingface_hub'    ,       HfhMock()		)







@pytest.mark.parametrize(
    'func, args'    ,       [(load_metric, ('metrics/mse',)), (list_metrics, ()), (inspect_metric, ('metrics/mse', 'tmp_path'))]		)
def 	_UpperCamelCase   (    UpperCamelCase_       :					List[str]    ,       UpperCamelCase_       :					Tuple    ,       UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					Union[str, Any]    ,       UpperCamelCase_       :					Tuple		)  ->   List[Any]:

						"""simple docstring"""



						if "tmp_path" in args:
												lowerCAmelCase__    						=						tuple(arg if arg != 'tmp_path' else tmp_path for arg in args		)
						with pytest.warns(UpperCamelCase_    ,       match='https://huggingface.co/docs/evaluate'		):
												func(*UpperCamelCase_		)













# 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 model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

__snake_case    :    Optional[int]         =     """facebook/wmt19-en-de"""
__snake_case    :    Optional[Any]         =     FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__snake_case    :    List[str]         =     FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        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,
    )
)

__snake_case    :    Optional[Any]         =     FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
__snake_case    :    List[Any]         =     tokenizer(["""Making tiny model"""], return_tensors="""pt""")
__snake_case    :    Any         =     tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
__snake_case    :    Any         =     """tiny-wmt19-en-de"""
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-de





