
"""simple docstring"""




from typing import TYPE_CHECKING

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


snake_case    =		{
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case    =		['MarianTokenizer']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case    =		[
	    'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MarianForCausalLM',
	    'MarianModel',
	    'MarianMTModel',
	    'MarianPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case    =		['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case    =		['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
	from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_marian import MarianTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_marian import (
		    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MarianForCausalLM,
		    MarianModel,
		    MarianMTModel,
		    MarianPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
	import sys

	snake_case    =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_A  					= 50_00_00

_A					,  _A  					= os.path.split(__file__)
_A  					= os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))



@get_duration
def 						lowercase_			(   A__		)							->							str:
    """simple docstring"""

    snake_case      =					dataset.select(range(0 ,		len(A__		) ,		2		)		)



@get_duration
def 						lowercase_			(   A__		)							->							int:
    """simple docstring"""

    snake_case      =					dataset.sort("numbers"		)



@get_duration
def 						lowercase_			(   A__		)							->							Optional[int]:
    """simple docstring"""

    snake_case      =					dataset.shuffle()



@get_duration
def 						lowercase_			(   A__		)							->							int:
    """simple docstring"""

    snake_case      =					dataset.train_test_split(0.1		)



@get_duration
def 						lowercase_			(   A__ ,		A__=10		)							->							List[Any]:
    """simple docstring"""

    for shard_id in range(A__		):
        snake_case      =					dataset.shard(A__ ,		A__		)



def 						lowercase_			(   )							->							List[str]:
    """simple docstring"""

    snake_case      =					{"num examples": SPEED_TEST_N_EXAMPLES}
    snake_case      =					(select, sort, shuffle, train_test_split, shard)
    with tempfile.TemporaryDirectory() as tmp_dir:
        print("generating dataset"		)
        snake_case      =					datasets.Features({"text": datasets.Value("string"		), "numbers": datasets.Value("float32"		)}		)
        snake_case      =					generate_example_dataset(
            os.path.join(A__ ,		"dataset.arrow"		) ,		A__ ,		num_examples=A__		)
        print("Functions"		)
        for func in functions:
            print(func.__name__		)
            snake_case      =					func(A__		)

    with open(A__ ,		"wb"		) as f:
        f.write(json.dumps(A__		).encode("utf-8"		)		)


if __name__ == "__main__":  # useful to run the profiler
   benchmark_indices_mapping()

