from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_:					Tuple  ={'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					Tuple  =['FNetTokenizer']

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					Union[str, Any]  =['FNetTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					int  =[
        'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
        'FNetForMaskedLM',
        'FNetForMultipleChoice',
        'FNetForNextSentencePrediction',
        'FNetForPreTraining',
        'FNetForQuestionAnswering',
        'FNetForSequenceClassification',
        'FNetForTokenClassification',
        'FNetLayer',
        'FNetModel',
        'FNetPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_fnet import FNetTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_fnet_fast import FNetTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_fnet import (
            FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            FNetForMaskedLM,
            FNetForMultipleChoice,
            FNetForNextSentencePrediction,
            FNetForPreTraining,
            FNetForQuestionAnswering,
            FNetForSequenceClassification,
            FNetForTokenClassification,
            FNetLayer,
            FNetModel,
            FNetPreTrainedModel,
        )


else:
    import sys

    SCREAMING_SNAKE_CASE_:					Optional[int]  =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





'''simple docstring'''





from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters





def      lowerCAmelCase_    ( )   ->				str:


     '''simple docstring'''




     UpperCAmelCase_	   =       {
         "repo_name": ["test_repo1", "test_repo2", "test_repo3"],
         "path": ["test_1.py", "test_2.py", "unit_test.py"],
         "content": ["a " * 20, "a " * 30, "b " * 7],
     }
     UpperCAmelCase_	   =       Dataset.from_dict(snake_case_      )
     return dataset






class 	__A				(			UpperCamelCase__     ):



    def  _lowercase						(self   :   str			):
         UpperCAmelCase_	   =       get_dataset()
         UpperCAmelCase_	   =       make_duplicate_clusters(__a       ,      0.85			)
         self.assertEqual(len(duplicate_clusters[0]			)       ,      2			)






    def  _lowercase						(self   :   Optional[Any]			):
         UpperCAmelCase_	   =       get_dataset()
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       deduplicate_dataset(__a			)
         self.assertEqual(len(__a			)       ,      2			)
         print(__a			)
         self.assertEqual(duplicate_clusters[0][0]["copies"]       ,      2			)
         self.assertEqual(duplicate_clusters[0][0]["is_extreme"]       ,      __a			)




