
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_UpperCAmelCase					: Optional[Any]      =       {
    "configuration_transfo_xl": ["TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP", "TransfoXLConfig"],
    "tokenization_transfo_xl": ["TransfoXLCorpus", "TransfoXLTokenizer"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase					: Optional[Any]      =       [
					    "TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "AdaptiveEmbedding",
					    "TransfoXLForSequenceClassification",
					    "TransfoXLLMHeadModel",
					    "TransfoXLModel",
					    "TransfoXLPreTrainedModel",
					    "load_tf_weights_in_transfo_xl",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase					: Optional[Any]      =       [
					    "TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFAdaptiveEmbedding",
					    "TFTransfoXLForSequenceClassification",
					    "TFTransfoXLLMHeadModel",
					    "TFTransfoXLMainLayer",
					    "TFTransfoXLModel",
					    "TFTransfoXLPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
					from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_transfo_xl import (
										    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    AdaptiveEmbedding,
										    TransfoXLForSequenceClassification,
										    TransfoXLLMHeadModel,
										    TransfoXLModel,
										    TransfoXLPreTrainedModel,
										    load_tf_weights_in_transfo_xl,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_transfo_xl import (
										    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFAdaptiveEmbedding,
										    TFTransfoXLForSequenceClassification,
										    TFTransfoXLLMHeadModel,
										    TFTransfoXLMainLayer,
										    TFTransfoXLModel,
										    TFTransfoXLPreTrainedModel,
										)

else:
					import sys

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


from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters


def       A       (					)   ->  Union[str, Any]:
		'''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(lowercase )
		return dataset


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     get_dataset()
				UpperCamelCase		=     make_duplicate_clusters(A_				,      0.85       )
				self.assertEqual(len(duplicate_clusters[0]       )				,      2       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				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_       )
