




'''simple docstring'''




from typing import TYPE_CHECKING

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


__lowerCamelCase   :					Optional[Any]   							=					{
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase   :					Optional[int]   							=					['''RoFormerTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase   :					Tuple   							=					[
		    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''RoFormerForCausalLM''',
		    '''RoFormerForMaskedLM''',
		    '''RoFormerForMultipleChoice''',
		    '''RoFormerForQuestionAnswering''',
		    '''RoFormerForSequenceClassification''',
		    '''RoFormerForTokenClassification''',
		    '''RoFormerLayer''',
		    '''RoFormerModel''',
		    '''RoFormerPreTrainedModel''',
		    '''load_tf_weights_in_roformer''',
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase   :					Union[str, Any]   							=					[
		    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFRoFormerForCausalLM''',
		    '''TFRoFormerForMaskedLM''',
		    '''TFRoFormerForMultipleChoice''',
		    '''TFRoFormerForQuestionAnswering''',
		    '''TFRoFormerForSequenceClassification''',
		    '''TFRoFormerForTokenClassification''',
		    '''TFRoFormerLayer''',
		    '''TFRoFormerModel''',
		    '''TFRoFormerPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase   :					Optional[Any]   							=					[
		    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''FlaxRoFormerForMaskedLM''',
		    '''FlaxRoFormerForMultipleChoice''',
		    '''FlaxRoFormerForQuestionAnswering''',
		    '''FlaxRoFormerForSequenceClassification''',
		    '''FlaxRoFormerForTokenClassification''',
		    '''FlaxRoFormerModel''',
		    '''FlaxRoFormerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
		from .tokenization_roformer import RoFormerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roformer_fast import RoFormerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roformer import (
				    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoFormerForCausalLM,
				    RoFormerForMaskedLM,
				    RoFormerForMultipleChoice,
				    RoFormerForQuestionAnswering,
				    RoFormerForSequenceClassification,
				    RoFormerForTokenClassification,
				    RoFormerLayer,
				    RoFormerModel,
				    RoFormerPreTrainedModel,
				    load_tf_weights_in_roformer,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roformer import (
				    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRoFormerForCausalLM,
				    TFRoFormerForMaskedLM,
				    TFRoFormerForMultipleChoice,
				    TFRoFormerForQuestionAnswering,
				    TFRoFormerForSequenceClassification,
				    TFRoFormerForTokenClassification,
				    TFRoFormerLayer,
				    TFRoFormerModel,
				    TFRoFormerPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roformer import (
				    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlaxRoFormerForMaskedLM,
				    FlaxRoFormerForMultipleChoice,
				    FlaxRoFormerForQuestionAnswering,
				    FlaxRoFormerForSequenceClassification,
				    FlaxRoFormerForTokenClassification,
				    FlaxRoFormerModel,
				    FlaxRoFormerPreTrainedModel,
				)


else:
		import sys

		__lowerCamelCase   :					Dict   							=					_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 	__UpperCAmelCase       ( )->    int:



			"""simple docstring"""



			snake_case_				:					Any   =       {
			    "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],
			}
			snake_case_				:					int   =       Dataset.from_dict(__magic_name__						)
			return dataset







class    A_	(a_							):
							"""simple docstring"""




							def 							_A	(		self			:List[str]				)				->  str:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       get_dataset()
										snake_case_				:					Optional[int]   =       make_duplicate_clusters(lowerCAmelCase__       ,							0.8_5				)
										self.assertEqual(len(duplicate_clusters[0]				)       ,							2				)






							def 							_A	(		self			:Union[str, Any]				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       get_dataset()
										snake_case_,	snake_case_				:					List[Any]   =       deduplicate_dataset(lowerCAmelCase__				)
										self.assertEqual(len(lowerCAmelCase__				)       ,							2				)
										print(lowerCAmelCase__				)
										self.assertEqual(duplicate_clusters[0][0]["copies"]       ,							2				)
										self.assertEqual(duplicate_clusters[0][0]["is_extreme"]       ,							lowerCAmelCase__				)




