
from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
					import tensorflow as tf


_UpperCAmelCase					: List[Any]      =       logging.get_logger(__name__)


@dataclass
class 				lowercase					(		lowercase__   ):
		__lowercase       :       List[Any]	     =				[
		    """no_inference""",
		    """no_cuda""",
		    """no_tpu""",
		    """no_speed""",
		    """no_memory""",
		    """no_env_print""",
		    """no_multi_process""",
		]



		def __init__( self				,      **A_       )  ->					Dict:
				"""simple docstring"""
				for deprecated_arg in self.deprecated_args:
						if deprecated_arg in kwargs:
								UpperCamelCase		=     deprecated_arg[3:]
								UpperCamelCase		=     not kwargs.pop(lowercase_       )
								logger.warning(
								    F'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
								    F''' {positive_arg}={kwargs[positive_arg]}'''       )
				UpperCamelCase		=     kwargs.pop('tpu_name'				,      self.tpu_name       )
				UpperCamelCase		=     kwargs.pop('device_idx'				,      self.device_idx       )
				UpperCamelCase		=     kwargs.pop('eager_mode'				,      self.eager_mode       )
				UpperCamelCase		=     kwargs.pop('use_xla'				,      self.use_xla       )
				super().__init__(**lowercase_       )

		__lowercase       :       str	     =				field(
		    default=lowercase__  ,   metadata={"help": "Name of TPU"}  ,   )
		__lowercase       :       int	     =				field(
		    default=0  ,   metadata={"help": "CPU / GPU device index. Defaults to 0."}  ,   )
		__lowercase       :       bool	     =				field(default=lowercase__  ,   metadata={"help": "Benchmark models in eager model."}   )
		__lowercase       :       bool	     =				field(
		    default=lowercase__  ,   metadata={
		        "help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
		    }  ,   )



		@cached_property
		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				requires_backends(self				,      ['tf']       )
				UpperCamelCase		=     None
				if self.tpu:
						try:
								if self.tpu_name:
										UpperCamelCase		=     tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name       )
								else:
										UpperCamelCase		=     tf.distribute.cluster_resolver.TPUClusterResolver()
						except ValueError:
								UpperCamelCase		=     None
				return tpu



		@cached_property
		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				requires_backends(self				,      ['tf']       )
				if self.is_tpu:
						tf.config.experimental_connect_to_cluster(self._setup_tpu       )
						tf.tpu.experimental.initialize_tpu_system(self._setup_tpu       )

						UpperCamelCase		=     tf.distribute.TPUStrategy(self._setup_tpu       )
				else:
						# currently no multi gpu is allowed
						if self.is_gpu:
								# TODO: Currently only single GPU is supported
								tf.config.set_visible_devices(self.gpu_list[self.device_idx]				,      'GPU'       )
								UpperCamelCase		=     tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}'''       )
						else:
								tf.config.set_visible_devices([]				,      'GPU'       )  # disable GPU
								UpperCamelCase		=     tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}'''       )

				return strategy



		@property
		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				requires_backends(self				,      ['tf']       )
				return self._setup_tpu is not None



		@property
		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				requires_backends(self				,      ['tf']       )
				return self._setup_strategy



		@property
		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				requires_backends(self				,      ['tf']       )
				return tf.config.list_physical_devices('GPU'       )



		@property
		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				requires_backends(self				,      ['tf']       )
				if self.cuda:
						return len(self.gpu_list       )
				return 0



		@property
		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				return self.n_gpu > 0


"""simple docstring"""





from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters







def 	__a     (		):
   UpperCAmelCase_					:					List[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],
   }
   UpperCAmelCase_					:					Optional[int]			     =    Dataset.from_dict(__lowerCamelCase  )
   return dataset


class A_	(lowercase__      ):




  '''simple docstring'''







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    get_dataset()
     UpperCAmelCase_					:					Any			     =    make_duplicate_clusters(lowercase_				,   0.85  )
     self.assertEqual(len(duplicate_clusters[0]  )				,   2  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    get_dataset()
     UpperCAmelCase_     ,   UpperCAmelCase_					:					List[str]			     =    deduplicate_dataset(lowercase_  )
     self.assertEqual(len(lowercase_  )				,   2  )
     print(lowercase_  )
     self.assertEqual(duplicate_clusters[0][0]["copies"]				,   2  )
     self.assertEqual(duplicate_clusters[0][0]["is_extreme"]				,   lowercase_  )


