





from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
      import torch

if is_torch_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm


lowerCAmelCase_						   =    logging.get_logger(__name__)



@dataclass
class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       Dict      					=[
      """no_inference""",
      """no_cuda""",
      """no_tpu""",
      """no_speed""",
      """no_memory""",
      """no_env_print""",
      """no_multi_process""",
  ]







  def __init__(      self      :					Union[str, Any]      ,				**UpperCamelCase      :					Optional[int]							):


    '''simple docstring'''





    for deprecated_arg in self.deprecated_args:
      if deprecated_arg in kwargs:
        _snake_case   :				Optional[Any]														=	deprecated_arg[3:]
        setattr(self      ,				UpperCamelCase      ,				not kwargs.pop(UpperCamelCase							)							)
        logger.warning(
            f"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
            f""" {positive_arg}={kwargs[positive_arg]}"""							)

    _snake_case   :				Union[str, Any]														=	kwargs.pop('torchscript'      ,				self.torchscript							)
    _snake_case   :				Dict														=	kwargs.pop('torch_xla_tpu_print_metrics'      ,				self.torch_xla_tpu_print_metrics							)
    _snake_case   :				Any														=	kwargs.pop('fp16_opt_level'      ,				self.fpaa_opt_level							)
    super().__init__(**UpperCamelCase							)

  a_							:       bool      					=field(default=UpperCAmelCase_							,    metadata={"""help""": """Trace the models using torchscript"""}							)
  a_							:       bool      					=field(default=UpperCAmelCase_							,    metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}							)
  a_							:       str      					=field(
      default="""O1"""							,    metadata={
          """help""": (
              """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. """
              """See details at https://nvidia.github.io/apex/amp.html"""
          )
      }							,    )







  @cached_property
  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    requires_backends(self      ,				['torch']							)
    logger.info('PyTorch: setting up devices'							)
    if not self.cuda:
      _snake_case   :				List[str]														=	torch.device('cpu'							)
      _snake_case   :				Dict														=	0
    elif is_torch_tpu_available():
      _snake_case   :				str														=	xm.xla_device()
      _snake_case   :				Optional[Any]														=	0
    else:
      _snake_case   :				Optional[Any]														=	torch.device('cuda' if torch.cuda.is_available() else 'cpu'							)
      _snake_case   :				Any														=	torch.cuda.device_count()
    return device, n_gpu







  @property
  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    return is_torch_tpu_available() and self.tpu







  @property
  def UpperCamelCase_ (      self      :					str							):


    '''simple docstring'''





    requires_backends(self      ,				['torch']							)
    # TODO(PVP): currently only single GPU is supported
    return torch.cuda.current_device()







  @property
  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    requires_backends(self      ,				['torch']							)
    return self._setup_devices[0]







  @property
  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





    requires_backends(self      ,				['torch']							)
    return self._setup_devices[1]







  @property
  def UpperCamelCase_ (      self      :					List[str]							):


    '''simple docstring'''





    return self.n_gpu > 0











from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters


def     lowerCamelCase_	(      )->		Any:
  _snake_case   :				List[str]														=	{
      '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   :				Optional[Any]														=	Dataset.from_dict(lowerCAmelCase				)
  return dataset







class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				Union[str, Any]														=	get_dataset()
    _snake_case   :				Tuple														=	make_duplicate_clusters(UpperCamelCase      ,				0.85							)
    self.assertEqual(len(duplicate_clusters[0]							)      ,				2							)







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	get_dataset()
    _snake_case							,					_snake_case   :				str														=	deduplicate_dataset(UpperCamelCase							)
    self.assertEqual(len(UpperCamelCase							)      ,				2							)
    print(UpperCamelCase							)
    self.assertEqual(duplicate_clusters[0][0]['copies']      ,				2							)
    self.assertEqual(duplicate_clusters[0][0]['is_extreme']      ,				UpperCamelCase							)




