

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


lowerCAmelCase_			      =      logging.get_logger(__name__)





@dataclass
class       snake_case_  (						__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  str		     =							[
			    "no_inference",
			    "no_cuda",
			    "no_tpu",
			    "no_speed",
			    "no_memory",
			    "no_env_print",
			    "no_multi_process",
			]







			def __init__(	self					:							Dict				,  **_UpperCamelCase					:							List[Any]				) ->Any:
									for deprecated_arg in self.deprecated_args:
															if deprecated_arg in kwargs:
																					snake_case_  =      deprecated_arg[3:]
																					snake_case_  =      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_  =      kwargs.pop('''tpu_name'''				,  self.tpu_name				)
									snake_case_  =      kwargs.pop('''device_idx'''				,  self.device_idx				)
									snake_case_  =      kwargs.pop('''eager_mode'''				,  self.eager_mode				)
									snake_case_  =      kwargs.pop('''use_xla'''				,  self.use_xla				)
									super().__init__(**_UpperCamelCase				)

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







			@cached_property
			def 							snake_case__(	self					:							Optional[Any]				) ->Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
									requires_backends(self				,  ['''tf''']				)
									snake_case_  =      None
									if self.tpu:
															try:
																					if self.tpu_name:
																											snake_case_  =      tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name				)
																					else:
																											snake_case_  =      tf.distribute.cluster_resolver.TPUClusterResolver()
															except ValueError:
																					snake_case_  =      None
									return tpu







			@cached_property
			def 							snake_case__(	self					:							Tuple				) ->Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
									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				)

															snake_case_  =      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'''				)
																					snake_case_  =      tf.distribute.OneDeviceStrategy(device=f'''/gpu:{self.device_idx}'''				)
															else:
																					tf.config.set_visible_devices([]				,  '''GPU'''				)  # disable GPU
																					snake_case_  =      tf.distribute.OneDeviceStrategy(device=f'''/cpu:{self.device_idx}'''				)

									return strategy







			@property
			def 							snake_case__(	self					:							Dict				) ->bool:
									requires_backends(self				,  ['''tf''']				)
									return self._setup_tpu is not None







			@property
			def 							snake_case__(	self					:							Union[str, Any]				) ->"tf.distribute.Strategy":
									requires_backends(self				,  ['''tf''']				)
									return self._setup_strategy







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







			@property
			def 							snake_case__(	self					:							Dict				) ->int:
									requires_backends(self				,  ['''tf''']				)
									if self.cuda:
															return len(self.gpu_list				)
									return 0







			@property
			def 							snake_case__(	self					:							Union[str, Any]				) ->bool:
									return self.n_gpu > 0






'''simple docstring'''

from __future__ import annotations





def 		UpperCamelCase							(				_lowerCamelCase   :   list[int]			):  # This function is recursive
    A__							=     len(_lowerCamelCase			)
    # If the array contains only one element, we return it (it's the stop condition of
    # recursion)
    if array_length <= 1:
        return array
        # Else
    A__							=     array[0]
    A__							=     False
    A__							=     1
    A__							=     []
    while not is_found and i < array_length:
        if array[i] < pivot:
            A__							=     True
            A__							=     [element for element in array[i:] if element >= array[i]]
            A__							=     longest_subsequence(_lowerCamelCase			)
            if len(_lowerCamelCase			) > len(_lowerCamelCase			):
                A__							=     temp_array
        else:
            i += 1

    A__							=     [element for element in array[1:] if element >= pivot]
    A__							=     [pivot, *longest_subsequence(_lowerCamelCase			)]
    if len(_lowerCamelCase			) > len(_lowerCamelCase			):
        return temp_array
    else:
        return longest_subseq


if __name__ == "__main__":
  import doctest

  doctest.testmod()



