
'''simple docstring'''






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_							: Optional[Any] =							logging.get_logger(__name__)

@dataclass
class 			UpperCAmelCase__    (							A			):
    lowerCAmelCase_					     =		[
        'no_inference',
        'no_cuda',
        'no_tpu',
        'no_speed',
        'no_memory',
        'no_env_print',
        'no_multi_process',
    ]



    def __init__(						self   :							Dict,**__A   :							Optional[int]  ):
          for deprecated_arg in self.deprecated_args:
                if deprecated_arg in kwargs:
                      _lowerCamelCase					: Tuple		    = deprecated_arg[3:]
                      _lowerCamelCase					: Union[str, Any]		    = not kwargs.pop(__A  )
                      logger.warning(
                          f'{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'
                          f' {positive_arg}={kwargs[positive_arg]}'  )
          _lowerCamelCase					: List[Any]		    = kwargs.pop("tpu_name",self.tpu_name  )
          _lowerCamelCase					: Union[str, Any]		    = kwargs.pop("device_idx",self.device_idx  )
          _lowerCamelCase					: Any		    = kwargs.pop("eager_mode",self.eager_mode  )
          _lowerCamelCase					: Tuple		    = kwargs.pop("use_xla",self.use_xla  )
          super().__init__(**__A  )

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



    @cached_property
    def 	lowerCamelCase_	(						self   :							Optional[Any]  ):
          requires_backends(self,["tf"]  )
          _lowerCamelCase					: Optional[int]		    = None
          if self.tpu:
                try:
                      if self.tpu_name:
                            _lowerCamelCase					: Union[str, Any]		    = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name  )
                      else:
                            _lowerCamelCase					: Union[str, Any]		    = tf.distribute.cluster_resolver.TPUClusterResolver()
                except ValueError:
                      _lowerCamelCase					: int		    = None
          return tpu



    @cached_property
    def 	lowerCamelCase_	(						self   :							Optional[Any]  ):
          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  )

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

          return strategy



    @property
    def 	lowerCamelCase_	(						self   :							Any  ):
          requires_backends(self,["tf"]  )
          return self._setup_tpu is not None



    @property
    def 	lowerCamelCase_	(						self   :							List[str]  ):
          requires_backends(self,["tf"]  )
          return self._setup_strategy



    @property
    def 	lowerCamelCase_	(						self   :							Tuple  ):
          requires_backends(self,["tf"]  )
          return tf.config.list_physical_devices("GPU"  )



    @property
    def 	lowerCamelCase_	(						self   :							int  ):
          requires_backends(self,["tf"]  )
          if self.cuda:
                return len(self.gpu_list  )
          return 0



    @property
    def 	lowerCamelCase_	(						self   :							Dict  ):
          return self.n_gpu > 0






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowercase		:  List[str]			       =5_0000
_lowercase		:  str			       =5000

_lowercase       , _lowercase		:  List[str]			       =os.path.split(__file__)
_lowercase		:  Union[str, Any]			       =os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		for i in range(lowerCAmelCase__						):
				lowerCamelCase_	:     Tuple 						=      dataset[i]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		for i in range(0							,len(lowerCAmelCase__						)							,lowerCAmelCase__						):
				lowerCamelCase_	:     Tuple 						=      dataset[i : i + batch_size]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		with dataset.formatted_as(type=lowerCAmelCase__						):
				for i in range(lowerCAmelCase__						):
						lowerCamelCase_	:     Tuple 						=      dataset[i]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		with dataset.formatted_as(type=lowerCAmelCase__						):
				for i in range(0							,lowerCAmelCase__							,lowerCAmelCase__						):
						lowerCamelCase_	:     Optional[int] 						=      dataset[i : i + batch_size]


def 		_SCREAMING_SNAKE_CASE     ( ):
		lowerCamelCase_	:     Any 						=      {'num examples': SPEED_TEST_N_EXAMPLES}
		lowerCamelCase_	:     Optional[Any] 						=      [
		    (read, {'length': SMALL_TEST}),
		    (read, {'length': SPEED_TEST_N_EXAMPLES}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
		    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
		]

		lowerCamelCase_	:     Optional[Any] 						=      [
		    (read, {'length': SMALL_TEST}),
		    (read, {'length': SPEED_TEST_N_EXAMPLES}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
		    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
		]
		with tempfile.TemporaryDirectory() as tmp_dir:
				print('generating dataset'						)
				lowerCamelCase_	:     List[Any] 						=      datasets.Features(
				    {'list': datasets.Sequence(datasets.Value('float32'						)						), 'numbers': datasets.Value('float32'						)}						)
				lowerCamelCase_	:     Union[str, Any] 						=      generate_example_dataset(
				    os.path.join(lowerCAmelCase__							,'dataset.arrow'						)							,lowerCAmelCase__							,num_examples=lowerCAmelCase__							,seq_shapes={'list': (1_00,)}							,)
				print('first set of iterations'						)
				for func, kwargs in functions:
						print(func.__name__							,str(lowerCAmelCase__						)						)
						lowerCamelCase_	:     Optional[int] 						=      func(lowerCAmelCase__							,**lowerCAmelCase__						)

				print('shuffling dataset'						)
				lowerCamelCase_	:     int 						=      dataset.shuffle()
				print('Second set of iterations (after shuffling'						)
				for func, kwargs in functions_shuffled:
						print('shuffled '							,func.__name__							,str(lowerCAmelCase__						)						)
						lowerCamelCase_	:     Optional[Any] 						=      func(
						    lowerCAmelCase__							,**lowerCAmelCase__						)

		with open(lowerCAmelCase__							,'wb'						) as f:
				f.write(json.dumps(lowerCAmelCase__						).encode('utf-8'						)						)


if __name__ == "__main__":  # useful to run the profiler
		benchmark_iterating()
