




"""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


lowercase__     						=						logging.get_logger(__name__)






@dataclass
class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			[
			    """no_inference""",
			    """no_cuda""",
			    """no_tpu""",
			    """no_speed""",
			    """no_memory""",
			    """no_env_print""",
			    """no_multi_process""",
			]
			def __init__(  self  ,      **lowercase  ):
							for deprecated_arg in self.deprecated_args:
											if deprecated_arg in kwargs:
															_lowerCamelCase     :			Any    =    deprecated_arg[3:]
															_lowerCamelCase     :			int    =    not kwargs.pop(lowercase  )
															logger.warning(
															    F'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
															    F''' {positive_arg}={kwargs[positive_arg]}'''  )
							_lowerCamelCase     :			Any    =    kwargs.pop('tpu_name'  ,      self.tpu_name  )
							_lowerCamelCase     :			Dict    =    kwargs.pop('device_idx'  ,      self.device_idx  )
							_lowerCamelCase     :			int    =    kwargs.pop('eager_mode'  ,      self.eager_mode  )
							_lowerCamelCase     :			List[str]    =    kwargs.pop('use_xla'  ,      self.use_xla  )
							super().__init__(**lowercase  )

			lowerCamelCase__      =			field(
			    default=lowercase,	metadata={"""help""": """Name of TPU"""},	)
			lowerCamelCase__      =			field(
			    default=0,	metadata={"""help""": """CPU / GPU device index. Defaults to 0."""},	)
			lowerCamelCase__      =			field(default=lowercase,	metadata={"""help""": """Benchmark models in eager model."""}						)
			lowerCamelCase__      =			field(
			    default=lowercase,	metadata={
			        """help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
			    },	)
			@cached_property
			def 					A_    (  self  ):
							requires_backends(self  ,      ['tf']  )
							_lowerCamelCase     :			int    =    None
							if self.tpu:
											try:
															if self.tpu_name:
																			_lowerCamelCase     :			List[Any]    =    tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name  )
															else:
																			_lowerCamelCase     :			str    =    tf.distribute.cluster_resolver.TPUClusterResolver()
											except ValueError:
															_lowerCamelCase     :			List[str]    =    None
							return tpu
			@cached_property
			def 					A_    (  self  ):
							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     :			Any    =    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     :			Dict    =    tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}'''  )
											else:
															tf.config.set_visible_devices([]  ,      'GPU'  )  # disable GPU
															_lowerCamelCase     :			int    =    tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}'''  )

							return strategy
			@property
			def 					A_    (  self  ):
							requires_backends(self  ,      ['tf']  )
							return self._setup_tpu is not None
			@property
			def 					A_    (  self  ):
							requires_backends(self  ,      ['tf']  )
							return self._setup_strategy
			@property
			def 					A_    (  self  ):
							requires_backends(self  ,      ['tf']  )
							return tf.config.list_physical_devices('GPU'  )
			@property
			def 					A_    (  self  ):
							requires_backends(self  ,      ['tf']  )
							if self.cuda:
											return len(self.gpu_list  )
							return 0






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






"""simple docstring"""



import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


a_ =			50000
a_ =			5000

a_ ,			a_ =			os.path.split(__file__)
a_ =			os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))







@get_duration
def 		__lowercase     (   snake_case_  :  datasets.Dataset				,snake_case_  :  Tuple						)   ->Tuple:



    '''simple docstring'''







    for i in range(snake_case_						):
        __A    :						int						 =     dataset[i]







@get_duration
def 		__lowercase     (   snake_case_  :  datasets.Dataset				,snake_case_  :  Optional[Any]				,snake_case_  :  int						)   ->Tuple:



    '''simple docstring'''







    for i in range(0				,len(snake_case_						)				,snake_case_						):
        __A    :						List[str]						 =     dataset[i : i + batch_size]







@get_duration
def 		__lowercase     (   snake_case_  :  datasets.Dataset				,snake_case_  :  List[Any]				,snake_case_  :  Any						)   ->int:



    '''simple docstring'''







    with dataset.formatted_as(type=snake_case_						):
        for i in range(snake_case_						):
            __A    :						Union[str, Any]						 =     dataset[i]







@get_duration
def 		__lowercase     (   snake_case_  :  datasets.Dataset				,snake_case_  :  Any				,snake_case_  :  Union[str, Any]				,snake_case_  :  Optional[int]						)   ->Union[str, Any]:



    '''simple docstring'''







    with dataset.formatted_as(type=snake_case_						):
        for i in range(0				,snake_case_				,snake_case_						):
            __A    :						Dict						 =     dataset[i : i + batch_size]







def 		__lowercase     (   )   ->Optional[int]:



    '''simple docstring'''







    __A    :						int						 =     {'''num examples''': SPEED_TEST_N_EXAMPLES}
    __A    :						Optional[int]						 =     [
        (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''': 100}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1000}),
        (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''': 1000}),
    ]

    __A    :						int						 =     [
        (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''': 100}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1000}),
        (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''': 1000}),
    ]
    with tempfile.TemporaryDirectory() as tmp_dir:
        print('''generating dataset'''						)
        __A    :						Any						 =     datasets.Features(
            {'''list''': datasets.Sequence(datasets.Value('''float32'''						)						), '''numbers''': datasets.Value('''float32'''						)}						)
        __A    :						List[Any]						 =     generate_example_dataset(
            os.path.join(snake_case_				,'''dataset.arrow'''						)				,snake_case_				,num_examples=snake_case_				,seq_shapes={'''list''': (100,)}				,)
        print('''first set of iterations'''						)
        for func, kwargs in functions:
            print(func.__name__				,str(snake_case_						)						)
            __A    :						Dict						 =     func(snake_case_				,**snake_case_						)

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

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


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




