


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_A		       =						5_0000
_A		       =						5000

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

@get_duration
def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					datasets.Dataset			,     SCREAMING_SNAKE_CASE__			:					Any				):
     for i in range(SCREAMING_SNAKE_CASE__				):
          __UpperCamelCase    							=dataset[i]

@get_duration
def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					datasets.Dataset			,     SCREAMING_SNAKE_CASE__			:					Optional[int]			,     SCREAMING_SNAKE_CASE__			:					List[str]				):
     for i in range(0			,     len(SCREAMING_SNAKE_CASE__				)			,     SCREAMING_SNAKE_CASE__				):
          __UpperCamelCase    							=dataset[i : i + batch_size]

@get_duration
def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					datasets.Dataset			,     SCREAMING_SNAKE_CASE__			:					Union[str, Any]			,     SCREAMING_SNAKE_CASE__			:					Any				):
     with dataset.formatted_as(type=SCREAMING_SNAKE_CASE__				):
          for i in range(SCREAMING_SNAKE_CASE__				):
               __UpperCamelCase    							=dataset[i]

@get_duration
def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					datasets.Dataset			,     SCREAMING_SNAKE_CASE__			:					Dict			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					Dict				):
     with dataset.formatted_as(type=SCREAMING_SNAKE_CASE__				):
          for i in range(0			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				):
               __UpperCamelCase    							=dataset[i : i + batch_size]

def 						_UpperCAmelCase				(			):
     __UpperCamelCase    							={'num examples': SPEED_TEST_N_EXAMPLES}
     __UpperCamelCase    							=[
         (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}),
     ]

     __UpperCamelCase    							=[
         (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'				)
          __UpperCamelCase    							=datasets.Features(
              {'list': datasets.Sequence(datasets.Value('float32'				)				), 'numbers': datasets.Value('float32'				)}				)
          __UpperCamelCase    							=generate_example_dataset(
              os.path.join(SCREAMING_SNAKE_CASE__			,     'dataset.arrow'				)			,     SCREAMING_SNAKE_CASE__			,     num_examples=SCREAMING_SNAKE_CASE__			,     seq_shapes={'list': (1_00,)}			,     )
          print('first set of iterations'				)
          for func, kwargs in functions:
               print(func.__name__			,     str(SCREAMING_SNAKE_CASE__				)				)
               __UpperCamelCase    							=func(SCREAMING_SNAKE_CASE__			,     **SCREAMING_SNAKE_CASE__				)

          print('shuffling dataset'				)
          __UpperCamelCase    							=dataset.shuffle()
          print('Second set of iterations (after shuffling'				)
          for func, kwargs in functions_shuffled:
               print('shuffled '			,     func.__name__			,     str(SCREAMING_SNAKE_CASE__				)				)
               __UpperCamelCase    							=func(
                   SCREAMING_SNAKE_CASE__			,     **SCREAMING_SNAKE_CASE__				)

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


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











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__    			=			logging.get_logger(__name__)
@dataclass
class 			a__    (					snake_case__      ):
						_a							:    List[str]					=    [
						    """no_inference""",
						    """no_cuda""",
						    """no_tpu""",
						    """no_speed""",
						    """no_memory""",
						    """no_env_print""",
						    """no_multi_process""",
						]






						def __init__(   self		,			**_A			):




											"""simple docstring"""



											for deprecated_arg in self.deprecated_args:
																if deprecated_arg in kwargs:
																					__lowerCAmelCase				   =       deprecated_arg[3:]
																					__lowerCAmelCase				   =       not kwargs.pop(_A			)
																					logger.warning(
																					    f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
																					    f""" {positive_arg}={kwargs[positive_arg]}"""			)
											__lowerCAmelCase				   =       kwargs.pop("tpu_name"		,			self.tpu_name			)
											__lowerCAmelCase				   =       kwargs.pop("device_idx"		,			self.device_idx			)
											__lowerCAmelCase				   =       kwargs.pop("eager_mode"		,			self.eager_mode			)
											__lowerCAmelCase				   =       kwargs.pop("use_xla"		,			self.use_xla			)
											super().__init__(**_A			)

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






						@cached_property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											requires_backends(self		,			["tf"]			)
											__lowerCAmelCase				   =       None
											if self.tpu:
																try:
																					if self.tpu_name:
																										__lowerCAmelCase				   =       tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name			)
																					else:
																										__lowerCAmelCase				   =       tf.distribute.cluster_resolver.TPUClusterResolver()
																except ValueError:
																					__lowerCAmelCase				   =       None
											return tpu






						@cached_property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""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			)

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

											return strategy






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											requires_backends(self		,			["tf"]			)
											return self._setup_tpu is not None






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											requires_backends(self		,			["tf"]			)
											return self._setup_strategy






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											requires_backends(self		,			["tf"]			)
											return tf.config.list_physical_devices("GPU"			)






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											requires_backends(self		,			["tf"]			)
											if self.cuda:
																return len(self.gpu_list			)
											return 0






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return self.n_gpu > 0




