
import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


_A  					= logging.get_logger(__name__)

if is_tf_available():
   import tensorflow as tf







@dataclass
class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							Union[str, Any]           =		"tf"
     UpperCAmelCase__				:							Optional[str]           =		field(
         default=A_							, metadata={"help": "Name of TPU"}							, )

     UpperCAmelCase__				:							Optional[str]           =		field(
         default=A_							, metadata={"help": "Zone of TPU"}							, )

     UpperCAmelCase__				:							Optional[str]           =		field(
         default=A_							, metadata={"help": "Name of Cloud TPU-enabled project"}							, )

     UpperCAmelCase__				:							float           =		field(
         default=1.0							, metadata={"help": "Power for the Polynomial decay LR scheduler."}							, )

     UpperCAmelCase__				:							bool           =		field(default=A_							, metadata={"help": "Whether to activate the XLA compilation or not"}  )





     @cached_property
     def 		UpperCAmelCase(self      :    Dict   )						->						Tuple["tf.distribute.Strategy", int]:
         requires_backends(self				,    ["tf"]   )
         logger.info("Tensorflow: setting up strategy"   )

         snake_case      =					tf.config.list_physical_devices("GPU"   )

         # Set to float16 at first
         if self.fpaa:
             tf.keras.mixed_precision.set_global_policy("mixed_float16"   )

         if self.no_cuda:
             snake_case      =					tf.distribute.OneDeviceStrategy(device="/cpu:0"   )
         else:
             try:
                 if self.tpu_name:
                     snake_case      =					tf.distribute.cluster_resolver.TPUClusterResolver(
                         self.tpu_name				,    zone=self.tpu_zone				,    project=self.gcp_project   )
                 else:
                     snake_case      =					tf.distribute.cluster_resolver.TPUClusterResolver()
             except ValueError:
                 if self.tpu_name:
                     raise RuntimeError(f'Couldn\'t connect to TPU {self.tpu_name}!'   )
                 else:
                     snake_case      =					None

             if tpu:
                 # Set to bfloat16 in case of TPU
                 if self.fpaa:
                     tf.keras.mixed_precision.set_global_policy("mixed_bfloat16"   )

                 tf.config.experimental_connect_to_cluster(_A   )
                 tf.tpu.experimental.initialize_tpu_system(_A   )

                 snake_case      =					tf.distribute.TPUStrategy(_A   )

             elif len(_A   ) == 0:
                 snake_case      =					tf.distribute.OneDeviceStrategy(device="/cpu:0"   )
             elif len(_A   ) == 1:
                 snake_case      =					tf.distribute.OneDeviceStrategy(device="/gpu:0"   )
             elif len(_A   ) > 1:
                 # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
                 snake_case      =					tf.distribute.MirroredStrategy()
             else:
                 raise ValueError("Cannot find the proper strategy, please check your environment properties."   )

         return strategy





     @property
     def 		UpperCAmelCase(self      :    Tuple   )						->						"tf.distribute.Strategy":
         requires_backends(self				,    ["tf"]   )
         return self._setup_strategy





     @property
     def 		UpperCAmelCase(self      :    Tuple   )						->						int:
         requires_backends(self				,    ["tf"]   )
         return self._setup_strategy.num_replicas_in_sync





     @property
     def 		UpperCAmelCase(self      :    List[Any]   )						->						List[Any]:
         return False  # TF Logging is handled by Keras not the Trainer





     @property
     def 		UpperCAmelCase(self      :    Dict   )						->						int:
         if self.per_gpu_train_batch_size:
             logger.warning(
                 "Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future "
                 "version. Using `--per_device_train_batch_size` is preferred."   )
         snake_case      =					self.per_gpu_train_batch_size or self.per_device_train_batch_size
         return per_device_batch_size * self.n_replicas





     @property
     def 		UpperCAmelCase(self      :    int   )						->						int:
         if self.per_gpu_eval_batch_size:
             logger.warning(
                 "Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future "
                 "version. Using `--per_device_eval_batch_size` is preferred."   )
         snake_case      =					self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
         return per_device_batch_size * self.n_replicas





     @property
     def 		UpperCAmelCase(self      :    Dict   )						->						int:
         requires_backends(self				,    ["tf"]   )
         warnings.warn(
             "The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead."				,    _A				,    )
         return self._setup_strategy.num_replicas_in_sync



import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_A  					= 50_00_00

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



@get_duration
def 						lowercase_			(   A__		)							->							str:
    """simple docstring"""

    snake_case      =					dataset.select(range(0 ,		len(A__		) ,		2		)		)



@get_duration
def 						lowercase_			(   A__		)							->							int:
    """simple docstring"""

    snake_case      =					dataset.sort("numbers"		)



@get_duration
def 						lowercase_			(   A__		)							->							Optional[int]:
    """simple docstring"""

    snake_case      =					dataset.shuffle()



@get_duration
def 						lowercase_			(   A__		)							->							int:
    """simple docstring"""

    snake_case      =					dataset.train_test_split(0.1		)



@get_duration
def 						lowercase_			(   A__ ,		A__=10		)							->							List[Any]:
    """simple docstring"""

    for shard_id in range(A__		):
        snake_case      =					dataset.shard(A__ ,		A__		)



def 						lowercase_			(   )							->							List[str]:
    """simple docstring"""

    snake_case      =					{"num examples": SPEED_TEST_N_EXAMPLES}
    snake_case      =					(select, sort, shuffle, train_test_split, shard)
    with tempfile.TemporaryDirectory() as tmp_dir:
        print("generating dataset"		)
        snake_case      =					datasets.Features({"text": datasets.Value("string"		), "numbers": datasets.Value("float32"		)}		)
        snake_case      =					generate_example_dataset(
            os.path.join(A__ ,		"dataset.arrow"		) ,		A__ ,		num_examples=A__		)
        print("Functions"		)
        for func in functions:
            print(func.__name__		)
            snake_case      =					func(A__		)

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


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

