


"""simple docstring"""


from typing import List

import numpy as np







def  __snake_case       (   UpperCamelCase__	)      ->		Dict:







		"""simple docstring"""





		A					       = {key: len(UpperCamelCase__	) for key, value in gen_kwargs.items() if isinstance(UpperCamelCase__ ,			UpperCamelCase__	)}
		if len(set(lists_lengths.values()	)	) > 1:
				raise RuntimeError(
				    (
				        'Sharding is ambiguous for this dataset: '
				        + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
				        + '\n'.join(f'\t- key {key} has length {length}' for key, length in lists_lengths.items()	)
				        + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
				        + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
				    )	)
		A					       = max(lists_lengths.values() ,			default=0	)
		return max(1 ,			UpperCamelCase__	)







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		A					       = []
		for group_idx in range(UpperCamelCase__	):
				A					       = num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
				if num_shards_to_add == 0:
						break
				A					       = shards_indices_per_group[-1].stop if shards_indices_per_group else 0
				A					       = range(UpperCamelCase__ ,			start + num_shards_to_add	)
				shards_indices_per_group.append(UpperCamelCase__	)
		return shards_indices_per_group







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		Tuple:







		"""simple docstring"""





		A					       = _number_of_shards_in_gen_kwargs(UpperCamelCase__	)
		if num_shards == 1:
				return [dict(UpperCamelCase__	)]
		else:
				A					       = _distribute_shards(num_shards=UpperCamelCase__ ,			max_num_jobs=UpperCamelCase__	)
				return [
				    {
				        key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
				        if isinstance(UpperCamelCase__ ,			UpperCamelCase__	)
				        else value
				        for key, value in gen_kwargs.items()
				    }
				    for group_idx in range(len(UpperCamelCase__	)	)
				]







def  __snake_case       (   UpperCamelCase__	)      ->		Dict:







		"""simple docstring"""





		return {
		    key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
		    if isinstance(gen_kwargs_list[0][key] ,			UpperCamelCase__	)
		    else gen_kwargs_list[0][key]
		    for key in gen_kwargs_list[0]
		}







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		str:







		"""simple docstring"""





		A					       = {len(UpperCamelCase__	) for value in gen_kwargs.values() if isinstance(UpperCamelCase__ ,			UpperCamelCase__	)}
		A					       = {}
		for size in list_sizes:
				A					       = list(range(UpperCamelCase__	)	)
				rng.shuffle(indices_per_size[size]	)
		# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
		A					       = dict(UpperCamelCase__	)
		for key, value in shuffled_kwargs.items():
				if isinstance(UpperCamelCase__ ,			UpperCamelCase__	):
						A					       = [value[i] for i in indices_per_size[len(UpperCamelCase__	)]]
		return shuffled_kwargs









"""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 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     [
						    """no_inference""",
						    """no_cuda""",
						    """no_tpu""",
						    """no_speed""",
						    """no_memory""",
						    """no_env_print""",
						    """no_multi_process""",
						]







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

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







						@cached_property
						def __a						(							self   :    Optional[Any]		):
								requires_backends(self			,			['tf']		)
								A					       = None
								if self.tpu:
										try:
												if self.tpu_name:
														A					       = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name		)
												else:
														A					       = tf.distribute.cluster_resolver.TPUClusterResolver()
										except ValueError:
												A					       = None
								return tpu







						@cached_property
						def __a						(							self   :    Dict		):
								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		)

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

								return strategy







						@property
						def __a						(							self   :    List[Any]		):
								requires_backends(self			,			['tf']		)
								return self._setup_tpu is not None







						@property
						def __a						(							self   :    Optional[Any]		):
								requires_backends(self			,			['tf']		)
								return self._setup_strategy







						@property
						def __a						(							self   :    str		):
								requires_backends(self			,			['tf']		)
								return tf.config.list_physical_devices('GPU'		)







						@property
						def __a						(							self   :    Any		):
								requires_backends(self			,			['tf']		)
								if self.cuda:
										return len(self.gpu_list		)
								return 0







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





