





'''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					    =	logging.get_logger(__name__)




@dataclass
class  _A (					lowercase_		):
		lowercase__:   str				     =   [
		    '''no_inference''',
		    '''no_cuda''',
		    '''no_tpu''',
		    '''no_speed''',
		    '''no_memory''',
		    '''no_env_print''',
		    '''no_multi_process''',
		]

		def __init__(   self					:   Optional[int]  ,      **__magic_name__					:   Optional[Any]			)					->    List[Any]:


					"""simple docstring"""

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

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

		@cached_property
		def 	lowercase__     (   self					:   Union[str, Any]			)					->    Optional[int]:


					"""simple docstring"""

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

		@cached_property
		def 	lowercase__     (   self					:   Tuple			)					->    Dict:


					"""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			)

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

					return strategy

		@property
		def 	lowercase__     (   self					:   List[str]			)					->    str:


					"""simple docstring"""

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

		@property
		def 	lowercase__     (   self					:   Tuple			)					->    str:


					"""simple docstring"""

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

		@property
		def 	lowercase__     (   self					:   List[str]			)					->    Any:


					"""simple docstring"""

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

		@property
		def 	lowercase__     (   self					:   Tuple			)					->    int:


					"""simple docstring"""

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

		@property
		def 	lowercase__     (   self					:   Optional[Any]			)					->    int:


					"""simple docstring"""

					return self.n_gpu > 0









'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase            =       logging.get_logger(__name__)

__UpperCAmelCase            =       {
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''fnet'''


					def __init__(						self				:			Any      ,						lowerCamelCase_				:			List[str]=3_20_00      ,						lowerCamelCase_				:			List[Any]=7_68      ,						lowerCamelCase_				:			Union[str, Any]=12      ,						lowerCamelCase_				:			Optional[Any]=30_72      ,						lowerCamelCase_				:			Optional[Any]="gelu_new"      ,						lowerCamelCase_				:			Any=0.1      ,						lowerCamelCase_				:			Union[str, Any]=5_12      ,						lowerCamelCase_				:			Optional[int]=4      ,						lowerCamelCase_				:			Optional[int]=0.02      ,						lowerCamelCase_				:			str=1e-12      ,						lowerCamelCase_				:			Any=False      ,						lowerCamelCase_				:			str=5_12      ,						lowerCamelCase_				:			str=3      ,						lowerCamelCase_				:			int=1      ,						lowerCamelCase_				:			Optional[Any]=2      ,						**lowerCamelCase_				:			Optional[int]      ,						):

							'''simple docstring'''



							super().__init__(pad_token_id=lowerCamelCase_      ,						bos_token_id=lowerCamelCase_      ,						eos_token_id=lowerCamelCase_      ,						**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Dict		=			vocab_size
							SCREAMING_SNAKE_CASE :				Dict		=			max_position_embeddings
							SCREAMING_SNAKE_CASE :				str		=			hidden_size
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			num_hidden_layers
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			intermediate_size
							SCREAMING_SNAKE_CASE :				int		=			hidden_act
							SCREAMING_SNAKE_CASE :				Dict		=			hidden_dropout_prob
							SCREAMING_SNAKE_CASE :				Tuple		=			initializer_range
							SCREAMING_SNAKE_CASE :				List[Any]		=			type_vocab_size
							SCREAMING_SNAKE_CASE :				Any		=			layer_norm_eps
							SCREAMING_SNAKE_CASE :				int		=			use_tpu_fourier_optimizations
							SCREAMING_SNAKE_CASE :				List[Any]		=			tpu_short_seq_length



