'''simple docstring'''

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


__a: List[Any]          =   logging.get_logger(__name__)

__a: Union[str, Any]          =   {
    '''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  (						__a					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    '''fnet'''

       def __init__(						self	,   __lowerCAmelCase=32000	,   __lowerCAmelCase=768	,   __lowerCAmelCase=12	,   __lowerCAmelCase=3072	,   __lowerCAmelCase="gelu_new"	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=512	,   __lowerCAmelCase=4	,   __lowerCAmelCase=0.0_2	,   __lowerCAmelCase=1E-12	,   __lowerCAmelCase=False	,   __lowerCAmelCase=512	,   __lowerCAmelCase=3	,   __lowerCAmelCase=1	,   __lowerCAmelCase=2	,   **__lowerCAmelCase	,   )  ->  str:
        super().__init__(pad_token_id=__a	,   bos_token_id=__a	,   eos_token_id=__a	,   **__a )

        lowercase__				:    Any  =					vocab_size
        lowercase__				:    Optional[int]  =					max_position_embeddings
        lowercase__				:    Dict  =					hidden_size
        lowercase__				:    Dict  =					num_hidden_layers
        lowercase__				:    Optional[int]  =					intermediate_size
        lowercase__				:    Optional[Any]  =					hidden_act
        lowercase__				:    Optional[int]  =					hidden_dropout_prob
        lowercase__				:    Dict  =					initializer_range
        lowercase__				:    int  =					type_vocab_size
        lowercase__				:    Optional[int]  =					layer_norm_eps
        lowercase__				:    List[Any]  =					use_tpu_fourier_optimizations
        lowercase__				:    Optional[int]  =					tpu_short_seq_length









lowerCamelCase       :       Dict        =				'''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
	from .utils import rich