



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


A    =	{'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =	['PLBartTokenizer']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =	[
							    'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'PLBartForCausalLM',
							    'PLBartForConditionalGeneration',
							    'PLBartForSequenceClassification',
							    'PLBartModel',
							    'PLBartPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_plbart import PLBartTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_plbart import (
														    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PLBartForCausalLM,
														    PLBartForConditionalGeneration,
														    PLBartForSequenceClassification,
														    PLBartModel,
														    PLBartPreTrainedModel,
														)


else:
							import sys

							A    =	_LazyModule(__name__, globals()['__file__'], _import_structure)








"""simple docstring"""







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


A    =			logging.get_logger(__name__)

A    =			{
    '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__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	List[Any] 							=     """fnet"""



		def __init__( self  :    Tuple      ,							snake_case  :    Optional[int]=32_000      ,							snake_case  :    Optional[Any]=768      ,							snake_case  :    Union[str, Any]=12      ,							snake_case  :    List[str]=3_072      ,							snake_case  :    str="gelu_new"      ,							snake_case  :    Any=0.1      ,							snake_case  :    List[str]=512      ,							snake_case  :    Any=4      ,							snake_case  :    str=0.02      ,							snake_case  :    Union[str, Any]=1E-1_2      ,							snake_case  :    Optional[Any]=False      ,							snake_case  :    List[Any]=512      ,							snake_case  :    int=3      ,							snake_case  :    Any=1      ,							snake_case  :    Union[str, Any]=2      ,							**snake_case  :    Union[str, Any]      ,							)			->						Dict:






									'''simple docstring'''

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

									A         =					vocab_size
									A         =					max_position_embeddings
									A         =					hidden_size
									A         =					num_hidden_layers
									A         =					intermediate_size
									A         =					hidden_act
									A         =					hidden_dropout_prob
									A         =					initializer_range
									A         =					type_vocab_size
									A         =					layer_norm_eps
									A         =					use_tpu_fourier_optimizations
									A         =					tpu_short_seq_length



