


from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		NestedDataStructureLike[PathLike]     ,    SCREAMING_SNAKE_CASE	:		Optional[NamedSplit] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[Features] = None     ,    SCREAMING_SNAKE_CASE	:		str = None     ,    SCREAMING_SNAKE_CASE	:		bool = False     ,    SCREAMING_SNAKE_CASE	:		bool = False     ,    SCREAMING_SNAKE_CASE	:		Optional[int] = None     ,    **SCREAMING_SNAKE_CASE	:		Tuple     ,    )			->	str:




											'''simple docstring'''

											super().__init__(
											    SCREAMING_SNAKE_CASE     ,    split=SCREAMING_SNAKE_CASE     ,    features=SCREAMING_SNAKE_CASE     ,    cache_dir=SCREAMING_SNAKE_CASE     ,    keep_in_memory=SCREAMING_SNAKE_CASE     ,    streaming=SCREAMING_SNAKE_CASE     ,    num_proc=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE     ,    )
											__snake_case				=		path_or_paths if isinstance(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						) else {self.split: path_or_paths}
											__snake_case				=		Text(
											    cache_dir=SCREAMING_SNAKE_CASE     ,    data_files=SCREAMING_SNAKE_CASE     ,    features=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE     ,    )






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	Union[str, Any]:




											'''simple docstring'''

											if self.streaming:
																	__snake_case				=		self.builder.as_streaming_dataset(split=self.split						)
											# Build regular (map-style) dataset
											else:
																	__snake_case				=		None
																	__snake_case				=		None
																	__snake_case				=		None
																	__snake_case				=		None

																	self.builder.download_and_prepare(
																	    download_config=SCREAMING_SNAKE_CASE     ,    download_mode=SCREAMING_SNAKE_CASE     ,    verification_mode=SCREAMING_SNAKE_CASE     ,    base_path=SCREAMING_SNAKE_CASE     ,    num_proc=self.num_proc     ,    )
																	__snake_case				=		self.builder.as_dataset(
																	    split=self.split     ,    verification_mode=SCREAMING_SNAKE_CASE     ,    in_memory=self.keep_in_memory						)
											return dataset









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


A							:					str     						=     logging.get_logger(__name__)

A							:					str     						=     {
    '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   ):
					snake_case_		:				List[Any]										=	"""fnet"""


					def __init__(						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Dict=3_2_0_0_0     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=7_6_8     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1_2     ,    SCREAMING_SNAKE_CASE	:		Any=3_0_7_2     ,    SCREAMING_SNAKE_CASE	:		Tuple="gelu_new"     ,    SCREAMING_SNAKE_CASE	:		Any=0.1     ,    SCREAMING_SNAKE_CASE	:		Dict=5_1_2     ,    SCREAMING_SNAKE_CASE	:		str=4     ,    SCREAMING_SNAKE_CASE	:		Tuple=0.02     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1e-1_2     ,    SCREAMING_SNAKE_CASE	:		Dict=False     ,    SCREAMING_SNAKE_CASE	:		int=5_1_2     ,    SCREAMING_SNAKE_CASE	:		str=3     ,    SCREAMING_SNAKE_CASE	:		str=1     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=2     ,    **SCREAMING_SNAKE_CASE	:		List[Any]     ,    )			->	List[Any]:




											'''simple docstring'''

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

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





