"""simple docstring"""




from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase_    :		List[str]							=	{
    "configuration_vivit": ["VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VivitConfig"],
}
try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		List[Any]							=	["VivitImageProcessor"]


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Optional[int]							=	[
	    "VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "VivitModel",
	    "VivitPreTrainedModel",
	    "VivitForVideoClassification",
	]


if TYPE_CHECKING:
	from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_vivit import VivitImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_vivit import (
		    VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    VivitForVideoClassification,
		    VivitModel,
		    VivitPreTrainedModel,
		)


else:
	import sys

	lowerCamelCase_    :		int							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





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


lowerCamelCase_    :		Tuple							=	logging.get_logger(__name__)

lowerCamelCase_    :		List[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 						a__							(       __snake_case		):
			A__    :       Dict        =      'fnet'



			def __init__(						self				,						UpperCAmelCase=3_2_0_0_0				,						UpperCAmelCase=7_6_8				,						UpperCAmelCase=1_2				,						UpperCAmelCase=3_0_7_2				,						UpperCAmelCase="gelu_new"				,						UpperCAmelCase=0.1				,						UpperCAmelCase=5_1_2				,						UpperCAmelCase=4				,						UpperCAmelCase=0.02				,						UpperCAmelCase=1e-12				,						UpperCAmelCase=False				,						UpperCAmelCase=5_1_2				,						UpperCAmelCase=3				,						UpperCAmelCase=1				,						UpperCAmelCase=2				,						**UpperCAmelCase				,						)			->   Dict:
								super().__init__(pad_token_id=UpperCAmelCase				,						bos_token_id=UpperCAmelCase				,						eos_token_id=UpperCAmelCase				,						**UpperCAmelCase   )

								__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




