



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


lowerCAmelCase__		:					Union[str, Any]							=logging.get_logger(__name__)

lowerCAmelCase__		:					List[str]							={
    """facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json""",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}

class        UpperCAmelCase_  (       __snake_case	):

						'''simple docstring'''




						UpperCamelCase__      :			Any      =  '''vit_mae'''






						def __init__(   self				,							_A=768				,							_A=12				,							_A=12				,							_A=3_072				,							_A="gelu"				,							_A=0.0				,							_A=0.0				,							_A=0.0_2				,							_A=1e-12				,							_A=224				,							_A=16				,							_A=3				,							_A=True				,							_A=16				,							_A=512				,							_A=8				,							_A=2_048				,							_A=0.7_5				,							_A=False				,							**_A				,							):







													'''simple docstring'''

													super().__init__(**a_		)

													__SCREAMING_SNAKE_CASE									= hidden_size
													__SCREAMING_SNAKE_CASE									= num_hidden_layers
													__SCREAMING_SNAKE_CASE									= num_attention_heads
													__SCREAMING_SNAKE_CASE									= intermediate_size
													__SCREAMING_SNAKE_CASE									= hidden_act
													__SCREAMING_SNAKE_CASE									= hidden_dropout_prob
													__SCREAMING_SNAKE_CASE									= attention_probs_dropout_prob
													__SCREAMING_SNAKE_CASE									= initializer_range
													__SCREAMING_SNAKE_CASE									= layer_norm_eps
													__SCREAMING_SNAKE_CASE									= image_size
													__SCREAMING_SNAKE_CASE									= patch_size
													__SCREAMING_SNAKE_CASE									= num_channels
													__SCREAMING_SNAKE_CASE									= qkv_bias
													__SCREAMING_SNAKE_CASE									= decoder_num_attention_heads
													__SCREAMING_SNAKE_CASE									= decoder_hidden_size
													__SCREAMING_SNAKE_CASE									= decoder_num_hidden_layers
													__SCREAMING_SNAKE_CASE									= decoder_intermediate_size
													__SCREAMING_SNAKE_CASE									= mask_ratio
													__SCREAMING_SNAKE_CASE									= norm_pix_loss











from typing import TYPE_CHECKING

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


lowerCAmelCase__		:					Optional[int]							={'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[int]							=['''FNetTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[Any]							=['''FNetTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Tuple							=[
			    '''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FNetForMaskedLM''',
			    '''FNetForMultipleChoice''',
			    '''FNetForNextSentencePrediction''',
			    '''FNetForPreTraining''',
			    '''FNetForQuestionAnswering''',
			    '''FNetForSequenceClassification''',
			    '''FNetForTokenClassification''',
			    '''FNetLayer''',
			    '''FNetModel''',
			    '''FNetPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet import FNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet_fast import FNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_fnet import (
						    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FNetForMaskedLM,
						    FNetForMultipleChoice,
						    FNetForNextSentencePrediction,
						    FNetForPreTraining,
						    FNetForQuestionAnswering,
						    FNetForSequenceClassification,
						    FNetForTokenClassification,
						    FNetLayer,
						    FNetModel,
						    FNetPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase__		:					Any							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






