





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCamelCase							=     {
    'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase							=     [
	    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'NystromformerForMaskedLM',
	    'NystromformerForMultipleChoice',
	    'NystromformerForQuestionAnswering',
	    'NystromformerForSequenceClassification',
	    'NystromformerForTokenClassification',
	    'NystromformerLayer',
	    'NystromformerModel',
	    'NystromformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_nystromformer import (
		    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    NystromformerForMaskedLM,
		    NystromformerForMultipleChoice,
		    NystromformerForQuestionAnswering,
		    NystromformerForSequenceClassification,
		    NystromformerForTokenClassification,
		    NystromformerLayer,
		    NystromformerModel,
		    NystromformerPreTrainedModel,
		)


else:
	import sys

	_lowerCamelCase							=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






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


_lowerCamelCase							=     logging.get_logger(__name__)

_lowerCamelCase							=     {
    'MCG-NJU/videomae-base': 'https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json',
}




class 					UpperCamelCase_    (       UpperCamelCase__      ):
							lowerCamelCase_          =   "videomae"


							def __init__(							self      :Dict							,			__A      :List[str]=224							,			__A      :List[Any]=16							,			__A      :int=3							,			__A      :Optional[Any]=16							,			__A      :Optional[int]=2							,			__A      :Optional[Any]=768							,			__A      :List[str]=12							,			__A      :Optional[Any]=12							,			__A      :List[str]=3072							,			__A      :Tuple="gelu"							,			__A      :Optional[int]=0.0							,			__A      :Union[str, Any]=0.0							,			__A      :int=0.0_2							,			__A      :Union[str, Any]=1E-12							,			__A      :Optional[Any]=True							,			__A      :Union[str, Any]=True							,			__A      :Optional[int]=6							,			__A      :Tuple=384							,			__A      :str=4							,			__A      :List[str]=1536							,			__A      :Dict=True							,			**__A      :int							,			)				->				Optional[int]:



									"""simple docstring"""



									super().__init__(**__A						)

									SCREAMING_SNAKE_CASE__								= image_size
									SCREAMING_SNAKE_CASE__								= patch_size
									SCREAMING_SNAKE_CASE__								= num_channels
									SCREAMING_SNAKE_CASE__								= num_frames
									SCREAMING_SNAKE_CASE__								= tubelet_size

									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__								= qkv_bias
									SCREAMING_SNAKE_CASE__								= use_mean_pooling

									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__								= norm_pix_loss