





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


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

A   : List[Any]			=				{
    '''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json''',
}


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	Optional[int]          =   '''mgp-str'''






			def __init__(      self       :   Tuple							,     __lowerCAmelCase       :   Tuple=[32, 1_28]							,     __lowerCAmelCase       :   Optional[Any]=4							,     __lowerCAmelCase       :   List[str]=3							,     __lowerCAmelCase       :   Optional[int]=27							,     __lowerCAmelCase       :   Optional[Any]=38							,     __lowerCAmelCase       :   int=5_02_57							,     __lowerCAmelCase       :   int=3_05_22							,     __lowerCAmelCase       :   Any=7_68							,     __lowerCAmelCase       :   Dict=12							,     __lowerCAmelCase       :   Optional[Any]=12							,     __lowerCAmelCase       :   int=4.0							,     __lowerCAmelCase       :   Union[str, Any]=True							,     __lowerCAmelCase       :   List[str]=False							,     __lowerCAmelCase       :   List[str]=1e-5							,     __lowerCAmelCase       :   List[str]=0.0							,     __lowerCAmelCase       :   Dict=0.0							,     __lowerCAmelCase       :   Any=0.0							,     __lowerCAmelCase       :   List[str]=False							,     __lowerCAmelCase       :   int=0.0_2							,     **__lowerCAmelCase       :   List[Any]							,     )     ->     List[str]:







					"""simple docstring"""






					super().__init__(**__lowerCAmelCase       )

					A__  				=       image_size
					A__  				=       patch_size
					A__  				=       num_channels
					A__  				=       max_token_length
					A__  				=       num_character_labels
					A__  				=       num_bpe_labels
					A__  				=       num_wordpiece_labels
					A__  				=       hidden_size
					A__  				=       num_hidden_layers
					A__  				=       num_attention_heads
					A__  				=       mlp_ratio
					A__  				=       distilled
					A__  				=       layer_norm_eps
					A__  				=       drop_rate
					A__  				=       qkv_bias
					A__  				=       attn_drop_rate
					A__  				=       drop_path_rate
					A__  				=       output_aa_attentions
					A__  				=       initializer_range










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A   : Optional[Any]			=				{'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A   : List[str]			=				[
					    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''ViTMAEForPreTraining''',
					    '''ViTMAELayer''',
					    '''ViTMAEModel''',
					    '''ViTMAEPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A   : Any			=				[
					    '''TFViTMAEForPreTraining''',
					    '''TFViTMAEModel''',
					    '''TFViTMAEPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_vit_mae import (
										    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ViTMAEForPreTraining,
										    ViTMAELayer,
										    ViTMAEModel,
										    ViTMAEPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
					import sys

					A   : str			=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



