






'''simple docstring'''




from typing import TYPE_CHECKING

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


__lowerCamelCase   :					Optional[Any]   							=					{
    '''configuration_efficientformer''': [
        '''EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EfficientFormerConfig''',
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase   :					Optional[Any]   							=					['''EfficientFormerImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase   :					Optional[int]   							=					[
		    '''EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''EfficientFormerForImageClassification''',
		    '''EfficientFormerForImageClassificationWithTeacher''',
		    '''EfficientFormerModel''',
		    '''EfficientFormerPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase   :					List[str]   							=					[
		    '''TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFEfficientFormerForImageClassification''',
		    '''TFEfficientFormerForImageClassificationWithTeacher''',
		    '''TFEfficientFormerModel''',
		    '''TFEfficientFormerPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_efficientformer import EfficientFormerImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_efficientformer import (
				    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    EfficientFormerForImageClassification,
				    EfficientFormerForImageClassificationWithTeacher,
				    EfficientFormerModel,
				    EfficientFormerPreTrainedModel,
				)
		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_efficientformer import (
				    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFEfficientFormerForImageClassification,
				    TFEfficientFormerForImageClassificationWithTeacher,
				    TFEfficientFormerModel,
				    TFEfficientFormerPreTrainedModel,
				)

else:
		import sys

		__lowerCamelCase   :					Dict   							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)












'''simple docstring'''




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


__lowerCamelCase   :					List[str]   							=					logging.get_logger(__name__)

__lowerCamelCase   :					int   							=					{
    '''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}







class    A_	(a_							):
							"""simple docstring"""




							a__   		=	'''cvt'''

							def __init__(		self			:List[Any]       ,							lowerCAmelCase__			:Optional[int]=3       ,							lowerCAmelCase__			:Any=[7, 3, 3]       ,							lowerCAmelCase__			:Dict=[4, 2, 2]       ,							lowerCAmelCase__			:Union[str, Any]=[2, 1, 1]       ,							lowerCAmelCase__			:Any=[64, 192, 384]       ,							lowerCAmelCase__			:List[str]=[1, 3, 6]       ,							lowerCAmelCase__			:str=[1, 2, 10]       ,							lowerCAmelCase__			:Any=[4.0, 4.0, 4.0]       ,							lowerCAmelCase__			:int=[0.0, 0.0, 0.0]       ,							lowerCAmelCase__			:Optional[Any]=[0.0, 0.0, 0.0]       ,							lowerCAmelCase__			:Dict=[0.0, 0.0, 0.1]       ,							lowerCAmelCase__			:List[Any]=[True, True, True]       ,							lowerCAmelCase__			:List[Any]=[False, False, True]       ,							lowerCAmelCase__			:Dict=["dw_bn", "dw_bn", "dw_bn"]       ,							lowerCAmelCase__			:Any=[3, 3, 3]       ,							lowerCAmelCase__			:Tuple=[1, 1, 1]       ,							lowerCAmelCase__			:Optional[int]=[2, 2, 2]       ,							lowerCAmelCase__			:Union[str, Any]=[1, 1, 1]       ,							lowerCAmelCase__			:Any=[1, 1, 1]       ,							lowerCAmelCase__			:List[str]=0.0_2       ,							lowerCAmelCase__			:Dict=1E-1_2       ,							**lowerCAmelCase__			:Optional[Any]       ,							)				->  str:


										'''simple docstring'''




										super().__init__(**lowerCAmelCase__				)
										snake_case_				:					int   =       num_channels
										snake_case_				:					int   =       patch_sizes
										snake_case_				:					Optional[Any]   =       patch_stride
										snake_case_				:					Dict   =       patch_padding
										snake_case_				:					Tuple   =       embed_dim
										snake_case_				:					Optional[int]   =       num_heads
										snake_case_				:					Union[str, Any]   =       depth
										snake_case_				:					Optional[int]   =       mlp_ratio
										snake_case_				:					Tuple   =       attention_drop_rate
										snake_case_				:					str   =       drop_rate
										snake_case_				:					Tuple   =       drop_path_rate
										snake_case_				:					Any   =       qkv_bias
										snake_case_				:					Union[str, Any]   =       cls_token
										snake_case_				:					int   =       qkv_projection_method
										snake_case_				:					Any   =       kernel_qkv
										snake_case_				:					Union[str, Any]   =       padding_kv
										snake_case_				:					str   =       stride_kv
										snake_case_				:					Dict   =       padding_q
										snake_case_				:					Tuple   =       stride_q
										snake_case_				:					Any   =       initializer_range
										snake_case_				:					Any   =       layer_norm_eps




