



'''simple docstring'''



from typing import TYPE_CHECKING

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


lowerCAmelCase_     :       Optional[int]								=						{'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       Any								=						['GLPNFeatureExtractor']
							lowerCAmelCase_     :       str								=						['GLPNImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       Union[str, Any]								=						[
							    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'GLPNForDepthEstimation',
							    'GLPNLayer',
							    'GLPNModel',
							    'GLPNPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_glpn import GLPNFeatureExtractor
														from .image_processing_glpn import GLPNImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_glpn import (
														    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GLPNForDepthEstimation,
														    GLPNLayer,
														    GLPNModel,
														    GLPNPreTrainedModel,
														)


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_     :       Optional[int]								=						logging.get_logger(__name__)

lowerCAmelCase_     :       Optional[Any]								=						{
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}
class 	lowerCamelCase_							(					snake_case_			):
						_lowerCAmelCase     :   str	     =				'swinv2'

						_lowerCAmelCase     :   Optional[int]	     =				{
						    'num_attention_heads': 'num_heads',
						    'num_hidden_layers': 'num_layers',
						}







						def __init__(  self   :      Optional[int]				,    lowerCAmelCase__   :      Any=2_24				,    lowerCAmelCase__   :      Optional[Any]=4				,    lowerCAmelCase__   :      Tuple=3				,    lowerCAmelCase__   :      Tuple=96				,    lowerCAmelCase__   :      Optional[int]=[2, 2, 6, 2]				,    lowerCAmelCase__   :      Tuple=[3, 6, 12, 24]				,    lowerCAmelCase__   :      List[str]=7				,    lowerCAmelCase__   :      Tuple=4.0				,    lowerCAmelCase__   :      Dict=True				,    lowerCAmelCase__   :      Any=0.0				,    lowerCAmelCase__   :      Optional[int]=0.0				,    lowerCAmelCase__   :      Optional[Any]=0.1				,    lowerCAmelCase__   :      int="gelu"				,    lowerCAmelCase__   :      Tuple=False				,    lowerCAmelCase__   :      Tuple=0.02				,    lowerCAmelCase__   :      Dict=1e-5				,    lowerCAmelCase__   :      List[str]=32				,    **lowerCAmelCase__   :      Union[str, Any]				,    ):

													"""simple docstring"""
													super().__init__(**lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      int     					=			image_size
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			patch_size
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			num_channels
													SCREAMING_SNAKE_CASE					:      int     					=			embed_dim
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			depths
													SCREAMING_SNAKE_CASE					:      str     					=			len(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			num_heads
													SCREAMING_SNAKE_CASE					:      Tuple     					=			window_size
													SCREAMING_SNAKE_CASE					:      Any     					=			mlp_ratio
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			qkv_bias
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			hidden_dropout_prob
													SCREAMING_SNAKE_CASE					:      int     					=			attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE					:      List[str]     					=			drop_path_rate
													SCREAMING_SNAKE_CASE					:      Any     					=			hidden_act
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			use_absolute_embeddings
													SCREAMING_SNAKE_CASE					:      List[str]     					=			layer_norm_eps
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			initializer_range
													SCREAMING_SNAKE_CASE					:      Dict     					=			encoder_stride
													# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
													# this indicates the channel dimension after the last stage of the model
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			int(embed_dim * 2 ** (len(lowerCAmelCase__				) - 1)				)
													SCREAMING_SNAKE_CASE					:      int     					=			(0, 0, 0, 0)




