

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase      				=     logging.get_logger(__name__)

lowerCamelCase      				=     {
    '''microsoft/swin-tiny-patch4-window7-224''': (
        '''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}





class     snake_case_			(				_a			, _a							):


							"""simple docstring"""





							__UpperCAmelCase       ="""swin"""

							__UpperCAmelCase       ={
							    """num_attention_heads""": """num_heads""",
							    """num_hidden_layers""": """num_layers""",
							}




							def __init__( self					,						_A=2_2_4					,						_A=4					,						_A=3					,						_A=9_6					,						_A=[2, 2, 6, 2]					,						_A=[3, 6, 1_2, 2_4]					,						_A=7					,						_A=4.0					,						_A=True					,						_A=0.0					,						_A=0.0					,						_A=0.1					,						_A="gelu"					,						_A=False					,						_A=0.02					,						_A=1e-5					,						_A=3_2					,						_A=None					,						_A=None					,						**_A					,						):
												super().__init__(**_A							)

												__lowerCAmelCase					=							image_size
												__lowerCAmelCase					=							patch_size
												__lowerCAmelCase					=							num_channels
												__lowerCAmelCase					=							embed_dim
												__lowerCAmelCase					=							depths
												__lowerCAmelCase					=							len(_A							)
												__lowerCAmelCase					=							num_heads
												__lowerCAmelCase					=							window_size
												__lowerCAmelCase					=							mlp_ratio
												__lowerCAmelCase					=							qkv_bias
												__lowerCAmelCase					=							hidden_dropout_prob
												__lowerCAmelCase					=							attention_probs_dropout_prob
												__lowerCAmelCase					=							drop_path_rate
												__lowerCAmelCase					=							hidden_act
												__lowerCAmelCase					=							use_absolute_embeddings
												__lowerCAmelCase					=							layer_norm_eps
												__lowerCAmelCase					=							initializer_range
												__lowerCAmelCase					=							encoder_stride
												# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
												# this indicates the channel dimension after the last stage of the model
												__lowerCAmelCase					=							int(embed_dim * 2 ** (len(_A							) - 1)							)
												__lowerCAmelCase					=							['stem'] + [F"""stage{idx}""" for idx in range(1					,						len(_A							) + 1							)]
												__lowerCAmelCase,	__lowerCAmelCase					=							get_aligned_output_features_output_indices(
												    out_features=_A					,						out_indices=_A					,						stage_names=self.stage_names							)





class     snake_case_			(				_a							):


							"""simple docstring"""





							__UpperCAmelCase       =version.parse("""1.11"""							)




							@property
							def        A__  ( self							):
												return OrderedDict(
												    [
												        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
												    ]							)




							@property
							def        A__  ( self							):
												return 1e-4









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase      				=     {
    '''configuration_lilt''': ['''LILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LiltConfig'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase      				=     [
		    '''LILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''LiltForQuestionAnswering''',
		    '''LiltForSequenceClassification''',
		    '''LiltForTokenClassification''',
		    '''LiltModel''',
		    '''LiltPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_lilt import (
				    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LiltForQuestionAnswering,
				    LiltForSequenceClassification,
				    LiltForTokenClassification,
				    LiltModel,
				    LiltPreTrainedModel,
				)

else:
		import sys

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






