from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_						={
    """configuration_x_clip""": [
        """XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XCLIPConfig""",
        """XCLIPTextConfig""",
        """XCLIPVisionConfig""",
    ],
    """processing_x_clip""": ["""XCLIPProcessor"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_						=[
		    """XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """XCLIPModel""",
		    """XCLIPPreTrainedModel""",
		    """XCLIPTextModel""",
		    """XCLIPVisionModel""",
		]

if TYPE_CHECKING:
		from .configuration_x_clip import (
		    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    XCLIPConfig,
		    XCLIPTextConfig,
		    XCLIPVisionConfig,
		)
		from .processing_x_clip import XCLIPProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_x_clip import (
				    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XCLIPModel,
				    XCLIPPreTrainedModel,
				    XCLIPTextModel,
				    XCLIPVisionModel,
				)

else:
		import sys

		UpperCAmelCase_						=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









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


UpperCAmelCase_						=logging.get_logger(__name__)


class 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Optional[Any]										="""maskformer-swin"""

				__a							: Optional[int]										={
				    """num_attention_heads""": """num_heads""",
				    """num_hidden_layers""": """num_layers""",
				}



				def __init__( self       , UpperCAmelCase_=2_24       , UpperCAmelCase_=4       , UpperCAmelCase_=3       , UpperCAmelCase_=96       , UpperCAmelCase_=[2, 2, 6, 2]       , UpperCAmelCase_=[3, 6, 12, 24]       , UpperCAmelCase_=7       , UpperCAmelCase_=4.0       , UpperCAmelCase_=True       , UpperCAmelCase_=0.0       , UpperCAmelCase_=0.0       , UpperCAmelCase_=0.1       , UpperCAmelCase_="gelu"       , UpperCAmelCase_=False       , UpperCAmelCase_=0.02       , UpperCAmelCase_=1E-5       , UpperCAmelCase_=None       , UpperCAmelCase_=None       , **UpperCAmelCase_       , ):
											super().__init__(**UpperCAmelCase_						)

											lowerCAmelCase   =		image_size
											lowerCAmelCase   =		patch_size
											lowerCAmelCase   =		num_channels
											lowerCAmelCase   =		embed_dim
											lowerCAmelCase   =		depths
											lowerCAmelCase   =		len(UpperCAmelCase_						)
											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
											# 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(UpperCAmelCase_						) - 1)						)
											lowerCAmelCase   =		['''stem'''] + [F"""stage{idx}""" for idx in range(1       , len(UpperCAmelCase_						) + 1						)]
											lowerCAmelCase   ,     lowerCAmelCase   =		get_aligned_output_features_output_indices(
											    out_features=UpperCAmelCase_       , out_indices=UpperCAmelCase_       , stage_names=self.stage_names						)



