





from math import ceil





def 						lowerCamelCase__     (     UpperCamelCase__			:       Optional[Any]       ,    UpperCamelCase__			:       List[Any]						)			-> Any:



					'''simple docstring'''




					_snake_case									=				list(range(0       ,    UpperCamelCase__						)						)

					_snake_case									=				[item for sublist in list(device_map.values()						) for item in sublist]

					# Duplicate check
					_snake_case									=				[]
					for i in device_map_blocks:
										if device_map_blocks.count(UpperCamelCase__						) > 1 and i not in duplicate_blocks:
															duplicate_blocks.append(UpperCamelCase__						)
    # Missing blocks
					_snake_case									=				[i for i in blocks if i not in device_map_blocks]
					_snake_case									=				[i for i in device_map_blocks if i not in blocks]

					if len(UpperCamelCase__						) != 0:
										raise ValueError(
										    'Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'
										    ' These attention blocks were specified more than once: ' + str(UpperCamelCase__						)						)
					if len(UpperCamelCase__						) != 0:
										raise ValueError(
										    'There are attention blocks for this model that are not specified in the device_map. Add these attention '
										    'blocks to a device on the device_map: ' + str(UpperCamelCase__						)						)
					if len(UpperCamelCase__						) != 0:
										raise ValueError(
										    'The device_map contains more attention blocks than this model has. Remove these from the device_map:'
										    + str(UpperCamelCase__						)						)





def 						lowerCamelCase__     (     UpperCamelCase__			:       List[str]       ,    UpperCamelCase__			:       List[Any]						)			-> Dict:



					'''simple docstring'''




					_snake_case									=				list(range(UpperCamelCase__						)						)
					_snake_case									=				int(ceil(n_layers / len(UpperCamelCase__						)						)						)
					_snake_case									=				[layers[i : i + n_blocks] for i in range(0       ,    UpperCamelCase__       ,    UpperCamelCase__						)]

					return dict(zip(UpperCamelCase__       ,    UpperCamelCase__						)						)










from typing import TYPE_CHECKING

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


UpperCAmelCase_      							=     {"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase_      							=     [
					    """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """SwinForImageClassification""",
					    """SwinForMaskedImageModeling""",
					    """SwinModel""",
					    """SwinPreTrainedModel""",
					    """SwinBackbone""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase_      							=     [
					    """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFSwinForImageClassification""",
					    """TFSwinForMaskedImageModeling""",
					    """TFSwinModel""",
					    """TFSwinPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_swin import (
										    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SwinBackbone,
										    SwinForImageClassification,
										    SwinForMaskedImageModeling,
										    SwinModel,
										    SwinPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_swin import (
										    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFSwinForImageClassification,
										    TFSwinForMaskedImageModeling,
										    TFSwinModel,
										    TFSwinPreTrainedModel,
										)

else:
					import sys

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



