
from typing import TYPE_CHECKING

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


_A  =	{
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A  =	[
			    "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "ResNetForImageClassification",
			    "ResNetModel",
			    "ResNetPreTrainedModel",
			    "ResNetBackbone",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A  =	[
			    "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFResNetForImageClassification",
			    "TFResNetModel",
			    "TFResNetPreTrainedModel",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A  =	[
			    "FlaxResNetForImageClassification",
			    "FlaxResNetModel",
			    "FlaxResNetPreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_resnet import (
						    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ResNetBackbone,
						    ResNetForImageClassification,
						    ResNetModel,
						    ResNetPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_resnet import (
						    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFResNetForImageClassification,
						    TFResNetModel,
						    TFResNetPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
			import sys

			_A  =	_LazyModule(__name__, globals()["__file__"], _import_structure)





from math import ceil






def   lowerCamelCase__							(							__lowerCAmelCase    :    Dict       ,				__lowerCAmelCase    :    int							):




			"""simple docstring"""


			lowerCAmelCase_         =      list(range(0       ,				__lowerCAmelCase							)							)

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

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

			if len(__lowerCAmelCase							) != 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(__lowerCAmelCase							)							)
			if len(__lowerCAmelCase							) != 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(__lowerCAmelCase							)							)
			if len(__lowerCAmelCase							) != 0:
						raise ValueError(
						    "The device_map contains more attention blocks than this model has. Remove these from the device_map:"
						    + str(__lowerCAmelCase							)							)






def   lowerCamelCase__							(							__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Tuple							):




			"""simple docstring"""


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

			return dict(zip(__lowerCAmelCase       ,				__lowerCAmelCase							)							)



