

"""simple docstring"""





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


__UpperCamelCase  :      Union[str, Any]							      =		logging.get_logger(__name__)

__UpperCamelCase  :      List[str]							      =		{
    '''microsoft/resnet-50''': '''https://huggingface.co/microsoft/resnet-50/blob/main/config.json''',
}







class 				SCREAMING_SNAKE_CASE					(		a_    ,							a_       ):






					"""simple docstring"""



					lowercase__				 =       "resnet"
					lowercase__				 =       ["basic", "bottleneck"]







					def __init__(       self			:				str   ,lowercase_			:				Optional[Any]=3   ,lowercase_			:				List[Any]=6_4   ,lowercase_			:				Optional[int]=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]   ,lowercase_			:				str=[3, 4, 6, 3]   ,lowercase_			:				str="bottleneck"   ,lowercase_			:				List[Any]="relu"   ,lowercase_			:				Dict=False   ,lowercase_			:				List[str]=None   ,lowercase_			:				Tuple=None   ,**lowercase_			:				List[str]   ,):
						super().__init__(**lowercase_		)
						if layer_type not in self.layer_types:
							raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types		)}'		)
						lowerCAmelCase__	:						Tuple  				=	num_channels
						lowerCAmelCase__	:						str  				=	embedding_size
						lowerCAmelCase__	:						str  				=	hidden_sizes
						lowerCAmelCase__	:						Dict  				=	depths
						lowerCAmelCase__	:						Optional[Any]  				=	layer_type
						lowerCAmelCase__	:						Optional[int]  				=	hidden_act
						lowerCAmelCase__	:						Union[str, Any]  				=	downsample_in_first_stage
						lowerCAmelCase__	:						str  				=	['''stem'''] + [F'stage{idx}' for idx in range(1   ,len(lowercase_		) + 1		)]
						lowerCAmelCase__				,lowerCAmelCase__	:						List[str]  				=	get_aligned_output_features_output_indices(
						    out_features=lowercase_   ,out_indices=lowercase_   ,stage_names=self.stage_names		)







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       version.parse("1.11"       )







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







					@property
					def        __lowerCAmelCase (       self			:				Dict		):
						return 1E-3







"""simple docstring"""





from typing import TYPE_CHECKING

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


__UpperCamelCase  :      str							      =		{
    '''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      str							      =		[
							    '''LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LongT5EncoderModel''',
							    '''LongT5ForConditionalGeneration''',
							    '''LongT5Model''',
							    '''LongT5PreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      int							      =		[
							    '''FlaxLongT5ForConditionalGeneration''',
							    '''FlaxLongT5Model''',
							    '''FlaxLongT5PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_longta import (
														    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LongTaEncoderModel,
														    LongTaForConditionalGeneration,
														    LongTaModel,
														    LongTaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_longta import (
														    FlaxLongTaForConditionalGeneration,
														    FlaxLongTaModel,
														    FlaxLongTaPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase  :      int							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




