'''simple docstring'''







from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
		import timm


if is_torch_available():
		from torch import Tensor




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE					):
						A_			:							List[str]     		= 'pixel_values'
						A_			:							Optional[Any]     		= False
						A_			:							Optional[int]     		= TimmBackboneConfig




						def __init__(    self :       Optional[int]							,							UpperCamelCase_ :       Tuple							,							**UpperCamelCase_ :       Optional[Any]      )     ->				List[Any]:
													requires_backends(self							,							'timm'      )
													super().__init__(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = config

													if config.backbone is None:
																				raise ValueError('backbone is not set in the config. Please set it to a timm model name.'      )

													if config.backbone not in timm.list_models():
																				raise ValueError(f'''backbone {config.backbone} is not supported by timm.'''      )

													if hasattr(UpperCamelCase_							,							'out_features'      ) and config.out_features is not None:
																				raise ValueError('out_features is not supported by TimmBackbone. Please use out_indices instead.'      )

													SCREAMING_SNAKE_CASE__     :int						    = getattr(UpperCamelCase_							,							'use_pretrained_backbone'							,							UpperCamelCase_      )
													if pretrained is None:
																				raise ValueError('use_pretrained_backbone is not set in the config. Please set it to True or False.'      )

													# We just take the final layer by default. This matches the default for the transformers models.
													SCREAMING_SNAKE_CASE__     :List[Any]						    = config.out_indices if getattr(UpperCamelCase_							,							'out_indices'							,							UpperCamelCase_      ) is not None else (-1,)

													SCREAMING_SNAKE_CASE__     :List[Any]						    = timm.create_model(
													    config.backbone							,							pretrained=UpperCamelCase_							,							features_only=config.features_only							,							in_chans=config.num_channels							,							out_indices=UpperCamelCase_							,							**UpperCamelCase_							,							)
													# These are used to control the output of the model when called. If output_hidden_states is True, then
													# return_layers is modified to include all layers.
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self._backbone.return_layers
													SCREAMING_SNAKE_CASE__     :Any						    = {layer['module']: str(UpperCamelCase_      ) for i, layer in enumerate(self._backbone.feature_info.info      )}
													super()._init_backbone(UpperCamelCase_      )




						@classmethod
						def      __lowerCamelCase       (    cls :       List[Any]							,							UpperCamelCase_ :       int							,							*UpperCamelCase_ :       str							,							**UpperCamelCase_ :       List[Any]      )     ->				str:
													requires_backends(cls							,							['vision', 'timm']      )
													from ...models.timm_backbone import TimmBackboneConfig

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = kwargs.pop('config'							,							TimmBackboneConfig()      )

													SCREAMING_SNAKE_CASE__     :Dict						    = kwargs.pop('use_timm_backbone'							,							UpperCamelCase_      )
													if not use_timm:
																				raise ValueError('use_timm_backbone must be True for timm backbones'      )

													SCREAMING_SNAKE_CASE__     :int						    = kwargs.pop('num_channels'							,							config.num_channels      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = kwargs.pop('features_only'							,							config.features_only      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = kwargs.pop('use_pretrained_backbone'							,							config.use_pretrained_backbone      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = kwargs.pop('out_indices'							,							config.out_indices      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = TimmBackboneConfig(
													    backbone=UpperCamelCase_							,							num_channels=UpperCamelCase_							,							features_only=UpperCamelCase_							,							use_pretrained_backbone=UpperCamelCase_							,							out_indices=UpperCamelCase_							,							)
													return super()._from_config(UpperCamelCase_							,							**UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Optional[Any]							,							UpperCamelCase_ :       Optional[int]      )     ->				int:
													pass




						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       Optional[Any]							,							UpperCamelCase_ :       Tuple=None							,							UpperCamelCase_ :       Tuple=None							,							UpperCamelCase_ :       Optional[int]=None							,							**UpperCamelCase_ :       List[str]      )     ->				Union[BackboneOutput, Tuple[Tensor, ...]]:
													SCREAMING_SNAKE_CASE__     :Tuple						    = return_dict if return_dict is not None else self.config.use_return_dict
													SCREAMING_SNAKE_CASE__     :str						    = (
													    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
													)
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = output_attentions if output_attentions is not None else self.config.output_attentions

													if output_attentions:
																				raise ValueError('Cannot output attentions for timm backbones at the moment'      )

													if output_hidden_states:
																				# We modify the return layers to include all the stages of the backbone
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = self._all_layers
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = self._backbone(UpperCamelCase_							,							**UpperCamelCase_      )
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = self._return_layers
																				SCREAMING_SNAKE_CASE__     :int						    = tuple(hidden_states[i] for i in self.out_indices      )
													else:
																				SCREAMING_SNAKE_CASE__     :Tuple						    = self._backbone(UpperCamelCase_							,							**UpperCamelCase_      )
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = None

													SCREAMING_SNAKE_CASE__     :str						    = tuple(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = tuple(UpperCamelCase_      ) if hidden_states is not None else None

													if not return_dict:
																				SCREAMING_SNAKE_CASE__     :int						    = (feature_maps,)
																				if output_hidden_states:
																											SCREAMING_SNAKE_CASE__     :str						    = output + (hidden_states,)
																				return output

													return BackboneOutput(feature_maps=UpperCamelCase_							,							hidden_states=UpperCamelCase_							,							attentions=UpperCamelCase_      )







'''simple docstring'''







from typing import TYPE_CHECKING

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


UpperCamelCase_      	=	{
    '''configuration_m2m_100''': ['''M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''M2M100Config''', '''M2M100OnnxConfig'''],
    '''tokenization_m2m_100''': ['''M2M100Tokenizer'''],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''M2M100ForConditionalGeneration''',
		    '''M2M100Model''',
		    '''M2M100PreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
		from .tokenization_mam_aaa import MaMaaaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mam_aaa import (
				    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MaMaaaForConditionalGeneration,
				    MaMaaaModel,
				    MaMaaaPreTrainedModel,
				)


else:
		import sys

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






