



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        lowerCAmelCase__					(  _lowerCamelCase ,				_lowerCamelCase       ):
				A_   :     Optional[int]								=     'pixel_values'
				A_   :     List[str]								=     False
				A_   :     Optional[int]								=     TimmBackboneConfig


				def __init__(							self      :  List[str]						, __UpperCamelCase      :  int						, **__UpperCamelCase      :  int       )		->     Any:
											requires_backends(self						, 'timm'       )
											super().__init__(__UpperCamelCase       )
											A      						=    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.'       )

											A      						=    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.
											A      						=    config.out_indices if getattr(__UpperCamelCase						, 'out_indices'						, __UpperCamelCase       ) is not None else (-1,)

											A      						=    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.
											A      						=    self._backbone.return_layers
											A      						=    {layer['module']: str(__UpperCamelCase       ) for i, layer in enumerate(self._backbone.feature_info.info       )}
											super()._init_backbone(__UpperCamelCase       )


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

											A      						=    kwargs.pop('config'						, TimmBackboneConfig()       )

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

											A      						=    kwargs.pop('num_channels'						, config.num_channels       )
											A      						=    kwargs.pop('features_only'						, config.features_only       )
											A      						=    kwargs.pop('use_pretrained_backbone'						, config.use_pretrained_backbone       )
											A      						=    kwargs.pop('out_indices'						, config.out_indices       )
											A      						=    TimmBackboneConfig(
											    backbone=__UpperCamelCase						, num_channels=__UpperCamelCase						, features_only=__UpperCamelCase						, use_pretrained_backbone=__UpperCamelCase						, out_indices=__UpperCamelCase						, )
											return super()._from_config(__UpperCamelCase						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[Any]						, __UpperCamelCase      :  Optional[int]       )		->     Optional[Any]:
											pass


				def __UpperCamelCase						(							self      :  Union[str, Any]						, __UpperCamelCase      :  int						, __UpperCamelCase      :  List[str]=None						, __UpperCamelCase      :  Optional[Any]=None						, __UpperCamelCase      :  Tuple=None						, **__UpperCamelCase      :  List[str]       )		->     Union[BackboneOutput, Tuple[Tensor, ...]]:
											A      						=    return_dict if return_dict is not None else self.config.use_return_dict
											A      						=    (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											A      						=    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
																		A      						=    self._all_layers
																		A      						=    self._backbone(__UpperCamelCase						, **__UpperCamelCase       )
																		A      						=    self._return_layers
																		A      						=    tuple(hidden_states[i] for i in self.out_indices       )
											else:
																		A      						=    self._backbone(__UpperCamelCase						, **__UpperCamelCase       )
																		A      						=    None

											A      						=    tuple(__UpperCamelCase       )
											A      						=    tuple(__UpperCamelCase       ) if hidden_states is not None else None

											if not return_dict:
																		A      						=    (feature_maps,)
																		if output_hidden_states:
																									A      						=    output + (hidden_states,)
																		return output

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




from datetime import datetime

import matplotlib.pyplot as plt
import torch

def        __a			(   __lowerCAmelCase   )     ->     int:
					for param in module.parameters():
										SCREAMING_SNAKE_CASE							:     List[Any]						=				False

def        __a			(   )     ->     List[str]:
					SCREAMING_SNAKE_CASE							:     List[Any]						=				'cuda' if torch.cuda.is_available() else 'cpu'
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										SCREAMING_SNAKE_CASE							:     List[str]						=				'mps'
					if device == "mps":
										print(
										    'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
										    ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
										    ' with generations.'   )
					return device

def        __a			(   __lowerCAmelCase   )     ->     List[Any]:
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				plt.imshow(__lowerCAmelCase   )
					fig.axes.get_xaxis().set_visible(__lowerCAmelCase   )
					fig.axes.get_yaxis().set_visible(__lowerCAmelCase   )
					plt.show()



def        __a			(   )     ->     Optional[Any]:
					SCREAMING_SNAKE_CASE							:     str						=				datetime.now()
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				current_time.strftime('%H:%M:%S'   )
					return timestamp