




'''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      A       (						_a	,_a       ):
							lowercase_							     =							'pixel_values'
							lowercase_							     =							False
							lowercase_							     =							TimmBackboneConfig







							def __init__(    self			:    Union[str, Any]				,     lowerCAmelCase_			:    Any				,     **lowerCAmelCase_			:    Tuple   )     ->				List[Any]:


														"""simple docstring"""


														requires_backends(self				,     '''timm'''   )
														super().__init__(lowerCAmelCase_   )
														_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(lowerCAmelCase_				,     '''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(lowerCAmelCase_				,     '''use_pretrained_backbone'''				,     lowerCAmelCase_   )
														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(lowerCAmelCase_				,     '''out_indices'''				,     lowerCAmelCase_   ) is not None else (-1,)

														_a      	=							timm.create_model(
														    config.backbone				,     pretrained=lowerCAmelCase_				,     features_only=config.features_only				,     in_chans=config.num_channels				,     out_indices=lowerCAmelCase_				,     **lowerCAmelCase_				,     )
														# 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(lowerCAmelCase_   ) for i, layer in enumerate(self._backbone.feature_info.info   )}
														super()._init_backbone(lowerCAmelCase_   )







							@classmethod
							def   __lowerCAmelCase     (    cls			:    Any				,     lowerCAmelCase_			:    Tuple				,     *lowerCAmelCase_			:    Union[str, Any]				,     **lowerCAmelCase_			:    Optional[int]   )     ->				Optional[int]:


														"""simple docstring"""


														requires_backends(cls				,     ['''vision''', '''timm''']   )
														from ...models.timm_backbone import TimmBackboneConfig

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

														_a      	=							kwargs.pop('''use_timm_backbone'''				,     lowerCAmelCase_   )
														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=lowerCAmelCase_				,     num_channels=lowerCAmelCase_				,     features_only=lowerCAmelCase_				,     use_pretrained_backbone=lowerCAmelCase_				,     out_indices=lowerCAmelCase_				,     )
														return super()._from_config(lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    int   )     ->				Dict:


														"""simple docstring"""


														pass







							def   __lowerCAmelCase     (    self			:    str				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    str=None				,     lowerCAmelCase_			:    List[Any]=None				,     lowerCAmelCase_			:    List[str]=None				,     **lowerCAmelCase_			:    Any   )     ->				Union[BackboneOutput, Tuple[Tensor, ...]]:


														"""simple docstring"""


														_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(lowerCAmelCase_				,     **lowerCAmelCase_   )
																					_a      	=							self._return_layers
																					_a      	=							tuple(hidden_states[i] for i in self.out_indices   )
														else:
																					_a      	=							self._backbone(lowerCAmelCase_				,     **lowerCAmelCase_   )
																					_a      	=							None

														_a      	=							tuple(lowerCAmelCase_   )
														_a      	=							tuple(lowerCAmelCase_   ) 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=lowerCAmelCase_				,     hidden_states=lowerCAmelCase_				,     attentions=lowerCAmelCase_   )







'''simple docstring'''


import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
    '''split_dict'''					,		[
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''					,		num_bytes=1337					,		num_examples=42					,		dataset_name='''my_dataset''' )} ),
        SplitDict({'''train''': SplitInfo(name='''train'''					,		num_bytes=1337					,		num_examples=42 )} ),
        SplitDict({'''train''': SplitInfo()} ),
    ]					,		)
def 						snake_case_       (UpperCamelCase			:    SplitDict ):





							'''simple docstring'''


							_a      	=							split_dict._to_yaml_list()
							assert len(UpperCamelCase ) == len(UpperCamelCase )
							_a      	=							SplitDict._from_yaml_list(UpperCamelCase )
							for split_name, split_info in split_dict.items():
														# dataset_name field is deprecated, and is therefore not part of the YAML dump
														_a      	=							None
														# the split name of split_dict takes over the name of the split info object
														_a      	=							split_name
							assert split_dict == reloaded

@pytest.mark.parametrize(
    '''split_info'''					,		[SplitInfo(), SplitInfo(dataset_name=UpperCamelCase ), SplitInfo(dataset_name='''my_dataset''' )] )
def 						snake_case_       (UpperCamelCase			:    List[str] ):





							'''simple docstring'''


							_a      	=							asdict(SplitDict({'''train''': split_info} ) )
							assert "dataset_name" in split_dict_asdict["train"]
							assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name

