




import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__ :   List[Any]						=     []

       SCREAMING_SNAKE_CASE__ :   Optional[Any]						=     os.path.join('tests', 'models', '**', 'test_modeling_*.py')
       SCREAMING_SNAKE_CASE__ :   Optional[int]						=     glob.glob(pattern)
       # TODO: deal with TF/Flax too
       SCREAMING_SNAKE_CASE__ :   Dict						=     [
           x for x in test_files if not (x.startswith('test_modeling_tf_') or x.startswith('test_modeling_flax_'))
       ]

       for test_file in test_files:
              SCREAMING_SNAKE_CASE__ :   Tuple						=     get_tester_classes(test_file)
              for tester_class in tester_classes:
                     # A few tester classes don't have `parent` parameter in `__init__`.
                     # TODO: deal this better
                     try:
                            SCREAMING_SNAKE_CASE__ :   Union[str, Any]						=     tester_class(parent=None)
                     except Exception:
                            continue
                     if hasattr(tester, 'get_config'):
                            SCREAMING_SNAKE_CASE__ :   Union[str, Any]						=     tester.get_config()
                            for k, v in config.to_dict().items():
                                   if isinstance(v, int):
                                          SCREAMING_SNAKE_CASE__ :   int						=     None
                                          if k in ["vocab_size"]:
                                                 SCREAMING_SNAKE_CASE__ :   Optional[int]						=     100
                                          elif k in ["max_position_embeddings"]:
                                                 SCREAMING_SNAKE_CASE__ :   Union[str, Any]						=     128
                                          elif k in ["hidden_size", "d_model"]:
                                                 SCREAMING_SNAKE_CASE__ :   Optional[Any]						=     40
                                          elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
                                                 SCREAMING_SNAKE_CASE__ :   Dict						=     5
                                          if target is not None and v > target:
                                                 failures.append(
                                                     f'''{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'''
                                                     f''' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'''
                                                     f''' than {target}.'''
                                                 )

       if len(failures) > 0:
              raise Exception(f'''There were {len(failures)} failures:\n''' + '\n'.join(failures))












import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union




class 	UpperCamelCase__			(enum.Enum  ):

    '''simple docstring'''



    lowerCamelCase_   :			Tuple			  =   """timm"""
    lowerCamelCase_   :			Union[str, Any]			  =   """transformers"""




def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)			->				Optional[int]:
       if stage_names is None:
              raise ValueError("Stage_names must be set for transformers backbones"					)

       if out_features is not None:
              if not isinstance(_SCREAMING_SNAKE_CASE				,(list,)					):
                     raise ValueError(f'''out_features must be a list {type(_SCREAMING_SNAKE_CASE					)}'''					)
              if any(feat not in stage_names for feat in out_features					):
                     raise ValueError(f'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''					)

       if out_indices is not None:
              if not isinstance(_SCREAMING_SNAKE_CASE				,(list, tuple)					):
                     raise ValueError(f'''out_indices must be a list or tuple, got {type(_SCREAMING_SNAKE_CASE					)}'''					)
              if any(idx >= len(_SCREAMING_SNAKE_CASE					) for idx in out_indices					):
                     raise ValueError("out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"					)

       if out_features is not None and out_indices is not None:
              if len(_SCREAMING_SNAKE_CASE					) != len(_SCREAMING_SNAKE_CASE					):
                     raise ValueError("out_features and out_indices should have the same length if both are set"					)
              if out_features != [stage_names[idx] for idx in out_indices]:
                     raise ValueError("out_features and out_indices should correspond to the same stages if both are set"					)

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,)			->				Optional[Any]:
       if out_indices is None and out_features is None:
              lowerCamelCase     :   Dict    				=      [len(_SCREAMING_SNAKE_CASE					) - 1]
              lowerCamelCase     :   Tuple    				=      [stage_names[-1]]
       elif out_indices is None and out_features is not None:
              lowerCamelCase     :   List[Any]    				=      [stage_names.index(_SCREAMING_SNAKE_CASE					) for layer in out_features]
       elif out_features is None and out_indices is not None:
              lowerCamelCase     :   Union[str, Any]    				=      [stage_names[idx] for idx in out_indices]
       return out_features, out_indices

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,)			->				Tuple[List[str], List[int]]:
       verify_out_features_out_indices(out_features=_SCREAMING_SNAKE_CASE				,out_indices=_SCREAMING_SNAKE_CASE				,stage_names=_SCREAMING_SNAKE_CASE					)
       lowerCamelCase				,   lowerCamelCase     :   str    				=      _align_output_features_output_indices(
           out_features=_SCREAMING_SNAKE_CASE				,out_indices=_SCREAMING_SNAKE_CASE				,stage_names=_SCREAMING_SNAKE_CASE					)
       # Verify that the aligned out_features and out_indices are valid
       verify_out_features_out_indices(out_features=_SCREAMING_SNAKE_CASE				,out_indices=_SCREAMING_SNAKE_CASE				,stage_names=_SCREAMING_SNAKE_CASE					)
       return output_features, output_indices




class 	UpperCamelCase__			:

    '''simple docstring'''



    lowerCamelCase_   :			Optional[BackboneType]			  =   None







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   None:
           if getattr(self  ,						"_backbone"  ,						UpperCamelCase__  ) is None:
                  raise ValueError("self._backbone must be set before calling _init_timm_backbone"  )

           # These will diagree with the defaults for the transformers models e.g. for resnet50
           # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
           # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
           lowerCamelCase     :   Any    				=      [stage["module"] for stage in self._backbone.feature_info.info]
           lowerCamelCase     :   Optional[int]    				=      [stage["num_chs"] for stage in self._backbone.feature_info.info]
           lowerCamelCase     :   str    				=      self._backbone.feature_info.out_indices
           lowerCamelCase     :   Any    				=      self._backbone.feature_info.module_name()

           # We verify the out indices and out features are valid
           verify_out_features_out_indices(
               out_features=UpperCamelCase__  ,						out_indices=UpperCamelCase__  ,						stage_names=self.stage_names  )
           lowerCamelCase				,   lowerCamelCase     :   Tuple    				=      out_features, out_indices







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   None:
           lowerCamelCase     :   Optional[int]    				=      getattr(UpperCamelCase__  ,						"stage_names"  )
           lowerCamelCase     :   Optional[int]    				=      getattr(UpperCamelCase__  ,						"out_features"  ,						UpperCamelCase__  )
           lowerCamelCase     :   Union[str, Any]    				=      getattr(UpperCamelCase__  ,						"out_indices"  ,						UpperCamelCase__  )

           lowerCamelCase     :   List[Any]    				=      stage_names
           lowerCamelCase				,   lowerCamelCase     :   Dict    				=      get_aligned_output_features_output_indices(
               out_features=UpperCamelCase__  ,						out_indices=UpperCamelCase__  ,						stage_names=UpperCamelCase__  )
           # Number of channels for each stage. This is set in the transformer backbone model init
           lowerCamelCase     :   Tuple    				=      None







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   None:
           lowerCamelCase     :   Any    				=      config

           lowerCamelCase     :   Any    				=      getattr(UpperCamelCase__  ,						"use_timm_backbone"  ,						UpperCamelCase__  )
           lowerCamelCase     :   int    				=      BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

           if self.backbone_type == BackboneType.TIMM:
                  self._init_timm_backbone(UpperCamelCase__  )
           elif self.backbone_type == BackboneType.TRANSFORMERS:
                  self._init_transformers_backbone(UpperCamelCase__  )
           else:
                  raise ValueError(F'''backbone_type {self.backbone_type} not supported.'''  )







    @property
    def 					_lowercase  (					self  )  ->   Optional[Any]:
           return self._out_features







    @out_features.setter
    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   Union[str, Any]:
           lowerCamelCase				,   lowerCamelCase     :   Dict    				=      get_aligned_output_features_output_indices(
               out_features=UpperCamelCase__  ,						out_indices=UpperCamelCase__  ,						stage_names=self.stage_names  )







    @property
    def 					_lowercase  (					self  )  ->   Optional[Any]:
           return self._out_indices







    @out_indices.setter
    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   List[str]:
           lowerCamelCase				,   lowerCamelCase     :   int    				=      get_aligned_output_features_output_indices(
               out_features=UpperCamelCase__  ,						out_indices=UpperCamelCase__  ,						stage_names=self.stage_names  )







    @property
    def 					_lowercase  (					self  )  ->   Tuple:
           # the current backbones will output the number of channels for each stage
           # even if that stage is not in the out_features list.
           return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names  )}







    @property
    def 					_lowercase  (					self  )  ->   List[Any]:
           return [self.out_feature_channels[name] for name in self.out_features]







    def 					_lowercase  (					self  ,						*UpperCamelCase__  ,						**UpperCamelCase__  )  ->   Tuple:
           lowerCamelCase     :   Union[str, Any]    				=      dict(inspect.signature(self.forward  ).parameters  )
           lowerCamelCase     :   Optional[int]    				=      {k: v for k, v in kwargs.items() if k in signature}
           return self(*UpperCamelCase__  ,						**UpperCamelCase__  )







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__ = None  ,						UpperCamelCase__ = None  ,						UpperCamelCase__ = None  ,						)  ->   int:
           raise NotImplementedError("This method should be implemented by the derived class."  )







    def 					_lowercase  (					self  )  ->   Dict:
           lowerCamelCase     :   List[Any]    				=      super().to_dict()
           lowerCamelCase     :   Optional[int]    				=      output.pop("_out_features"  )
           lowerCamelCase     :   Dict    				=      output.pop("_out_indices"  )
           return output




class 	UpperCamelCase__			:

    '''simple docstring'''



    @property
    def 					_lowercase  (					self  )  ->   int:
           return self._out_features







    @out_features.setter
    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   Optional[Any]:
           lowerCamelCase				,   lowerCamelCase     :   List[Any]    				=      get_aligned_output_features_output_indices(
               out_features=UpperCamelCase__  ,						out_indices=UpperCamelCase__  ,						stage_names=self.stage_names  )







    @property
    def 					_lowercase  (					self  )  ->   Optional[Any]:
           return self._out_indices







    @out_indices.setter
    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   int:
           lowerCamelCase				,   lowerCamelCase     :   Optional[Any]    				=      get_aligned_output_features_output_indices(
               out_features=UpperCamelCase__  ,						out_indices=UpperCamelCase__  ,						stage_names=self.stage_names  )







    def 					_lowercase  (					self  )  ->   Any:
           lowerCamelCase     :   str    				=      super().to_dict()
           lowerCamelCase     :   Union[str, Any]    				=      output.pop("_out_features"  )
           lowerCamelCase     :   Optional[int]    				=      output.pop("_out_indices"  )
           return output






