






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

class        snake_case_				(							enum.Enum   ):







   """simple docstring"""



   __lowerCAmelCase  =    "timm"
   __lowerCAmelCase  =    "transformers"






def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase   ):


    '''simple docstring'''



    if stage_names is None:
        raise ValueError('Stage_names must be set for transformers backbones'   )

    if out_features is not None:
        if not isinstance(_lowercase,   (list,)   ):
            raise ValueError(f'''out_features must be a list {type(_lowercase   )}'''   )
        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(_lowercase,   (list, tuple)   ):
            raise ValueError(f'''out_indices must be a list or tuple, got {type(_lowercase   )}'''   )
        if any(idx >= len(_lowercase   ) 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(_lowercase   ) != len(_lowercase   ):
            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 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase,   ):


    '''simple docstring'''



    if out_indices is None and out_features is None:
        _lowercase     			=						[len(_lowercase   ) - 1]
        _lowercase     			=						[stage_names[-1]]
    elif out_indices is None and out_features is not None:
        _lowercase     			=						[stage_names.index(_lowercase   ) for layer in out_features]
    elif out_features is None and out_indices is not None:
        _lowercase     			=						[stage_names[idx] for idx in out_indices]
    return out_features, out_indices
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase,   ):


    '''simple docstring'''



    verify_out_features_out_indices(out_features=_lowercase,   out_indices=_lowercase,   stage_names=_lowercase   )
    _lowercase     			=						_align_output_features_output_indices(
        out_features=_lowercase,   out_indices=_lowercase,   stage_names=_lowercase   )
    # Verify that the aligned out_features and out_indices are valid
    verify_out_features_out_indices(out_features=_lowercase,   out_indices=_lowercase,   stage_names=_lowercase   )
    return output_features, output_indices

class        snake_case_				:







   """simple docstring"""



   __lowerCAmelCase  =    None

   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any] ,							snake_case__:						Optional[int]						)	->		None:


       """simple docstring"""



       if getattr(self ,							'_backbone' ,							lowerCamelCase__						) 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']
       _lowercase     			=						[stage['''module'''] for stage in self._backbone.feature_info.info]
       _lowercase     			=						[stage['''num_chs'''] for stage in self._backbone.feature_info.info]
       _lowercase     			=						self._backbone.feature_info.out_indices
       _lowercase     			=						self._backbone.feature_info.module_name()

       # We verify the out indices and out features are valid
       verify_out_features_out_indices(
           out_features=lowerCamelCase__ ,							out_indices=lowerCamelCase__ ,							stage_names=self.stage_names						)
       _lowercase     			=						out_features, out_indices

   def       SCREAMING_SNAKE_CASE__					(     self:						Tuple ,							snake_case__:						Union[str, Any]						)	->		None:


       """simple docstring"""



       _lowercase     			=						getattr(lowerCamelCase__ ,							'stage_names'						)
       _lowercase     			=						getattr(lowerCamelCase__ ,							'out_features' ,							lowerCamelCase__						)
       _lowercase     			=						getattr(lowerCamelCase__ ,							'out_indices' ,							lowerCamelCase__						)

       _lowercase     			=						stage_names
       _lowercase     			=						get_aligned_output_features_output_indices(
           out_features=lowerCamelCase__ ,							out_indices=lowerCamelCase__ ,							stage_names=lowerCamelCase__						)
       # Number of channels for each stage. This is set in the transformer backbone model init
       _lowercase     			=						None

   def       SCREAMING_SNAKE_CASE__					(     self:						Tuple ,							snake_case__:						Tuple						)	->		None:


       """simple docstring"""



       _lowercase     			=						config

       _lowercase     			=						getattr(lowerCamelCase__ ,							'use_timm_backbone' ,							lowerCamelCase__						)
       _lowercase     			=						BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

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

   @property
   def       SCREAMING_SNAKE_CASE__					(     self:						Dict						)	->		Optional[Any]:


       """simple docstring"""



       return self._out_features

   @out_features.setter
   def       SCREAMING_SNAKE_CASE__					(     self:						str ,							snake_case__:						List[str]						)	->		int:


       """simple docstring"""



       _lowercase     			=						get_aligned_output_features_output_indices(
           out_features=lowerCamelCase__ ,							out_indices=lowerCamelCase__ ,							stage_names=self.stage_names						)

   @property
   def       SCREAMING_SNAKE_CASE__					(     self:						Dict						)	->		Tuple:


       """simple docstring"""



       return self._out_indices

   @out_indices.setter
   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any] ,							snake_case__:						Union[Tuple[int], List[int]]						)	->		Tuple:


       """simple docstring"""



       _lowercase     			=						get_aligned_output_features_output_indices(
           out_features=lowerCamelCase__ ,							out_indices=lowerCamelCase__ ,							stage_names=self.stage_names						)

   @property
   def       SCREAMING_SNAKE_CASE__					(     self:						List[str]						)	->		Tuple:


       """simple docstring"""



       return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names						)}

   @property
   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int]						)	->		int:


       """simple docstring"""



       return [self.out_feature_channels[name] for name in self.out_features]

   def       SCREAMING_SNAKE_CASE__					(     self:						Tuple ,							*snake_case__:						Tuple ,							**snake_case__:						Optional[int]						)	->		Optional[int]:


       """simple docstring"""



       _lowercase     			=						dict(inspect.signature(self.forward						).parameters						)
       _lowercase     			=						{k: v for k, v in kwargs.items() if k in signature}
       return self(*lowerCamelCase__ ,							**lowerCamelCase__						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[Any] ,							snake_case__:						List[str] ,							snake_case__:						Optional[bool] = None ,							snake_case__:						Optional[bool] = None ,							snake_case__:						Optional[bool] = None ,							)	->		Dict:


       """simple docstring"""



       raise NotImplementedError('This method should be implemented by the derived class.'						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[Any]						)	->		Optional[int]:


       """simple docstring"""



       _lowercase     			=						super().to_dict()
       _lowercase     			=						output.pop('_out_features'						)
       _lowercase     			=						output.pop('_out_indices'						)
       return output






class        snake_case_				:







   """simple docstring"""



   @property
   def       SCREAMING_SNAKE_CASE__					(     self:						Dict						)	->		Tuple:


       """simple docstring"""



       return self._out_features

   @out_features.setter
   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int] ,							snake_case__:						List[str]						)	->		int:


       """simple docstring"""



       _lowercase     			=						get_aligned_output_features_output_indices(
           out_features=lowerCamelCase__ ,							out_indices=lowerCamelCase__ ,							stage_names=self.stage_names						)

   @property
   def       SCREAMING_SNAKE_CASE__					(     self:						Dict						)	->		Union[str, Any]:


       """simple docstring"""



       return self._out_indices

   @out_indices.setter
   def       SCREAMING_SNAKE_CASE__					(     self:						str ,							snake_case__:						Union[Tuple[int], List[int]]						)	->		str:


       """simple docstring"""



       _lowercase     			=						get_aligned_output_features_output_indices(
           out_features=lowerCamelCase__ ,							out_indices=lowerCamelCase__ ,							stage_names=self.stage_names						)

   def       SCREAMING_SNAKE_CASE__					(     self:						int						)	->		Tuple:


       """simple docstring"""



       _lowercase     			=						super().to_dict()
       _lowercase     			=						output.pop('_out_features'						)
       _lowercase     			=						output.pop('_out_indices'						)
       return output




import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir

@dataclass
class    __magic_name__						:
     A:		str



class    __magic_name__						(  unittest.TestCase):


     def   UpperCAmelCase__						(   self				:  Any      )	->   List[Any]:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[Any] 							= get_tests_dir('''fixtures/test_sentencepiece.model'''      )
      UpperCamelCase__       :     Any 							= get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model'''      )

      UpperCamelCase__       :     List[str] 							= FakeOriginalTokenizer(vocab_file=lowerCamelCase__      )

      with warnings.catch_warnings(record=lowerCamelCase__      ) as w:
       UpperCamelCase__       :     Optional[int] 							= SpmConverter(lowerCamelCase__      )
      self.assertEqual(len(lowerCamelCase__      )				,						0      )

      UpperCamelCase__       :     Tuple 							= FakeOriginalTokenizer(vocab_file=lowerCamelCase__      )

      with warnings.catch_warnings(record=lowerCamelCase__      ) as w:
       UpperCamelCase__       :     str 							= SpmConverter(lowerCamelCase__      )
      self.assertEqual(len(lowerCamelCase__      )				,						1      )

      self.assertIn(
          '''The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'''
          ''' which is not implemented in the fast tokenizers.'''				,						str(w[0].message      )				,						)


