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







class       __snake_case (  enum.Enum ):
 __lowerCamelCase     : List[Any] 	=   """timm"""
 __lowerCamelCase     : Dict 	=   """transformers"""





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	int:






      '''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(__lowerCAmelCase		,       (list,)    ):
                  raise ValueError(f'''out_features must be a list {type(__lowerCAmelCase    )}'''    )
            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(__lowerCAmelCase		,       (list, tuple)    ):
                  raise ValueError(f'''out_indices must be a list or tuple, got {type(__lowerCAmelCase    )}'''    )
            if any(idx >= len(__lowerCAmelCase    ) 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(__lowerCAmelCase    ) != len(__lowerCAmelCase    ):
                  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 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       )->	List[Any]:






      '''simple docstring'''





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





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       )->	Tuple[List[str], List[int]]:






      '''simple docstring'''





      verify_out_features_out_indices(out_features=__lowerCAmelCase		,       out_indices=__lowerCAmelCase		,       stage_names=__lowerCAmelCase    )
      UpperCAmelCase	,    UpperCAmelCase     :			List[Any]     	=_align_output_features_output_indices(
          out_features=__lowerCAmelCase		,       out_indices=__lowerCAmelCase		,       stage_names=__lowerCAmelCase    )
      # Verify that the aligned out_features and out_indices are valid
      verify_out_features_out_indices(out_features=__lowerCAmelCase		,       out_indices=__lowerCAmelCase		,       stage_names=__lowerCAmelCase    )
      return output_features, output_indices







class       __snake_case :
 __lowerCamelCase     : Optional[BackboneType] 	=   None
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				None:
       '''simple docstring'''





       if getattr(self		, '''_backbone'''		, snake_case__    ) 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']
       UpperCAmelCase     :			Union[str, Any]     	=[stage['''module'''] for stage in self._backbone.feature_info.info]
       UpperCAmelCase     :			str     	=[stage['''num_chs'''] for stage in self._backbone.feature_info.info]
       UpperCAmelCase     :			List[Any]     	=self._backbone.feature_info.out_indices
       UpperCAmelCase     :			Optional[Any]     	=self._backbone.feature_info.module_name()

       # We verify the out indices and out features are valid
       verify_out_features_out_indices(
           out_features=snake_case__		, out_indices=snake_case__		, stage_names=self.stage_names    )
       UpperCAmelCase	,    UpperCAmelCase     :			Optional[int]     	=out_features, out_indices
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				None:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=getattr(snake_case__		, '''stage_names'''    )
       UpperCAmelCase     :			int     	=getattr(snake_case__		, '''out_features'''		, snake_case__    )
       UpperCAmelCase     :			Any     	=getattr(snake_case__		, '''out_indices'''		, snake_case__    )

       UpperCAmelCase     :			List[Any]     	=stage_names
       UpperCAmelCase	,    UpperCAmelCase     :			str     	=get_aligned_output_features_output_indices(
           out_features=snake_case__		, out_indices=snake_case__		, stage_names=snake_case__    )
       # Number of channels for each stage. This is set in the transformer backbone model init
       UpperCAmelCase     :			List[Any]     	=None
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				None:
       '''simple docstring'''





       UpperCAmelCase     :			List[str]     	=config

       UpperCAmelCase     :			int     	=getattr(snake_case__		, '''use_timm_backbone'''		, snake_case__    )
       UpperCAmelCase     :			List[str]     	=BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

       if self.backbone_type == BackboneType.TIMM:
             self._init_timm_backbone(snake_case__    )
       elif self.backbone_type == BackboneType.TRANSFORMERS:
             self._init_transformers_backbone(snake_case__    )
       else:
             raise ValueError(f'''backbone_type {self.backbone_type} not supported.'''    )
 @property
 def 						UpperCAmelCase__							(	self    )						->				Optional[Any]:
       '''simple docstring'''





       return self._out_features
 @out_features.setter
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase	,    UpperCAmelCase     :			Optional[int]     	=get_aligned_output_features_output_indices(
           out_features=snake_case__		, out_indices=snake_case__		, stage_names=self.stage_names    )
 @property
 def 						UpperCAmelCase__							(	self    )						->				Optional[int]:
       '''simple docstring'''





       return self._out_indices
 @out_indices.setter
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				Optional[int]:
       '''simple docstring'''





       UpperCAmelCase	,    UpperCAmelCase     :			Tuple     	=get_aligned_output_features_output_indices(
           out_features=snake_case__		, out_indices=snake_case__		, stage_names=self.stage_names    )
 @property
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names    )}
 @property
 def 						UpperCAmelCase__							(	self    )						->				Optional[int]:
       '''simple docstring'''





       return [self.out_feature_channels[name] for name in self.out_features]
 def 						UpperCAmelCase__							(	self		, *snake_case__		, **snake_case__    )						->				List[str]:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=dict(inspect.signature(self.forward    ).parameters    )
       UpperCAmelCase     :			str     	={k: v for k, v in kwargs.items() if k in signature}
       return self(*snake_case__		, **snake_case__    )
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__ = None		, snake_case__ = None		, snake_case__ = None		, )						->				List[str]:
       '''simple docstring'''





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






 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=super().to_dict()
       UpperCAmelCase     :			str     	=output.pop('''_out_features'''    )
       UpperCAmelCase     :			int     	=output.pop('''_out_indices'''    )
       return output







class       __snake_case :
 @property
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       return self._out_features
 @out_features.setter
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase	,    UpperCAmelCase     :			Union[str, Any]     	=get_aligned_output_features_output_indices(
           out_features=snake_case__		, out_indices=snake_case__		, stage_names=self.stage_names    )
 @property
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       return self._out_indices
 @out_indices.setter
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase	,    UpperCAmelCase     :			List[str]     	=get_aligned_output_features_output_indices(
           out_features=snake_case__		, out_indices=snake_case__		, stage_names=self.stage_names    )






 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=super().to_dict()
       UpperCAmelCase     :			Dict     	=output.pop('''_out_features'''    )
       UpperCAmelCase     :			Any     	=output.pop('''_out_indices'''    )
       return output

import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin







class       __snake_case (  lowerCamelCase__       , unittest.TestCase ):
 __lowerCamelCase     : Any 	=   SpeechaTextaTokenizer
 __lowerCamelCase     : Tuple 	=   False
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       super().setUp()

       UpperCAmelCase     :			Union[str, Any]     	='''<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'''.split(''' '''    )
       UpperCAmelCase     :			int     	=['''he re</w> 123''', '''here a 1456''']
       UpperCAmelCase     :			int     	=dict(zip(snake_case__		, range(len(snake_case__    )    )    )    )

       UpperCAmelCase     :			Tuple     	={'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

       UpperCAmelCase     :			Tuple     	=tempfile.mkdtemp()
       UpperCAmelCase     :			Optional[Any]     	=os.path.join(self.tmpdirname		, VOCAB_FILES_NAMES['''vocab_file''']    )
       UpperCAmelCase     :			Dict     	=os.path.join(self.tmpdirname		, VOCAB_FILES_NAMES['''merges_file''']    )

       with open(self.vocab_file		, '''w'''		, encoding='''utf-8'''    ) as fp:
             fp.write(json.dumps(snake_case__    ) + '''\n'''    )

       with open(self.merges_file		, '''w'''    ) as fp:
             fp.write('''\n'''.join(snake_case__    )    )
 def 						UpperCAmelCase__							(	self    )						->				Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=list(self.get_tokenizer().get_vocab().keys()    )

       self.assertEqual(vocab_keys[0]		, '''<s>'''    )
       self.assertEqual(vocab_keys[1]		, '''<pad>'''    )
       self.assertEqual(vocab_keys[-1]		, '''vocab'''    )
       self.assertEqual(len(snake_case__    )		, 14    )
 def 						UpperCAmelCase__							(	self    )						->				Union[str, Any]:
       '''simple docstring'''





       self.assertEqual(self.get_tokenizer().vocab_size		, 14    )
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=SpeechaTextaTokenizer.from_pretrained(self.tmpdirname    )

       # make sure @@ is correctly concatenated
       UpperCAmelCase     :			str     	=[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
       UpperCAmelCase     :			Optional[Any]     	=tokenizer.decode(snake_case__    )

       self.assertTrue(output_string == '''herecouple words ofthe'''    )
 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[Any]     	=SpeechaTextaTokenizer.from_pretrained(self.tmpdirname    )

       with tempfile.TemporaryDirectory() as tmp_dirname:
             tokenizer.save_pretrained(snake_case__    )
             os.remove(os.path.join(snake_case__		, '''merges.txt'''    )    )

             # load tokenizer without merges file should not throw an error
             UpperCAmelCase     :			Any     	=SpeechaTextaTokenizer.from_pretrained(snake_case__    )

       with tempfile.TemporaryDirectory() as tmp_dirname:
             # save tokenizer and load again
             tokenizer.save_pretrained(snake_case__    )
             UpperCAmelCase     :			List[str]     	=SpeechaTextaTokenizer.from_pretrained(snake_case__    )

       self.assertIsNotNone(snake_case__    )






 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       if not self.test_slow_tokenizer:
             return

       UpperCAmelCase     :			List[str]     	=inspect.signature(self.tokenizer_class.__init__    )
       UpperCAmelCase     :			Union[str, Any]     	=self.get_tokenizer()

       for parameter_name, parameter in signature.parameters.items():
             if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
                   self.assertIn(snake_case__		, tokenizer.init_kwargs    )
