




'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFXLMForMultipleChoice,
       TFXLMForQuestionAnsweringSimple,
       TFXLMForSequenceClassification,
       TFXLMForTokenClassification,
       TFXLMModel,
       TFXLMWithLMHeadModel,
       XLMConfig,
   )

class      _a    :






     def __init__(				self      ,_SCREAMING_SNAKE_CASE      ,)							->    str:
          _snake_case							=   parent
          _snake_case							=   13
          _snake_case							=   7
          _snake_case							=   True
          _snake_case							=   True
          _snake_case							=   True
          _snake_case							=   True
          _snake_case							=   True
          _snake_case							=   False
          _snake_case							=   False
          _snake_case							=   False
          _snake_case							=   2
          _snake_case							=   99
          _snake_case							=   0
          _snake_case							=   32
          _snake_case							=   2
          _snake_case							=   4
          _snake_case							=   0.1
          _snake_case							=   0.1
          _snake_case							=   512
          _snake_case							=   16
          _snake_case							=   2
          _snake_case							=   0.0_2
          _snake_case							=   3
          _snake_case							=   4
          _snake_case							=   "last"
          _snake_case							=   True
          _snake_case							=   None
          _snake_case							=   0






     def        _lowercase			(				self					)							->    Union[str, Any]:
          _snake_case							=   ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size					)
          _snake_case							=   random_attention_mask([self.batch_size, self.seq_length]      ,dtype=tf.floataa					)

          _snake_case							=   None
          if self.use_input_lengths:
               _snake_case							=   (
                   ids_tensor([self.batch_size]      ,vocab_size=2					) + self.seq_length - 2
               )  # small variation of seq_length

          _snake_case							=   None
          if self.use_token_type_ids:
               _snake_case							=   ids_tensor([self.batch_size, self.seq_length]      ,self.n_langs					)

          _snake_case							=   None
          _snake_case							=   None
          _snake_case							=   None
          if self.use_labels:
               _snake_case							=   ids_tensor([self.batch_size]      ,self.type_sequence_label_size					)
               _snake_case							=   ids_tensor([self.batch_size, self.seq_length]      ,self.num_labels					)
               _snake_case							=   ids_tensor([self.batch_size]      ,2      ,dtype=tf.floataa					)
               _snake_case							=   ids_tensor([self.batch_size]      ,self.num_choices					)

          _snake_case							=   XLMConfig(
              vocab_size=self.vocab_size      ,n_special=self.n_special      ,emb_dim=self.hidden_size      ,n_layers=self.num_hidden_layers      ,n_heads=self.num_attention_heads      ,dropout=self.hidden_dropout_prob      ,attention_dropout=self.attention_probs_dropout_prob      ,gelu_activation=self.gelu_activation      ,sinusoidal_embeddings=self.sinusoidal_embeddings      ,asm=self.asm      ,causal=self.causal      ,n_langs=self.n_langs      ,max_position_embeddings=self.max_position_embeddings      ,initializer_range=self.initializer_range      ,summary_type=self.summary_type      ,use_proj=self.use_proj      ,bos_token_id=self.bos_token_id      ,)

          return (
              config,
              input_ids,
              token_type_ids,
              input_lengths,
              sequence_labels,
              token_labels,
              is_impossible_labels,
              choice_labels,
              input_mask,
          )






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,)							->    Optional[int]:
          _snake_case							=   TFXLMModel(config=_SCREAMING_SNAKE_CASE					)
          _snake_case							=   {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
          _snake_case							=   model(_SCREAMING_SNAKE_CASE					)

          _snake_case							=   [input_ids, input_mask]
          _snake_case							=   model(_SCREAMING_SNAKE_CASE					)
          self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.hidden_size)					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,)							->    int:
          _snake_case							=   TFXLMWithLMHeadModel(_SCREAMING_SNAKE_CASE					)

          _snake_case							=   {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
          _snake_case							=   model(_SCREAMING_SNAKE_CASE					)

          _snake_case							=   outputs

          self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.vocab_size)					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,)							->    int:
          _snake_case							=   TFXLMForQuestionAnsweringSimple(_SCREAMING_SNAKE_CASE					)

          _snake_case							=   {"input_ids": input_ids, "lengths": input_lengths}

          _snake_case							=   model(_SCREAMING_SNAKE_CASE					)

          self.parent.assertEqual(result.start_logits.shape      ,(self.batch_size, self.seq_length)					)
          self.parent.assertEqual(result.end_logits.shape      ,(self.batch_size, self.seq_length)					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,)							->    Tuple:
          _snake_case							=   TFXLMForSequenceClassification(_SCREAMING_SNAKE_CASE					)

          _snake_case							=   {"input_ids": input_ids, "lengths": input_lengths}

          _snake_case							=   model(_SCREAMING_SNAKE_CASE					)

          self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.type_sequence_label_size)					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,)							->    Dict:
          _snake_case							=   self.num_labels
          _snake_case							=   TFXLMForTokenClassification(config=_SCREAMING_SNAKE_CASE					)
          _snake_case							=   {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          _snake_case							=   model(_SCREAMING_SNAKE_CASE					)
          self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.num_labels)					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,)							->    Optional[int]:
          _snake_case							=   self.num_choices
          _snake_case							=   TFXLMForMultipleChoice(config=_SCREAMING_SNAKE_CASE					)
          _snake_case							=   tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE      ,1					)      ,(1, self.num_choices, 1)					)
          _snake_case							=   tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE      ,1					)      ,(1, self.num_choices, 1)					)
          _snake_case							=   tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE      ,1					)      ,(1, self.num_choices, 1)					)
          _snake_case							=   {
              "input_ids": multiple_choice_inputs_ids,
              "attention_mask": multiple_choice_input_mask,
              "token_type_ids": multiple_choice_token_type_ids,
          }
          _snake_case							=   model(_SCREAMING_SNAKE_CASE					)
          self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_choices)					)






     def        _lowercase			(				self					)							->    Optional[Any]:
          _snake_case							=   self.prepare_config_and_inputs()
          (
              (
              _snake_case      
          )							,      (
              _snake_case      
          )							,      (
              _snake_case      
          )							,      (
              _snake_case      
          )							,      (
              _snake_case      
          )							,      (
              _snake_case      
          )							,      (
              _snake_case      
          )							,      (
              _snake_case      
          )							,      (
              _snake_case      
          )							,            
          )							=   config_and_inputs
          _snake_case							=   {
              "input_ids": input_ids,
              "token_type_ids": token_type_ids,
              "langs": token_type_ids,
              "lengths": input_lengths,
          }
          return config, inputs_dict







@require_tf
class      _a    (		__lowerCAmelCase      ,      __lowerCAmelCase      ,      unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:					List[str]					       =		(
         (
             TFXLMModel,
             TFXLMWithLMHeadModel,
             TFXLMForSequenceClassification,
             TFXLMForQuestionAnsweringSimple,
             TFXLMForTokenClassification,
             TFXLMForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     SCREAMING_SNAKE_CASE_							:					Optional[int]					       =		(
         (TFXLMWithLMHeadModel,) if is_tf_available() else ()
     )  # TODO (PVP): Check other models whether language generation is also applicable
     SCREAMING_SNAKE_CASE_							:					Union[str, Any]					       =		(
         {
             """feature-extraction""": TFXLMModel,
             """fill-mask""": TFXLMWithLMHeadModel,
             """question-answering""": TFXLMForQuestionAnsweringSimple,
             """text-classification""": TFXLMForSequenceClassification,
             """text-generation""": TFXLMWithLMHeadModel,
             """token-classification""": TFXLMForTokenClassification,
             """zero-shot""": TFXLMForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     SCREAMING_SNAKE_CASE_							:					Union[str, Any]					       =		False
     SCREAMING_SNAKE_CASE_							:					Optional[int]					       =		False






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    Optional[Any]:
          if (
              pipeline_test_casse_name == "QAPipelineTests"
              and tokenizer_name is not None
              and not tokenizer_name.endswith("Fast"					)
          ):
               # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
               # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
               # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
               return True

          return False






     def        _lowercase			(				self					)							->    List[str]:
          _snake_case							=   TFXLMModelTester(self					)
          _snake_case							=   ConfigTester(self      ,config_class=_SCREAMING_SNAKE_CASE      ,emb_dim=37					)






     def        _lowercase			(				self					)							->    Tuple:
          self.config_tester.run_common_tests()






     def        _lowercase			(				self					)							->    Optional[Any]:
          _snake_case							=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_model(*_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self					)							->    Union[str, Any]:
          _snake_case							=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_lm_head(*_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self					)							->    Any:
          _snake_case							=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_qa(*_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self					)							->    Tuple:
          _snake_case							=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_sequence_classif(*_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self					)							->    Optional[int]:
          _snake_case							=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_for_token_classification(*_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self					)							->    Optional[int]:
          _snake_case							=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_for_multiple_choice(*_SCREAMING_SNAKE_CASE					)






     @slow
     def        _lowercase			(				self					)							->    Optional[Any]:
          for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _snake_case							=   TFXLMModel.from_pretrained(_SCREAMING_SNAKE_CASE					)
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)







@require_tf
class      _a    (		unittest.TestCase							):






     @slow
     def        _lowercase			(				self					)							->    Union[str, Any]:
          _snake_case							=   TFXLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048"					)
          _snake_case							=   tf.convert_to_tensor([[14, 447]]      ,dtype=tf.intaa					)  # the president
          _snake_case							=   [
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
          ]  # the president the president the president the president the president the president the president the president the president the president
          # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
          _snake_case							=   model.generate(_SCREAMING_SNAKE_CASE      ,do_sample=_SCREAMING_SNAKE_CASE					)
          self.assertListEqual(output_ids[0].numpy().tolist()      ,_SCREAMING_SNAKE_CASE					)








'''simple docstring'''





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

class      _a       (		enum.Enum							):
     SCREAMING_SNAKE_CASE_							:					Optional[int]					       =		"""timm"""
     SCREAMING_SNAKE_CASE_							:					int					       =		"""transformers"""







def 			__a			(     _UpperCamelCase:					Optional[Iterable[str]]      ,		_UpperCamelCase:					Optional[Iterable[int]]      ,		_UpperCamelCase:					Optional[Iterable[str]]					)		->				Union[str, Any]:

     """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(_UpperCamelCase      ,		(list,)					):
               raise ValueError(F"""out_features must be a list {type(_UpperCamelCase					)}"""					)
          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(_UpperCamelCase      ,		(list, tuple)					):
               raise ValueError(F"""out_indices must be a list or tuple, got {type(_UpperCamelCase					)}"""					)
          if any(idx >= len(_UpperCamelCase					) 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(_UpperCamelCase					) != len(_UpperCamelCase					):
               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			(     _UpperCamelCase:					Optional[List[str]]      ,		_UpperCamelCase:					Optional[Union[List[int], Tuple[int]]]      ,		_UpperCamelCase:					List[str]      ,		)		->				Any:

     """simple docstring"""




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





def 			__a			(     _UpperCamelCase:					Optional[List[str]]      ,		_UpperCamelCase:					Optional[Union[List[int], Tuple[int]]]      ,		_UpperCamelCase:					List[str]      ,		)		->				Tuple[List[str], List[int]]:

     """simple docstring"""




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

class      _a       :
     SCREAMING_SNAKE_CASE_							:					Optional[BackboneType]					       =		None






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    None:
          if getattr(self      ,"_backbone"      ,_SCREAMING_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']
          _snake_case							=   [stage["module"] for stage in self._backbone.feature_info.info]
          _snake_case							=   [stage["num_chs"] for stage in self._backbone.feature_info.info]
          _snake_case							=   self._backbone.feature_info.out_indices
          _snake_case							=   self._backbone.feature_info.module_name()

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






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    None:
          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"stage_names"					)
          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"out_features"      ,_SCREAMING_SNAKE_CASE					)
          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"out_indices"      ,_SCREAMING_SNAKE_CASE					)

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






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    None:
          _snake_case							=   config

          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"use_timm_backbone"      ,_SCREAMING_SNAKE_CASE					)
          _snake_case							=   BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

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






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






     @out_features.setter
     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    str:
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)






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






     @out_indices.setter
     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    int:
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)






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






     def        _lowercase			(				self      ,*_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)							->    str:
          _snake_case							=   dict(inspect.signature(self.forward					).parameters					)
          _snake_case							=   {k: v for k, v in kwargs.items() if k in signature}
          return self(*_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)






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






     def        _lowercase			(				self					)							->    Tuple:
          _snake_case							=   super().to_dict()
          _snake_case							=   output.pop("_out_features"					)
          _snake_case							=   output.pop("_out_indices"					)
          return output







class      _a       :






     @property
     def        _lowercase			(				self					)							->    List[str]:
          return self._out_features






     @out_features.setter
     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    str:
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)






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






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






     def        _lowercase			(				self					)							->    List[str]:
          _snake_case							=   super().to_dict()
          _snake_case							=   output.pop("_out_features"					)
          _snake_case							=   output.pop("_out_indices"					)
          return output


