





"""simple docstring"""


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



class        lowercase_			(       enum.Enum							):
       snake_case__			:							List[str]      							=       """timm"""
       snake_case__			:							Dict      							=       """transformers"""





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		)      ->					List[str]:

 '''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		,       _UpperCAmelCase		,       _UpperCAmelCase		,       )      ->					Optional[int]:

 '''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		,       _UpperCAmelCase		,       _UpperCAmelCase		,       )      ->					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        lowercase_			:
       snake_case__			:							Optional[BackboneType]      							=       None

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        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 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        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 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        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 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        return self._out_features

       @out_features.setter
       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        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 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        return self._out_indices

       @out_indices.setter
       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        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 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

       @property
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self				,   *__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        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 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			super().to_dict()
        snake_case__      							=			output.pop("_out_features"       )
        snake_case__      							=			output.pop("_out_indices"       )
        return output



class        lowercase_			:

       @property
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        return self._out_features

       @out_features.setter
       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        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 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        return self._out_indices

       @out_indices.setter
       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        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 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			super().to_dict()
        snake_case__      							=			output.pop("_out_features"       )
        snake_case__      							=			output.pop("_out_indices"       )
        return output








"""simple docstring"""


import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


a__   :       str   						=			logging.get_logger(__name__)



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Dict										=       """semantic"""

       def __init__(     self				,   __SCREAMING_SNAKE_CASE=1_0000				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=768				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=0.7				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=1_0048				,   __SCREAMING_SNAKE_CASE=12_9595				,   __SCREAMING_SNAKE_CASE=12_9599				,   __SCREAMING_SNAKE_CASE=1_0000				,   __SCREAMING_SNAKE_CASE=256				,   __SCREAMING_SNAKE_CASE=49.9				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        super().__init__(
            temperature=__SCREAMING_SNAKE_CASE				,   do_sample=__SCREAMING_SNAKE_CASE				,   eos_token_id=__SCREAMING_SNAKE_CASE				,   renormalize_logits=__SCREAMING_SNAKE_CASE				,   max_new_tokens=__SCREAMING_SNAKE_CASE				,   output_scores=__SCREAMING_SNAKE_CASE				,   return_dict_in_generate=__SCREAMING_SNAKE_CASE				,   output_hidden_states=__SCREAMING_SNAKE_CASE				,   output_attentions=__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE				,   )

        snake_case__      							=			text_encoding_offset
        snake_case__      							=			text_pad_token
        snake_case__      							=			eos_token_id
        snake_case__      							=			semantic_infer_token
        snake_case__      							=			semantic_vocab_size
        snake_case__      							=			max_input_semantic_length
        snake_case__      							=			semantic_rate_hz



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Union[str, Any]										=       """coarse_acoustics"""

       def __init__(     self				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=0.7				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=1_2048				,   __SCREAMING_SNAKE_CASE=75				,   __SCREAMING_SNAKE_CASE=2				,   __SCREAMING_SNAKE_CASE=1_2050				,   __SCREAMING_SNAKE_CASE=256				,   __SCREAMING_SNAKE_CASE = 630				,   __SCREAMING_SNAKE_CASE = 60				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        super().__init__(
            temperature=__SCREAMING_SNAKE_CASE				,   do_sample=__SCREAMING_SNAKE_CASE				,   renormalize_logits=__SCREAMING_SNAKE_CASE				,   output_scores=__SCREAMING_SNAKE_CASE				,   return_dict_in_generate=__SCREAMING_SNAKE_CASE				,   output_hidden_states=__SCREAMING_SNAKE_CASE				,   output_attentions=__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE				,   )

        snake_case__      							=			coarse_semantic_pad_token
        snake_case__      							=			coarse_rate_hz
        snake_case__      							=			n_coarse_codebooks
        snake_case__      							=			coarse_infer_token
        snake_case__      							=			max_coarse_input_length
        snake_case__      							=			max_coarse_history
        snake_case__      							=			sliding_window_len



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Union[str, Any]										=       """fine_acoustics"""

       def __init__(     self				,   __SCREAMING_SNAKE_CASE=0.5				,   __SCREAMING_SNAKE_CASE=512				,   __SCREAMING_SNAKE_CASE=1024				,   __SCREAMING_SNAKE_CASE=8				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        super().__init__(temperature=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			max_fine_history_length
        snake_case__      							=			max_fine_input_length
        snake_case__      							=			n_fine_codebooks



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							List[str]										=       """bark"""
       snake_case__			:							Dict										=       True

       def __init__(     self				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE=2_4000				,   __SCREAMING_SNAKE_CASE=1024				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        if semantic_config is None:
         snake_case__      							=			{}
         logger.info("semantic_config is None. initializing the semantic model with default values."       )

        if coarse_acoustics_config is None:
         snake_case__      							=			{}
         logger.info("coarse_acoustics_config is None. initializing the coarse model with default values."       )

        if fine_acoustics_config is None:
         snake_case__      							=			{}
         logger.info("fine_acoustics_config is None. initializing the fine model with default values."       )

        snake_case__      							=			BarkSemanticGenerationConfig(**__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			BarkCoarseGenerationConfig(**__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			BarkFineGenerationConfig(**__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			sample_rate
        snake_case__      							=			codebook_size

       @classmethod
       def 						_SCREAMING_SNAKE_CASE(     cls				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        return cls(
            semantic_config=semantic_config.to_dict()				,   coarse_acoustics_config=coarse_acoustics_config.to_dict()				,   fine_acoustics_config=fine_acoustics_config.to_dict()				,   **__SCREAMING_SNAKE_CASE				,   )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			copy.deepcopy(self.__dict__       )

        snake_case__      							=			self.semantic_config.to_dict()
        snake_case__      							=			self.coarse_acoustics_config.to_dict()
        snake_case__      							=			self.fine_acoustics_config.to_dict()

        snake_case__      							=			self.__class__.model_type
        return output

