"""simple docstring"""







import copy
from typing import Dict

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


lowerCamelCase      : Optional[Any]       =logging.get_logger(__name__)






class 							__snake_case(					A_	):


    '''simple docstring'''





    _UpperCAmelCase       =			'''semantic'''
    def __init__(  self					,       __lowerCamelCase=10000					,       __lowerCamelCase=True					,       __lowerCamelCase=768					,       __lowerCamelCase=False					,       __lowerCamelCase=False					,       __lowerCamelCase=False					,       __lowerCamelCase=False					,       __lowerCamelCase=0.7					,       __lowerCamelCase=True					,       __lowerCamelCase=10048					,       __lowerCamelCase=129595					,       __lowerCamelCase=129599					,       __lowerCamelCase=10000					,       __lowerCamelCase=256					,       __lowerCamelCase=49.9					,       **__lowerCamelCase					,       ):




         '''simple docstring'''



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

         __A					:					str								=		text_encoding_offset
         __A					:					Union[str, Any]								=		text_pad_token
         __A					:					Optional[Any]								=		eos_token_id
         __A					:					Optional[Any]								=		semantic_infer_token
         __A					:					Dict								=		semantic_vocab_size
         __A					:					Optional[Any]								=		max_input_semantic_length
         __A					:					Dict								=		semantic_rate_hz







class 							__snake_case(					A_	):


    '''simple docstring'''





    _UpperCAmelCase       =			'''coarse_acoustics'''
    def __init__(  self					,       __lowerCamelCase=True					,       __lowerCamelCase=False					,       __lowerCamelCase=False					,       __lowerCamelCase=False					,       __lowerCamelCase=False					,       __lowerCamelCase=0.7					,       __lowerCamelCase=True					,       __lowerCamelCase=12048					,       __lowerCamelCase=75					,       __lowerCamelCase=2					,       __lowerCamelCase=12050					,       __lowerCamelCase=256					,       __lowerCamelCase = 630					,       __lowerCamelCase = 60					,       **__lowerCamelCase					,       ):




         '''simple docstring'''



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

         __A					:					str								=		coarse_semantic_pad_token
         __A					:					Tuple								=		coarse_rate_hz
         __A					:					Dict								=		n_coarse_codebooks
         __A					:					Optional[Any]								=		coarse_infer_token
         __A					:					Tuple								=		max_coarse_input_length
         __A					:					int								=		max_coarse_history
         __A					:					Dict								=		sliding_window_len







class 							__snake_case(					A_	):


    '''simple docstring'''





    _UpperCAmelCase       =			'''fine_acoustics'''
    def __init__(  self					,       __lowerCamelCase=0.5					,       __lowerCamelCase=512					,       __lowerCamelCase=1024					,       __lowerCamelCase=8					,       **__lowerCamelCase					,       ):




         '''simple docstring'''



         super().__init__(temperature=A_	)

         __A					:					Tuple								=		max_fine_history_length
         __A					:					List[str]								=		max_fine_input_length
         __A					:					Optional[int]								=		n_fine_codebooks







class 							__snake_case(					A_	):


    '''simple docstring'''





    _UpperCAmelCase       =			'''bark'''
    _UpperCAmelCase       =			True
    def __init__(  self					,       __lowerCamelCase = None					,       __lowerCamelCase = None					,       __lowerCamelCase = None					,       __lowerCamelCase=24000					,       __lowerCamelCase=1024					,       **__lowerCamelCase					,       ):




         '''simple docstring'''



         if semantic_config is None:
              __A					:					Optional[int]								=		{}
              logger.info('semantic_config is None. initializing the semantic model with default values.'	)

         if coarse_acoustics_config is None:
              __A					:					Optional[int]								=		{}
              logger.info('coarse_acoustics_config is None. initializing the coarse model with default values.'	)

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

         __A					:					Union[str, Any]								=		BarkSemanticGenerationConfig(**A_	)
         __A					:					str								=		BarkCoarseGenerationConfig(**A_	)
         __A					:					Dict								=		BarkFineGenerationConfig(**A_	)

         __A					:					Dict								=		sample_rate
         __A					:					str								=		codebook_size
    @classmethod
    def 						_a							(  cls					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       **__lowerCamelCase					,       ):




         '''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()					,       **A_					,       )

    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Tuple								=		copy.deepcopy(self.__dict__	)

         __A					:					int								=		self.semantic_config.to_dict()
         __A					:					Any								=		self.coarse_acoustics_config.to_dict()
         __A					:					Union[str, Any]								=		self.fine_acoustics_config.to_dict()

         __A					:					str								=		self.__class__.model_type
         return output






import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=256					)							->   Optional[int]:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     min(img.size					)

     if s < target_image_size:
          raise ValueError(f'''min dim for image {s} < {target_image_size}'''					)

     _lowerCAmelCase :       Dict	   =     target_image_size / s
     _lowerCAmelCase :       Dict	   =     (round(r * img.size[1]					), round(r * img.size[0]					))
     _lowerCAmelCase :       Any	   =     TF.resize(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				interpolation=PIL.Image.LANCZOS					)
     _lowerCAmelCase :       str	   =     TF.center_crop(SCREAMING_SNAKE_CASE		,				output_size=2 * [target_image_size]					)
     _lowerCAmelCase :       int	   =     torch.unsqueeze(T.ToTensor()(SCREAMING_SNAKE_CASE					)		,				0					)
     return img







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   List[str]:

     """simple docstring"""







     _lowerCAmelCase :       List[Any]	   =     2.0 * x - 1.0
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE="RGB"					)							->   int:

     """simple docstring"""







     _lowerCAmelCase :       Optional[int]	   =     x.detach().cpu()
     if process:
          _lowerCAmelCase :       List[Any]	   =     post_process_tensor(SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       Optional[int]	   =     x.numpy()
     if process:
          _lowerCAmelCase :       List[Any]	   =     (255 * x).astype(np.uinta					)
     _lowerCAmelCase :       Optional[int]	   =     Image.fromarray(SCREAMING_SNAKE_CASE					)
     if not x.mode == mode:
          _lowerCAmelCase :       str	   =     x.convert(SCREAMING_SNAKE_CASE					)
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Union[str, Any]:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     torch.clamp(SCREAMING_SNAKE_CASE		,				-1.0		,				1.0					)
     _lowerCAmelCase :       Any	   =     (x + 1.0) / 2.0
     _lowerCAmelCase :       List[Any]	   =     x.permute(1		,				2		,				0					)
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Tuple:

     """simple docstring"""







     _lowerCAmelCase :       str	   =     post_process_tensor(x.squeeze()					)
     return x.permute(2		,				0		,				1					).unsqueeze(0					)






