


'''simple docstring'''

import copy
from typing import Dict

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


__lowercase:						Optional[int]  		=   logging.get_logger(__name__)







class        UpperCAmelCase       (				__lowerCAmelCase):
 _lowerCamelCase       :    Optional[Any]			    =    """semantic"""

 def __init__(       self       :		List[str],    a_       :		int=1_0000,    a_       :		Optional[Any]=True,    a_       :		Any=768,    a_       :		Tuple=False,    a_       :		Tuple=False,    a_       :		str=False,    a_       :		str=False,    a_       :		Any=0.7,    a_       :		Optional[Any]=True,    a_       :		Dict=1_0048,    a_       :		List[Any]=12_9595,    a_       :		Any=12_9599,    a_       :		Optional[int]=1_0000,    a_       :		Optional[Any]=256,    a_       :		Tuple=49.9,    **a_       :		Optional[Any],    ):





    """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,    )

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







class        UpperCAmelCase       (				__lowerCAmelCase):
 _lowerCamelCase       :    str			    =    """coarse_acoustics"""

 def __init__(       self       :		Optional[int],    a_       :		Dict=True,    a_       :		Dict=False,    a_       :		Union[str, Any]=False,    a_       :		List[str]=False,    a_       :		Any=False,    a_       :		Dict=0.7,    a_       :		str=True,    a_       :		str=1_2048,    a_       :		List[Any]=75,    a_       :		List[str]=2,    a_       :		Dict=1_2050,    a_       :		int=256,    a_       :		List[Any] = 630,    a_       :		Dict = 60,    **a_       :		str,    ):





    """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,    )

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







class        UpperCAmelCase       (				__lowerCAmelCase):
 _lowerCamelCase       :    List[str]			    =    """fine_acoustics"""

 def __init__(       self       :		Optional[Any],    a_       :		Optional[Any]=0.5,    a_       :		str=512,    a_       :		Tuple=1024,    a_       :		List[Any]=8,    **a_       :		Any,    ):





    """simple docstring"""



    super().__init__(temperature=__SCREAMING_SNAKE_CASE    )

    UpperCamelCase__         =  max_fine_history_length
    UpperCamelCase__         =  max_fine_input_length
    UpperCamelCase__         =  n_fine_codebooks







class        UpperCAmelCase       (				__lowerCAmelCase):
 _lowerCamelCase       :    str			    =    """bark"""
 _lowerCamelCase       :    Optional[int]			    =    True

 def __init__(       self       :		List[Any],    a_       :		str = None,    a_       :		List[str] = None,    a_       :		List[Any] = None,    a_       :		Tuple=2_4000,    a_       :		Dict=1024,    **a_       :		Union[str, Any],    ):





    """simple docstring"""



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

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

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

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

    UpperCamelCase__         =  sample_rate
    UpperCamelCase__         =  codebook_size

 @classmethod
 def 						lowercase_  (       cls       :		Optional[int],    a_       :		List[str],    a_       :		int,    a_       :		List[str],    **a_       :		str,    ):





    """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 						lowercase_  (       self       :		Dict    ):





    """simple docstring"""



    UpperCamelCase__         =  copy.deepcopy(self.__dict__    )

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

    UpperCamelCase__         =  self.__class__.model_type
    return output






"""simple docstring"""


from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION



class    lowercase_ (   __lowerCAmelCase ,__lowerCAmelCase							):

       def __init__(	self   ):






        '''simple docstring'''

        super().__init__()

        self.register_buffer("watermark_image"      ,    torch.zeros((62, 62, 4)   )   )
        snake_case__										= None

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






        '''simple docstring'''

        snake_case__										= images[0].height
        snake_case__										= images[0].width

        snake_case__										= sample_size or h

        snake_case__										= min(h / sample_size      ,    w / sample_size   )
        snake_case__							,      snake_case__										= (int(h / coef   ), int(w / coef   )) if coef < 1 else (h, w)

        snake_case__							,      snake_case__										= 1024**2, img_w * img_h
        snake_case__										= (Sa / Sa) ** 0.5
        snake_case__							,      snake_case__							,      snake_case__										= int(K * 62   ), img_w - int(14 * K   ), img_h - int(14 * K   )

        if self.watermark_image_as_pil is None:
         snake_case__										= self.watermark_image.to(torch.uinta   ).cpu().numpy()
         snake_case__										= Image.fromarray(__SCREAMING_SNAKE_CASE      ,    mode="RGBA"   )
         snake_case__										= watermark_image

        snake_case__										= self.watermark_image_as_pil.resize(
            (wm_size, wm_size)      ,    PIL_INTERPOLATION["bicubic"]      ,    reducing_gap=__SCREAMING_SNAKE_CASE   )

        for pil_img in images:
         pil_img.paste(__SCREAMING_SNAKE_CASE      ,    box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)      ,    mask=wm_img.split()[-1]   )

        return images

