





'''simple docstring'''

import copy
from typing import Dict

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


lowercase__		:    int =       logging.get_logger(__name__)
class        __lowerCAmelCase  (					__magic_name__				):






       """simple docstring"""
       _snake_case			:  Any		       =					'semantic'





       def __init__(							self     :	Optional[Any] ,				lowerCAmelCase__     :	int=10000 ,				lowerCAmelCase__     :	Dict=True ,				lowerCAmelCase__     :	Any=768 ,				lowerCAmelCase__     :	Optional[int]=False ,				lowerCAmelCase__     :	Optional[int]=False ,				lowerCAmelCase__     :	Any=False ,				lowerCAmelCase__     :	str=False ,				lowerCAmelCase__     :	Optional[Any]=0.7 ,				lowerCAmelCase__     :	Tuple=True ,				lowerCAmelCase__     :	Optional[Any]=10048 ,				lowerCAmelCase__     :	List[str]=129595 ,				lowerCAmelCase__     :	Optional[int]=129599 ,				lowerCAmelCase__     :	Optional[int]=10000 ,				lowerCAmelCase__     :	Tuple=256 ,				lowerCAmelCase__     :	List[str]=49.9 ,				**lowerCAmelCase__     :	List[str] ,				)					->  Any:

            '''simple docstring'''




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

            _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        __lowerCAmelCase  (					__magic_name__				):






       """simple docstring"""
       _snake_case			:  Dict		       =					'coarse_acoustics'





       def __init__(							self     :	Tuple ,				lowerCAmelCase__     :	Any=True ,				lowerCAmelCase__     :	Tuple=False ,				lowerCAmelCase__     :	Optional[Any]=False ,				lowerCAmelCase__     :	Dict=False ,				lowerCAmelCase__     :	Optional[int]=False ,				lowerCAmelCase__     :	Tuple=0.7 ,				lowerCAmelCase__     :	Any=True ,				lowerCAmelCase__     :	Any=12048 ,				lowerCAmelCase__     :	int=75 ,				lowerCAmelCase__     :	Union[str, Any]=2 ,				lowerCAmelCase__     :	Optional[Any]=12050 ,				lowerCAmelCase__     :	List[str]=256 ,				lowerCAmelCase__     :	int = 630 ,				lowerCAmelCase__     :	int = 60 ,				**lowerCAmelCase__     :	List[Any] ,				)					->  Optional[Any]:

            '''simple docstring'''




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

            _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        __lowerCAmelCase  (					__magic_name__				):






       """simple docstring"""
       _snake_case			:  int		       =					'fine_acoustics'





       def __init__(							self     :	str ,				lowerCAmelCase__     :	int=0.5 ,				lowerCAmelCase__     :	Tuple=512 ,				lowerCAmelCase__     :	Dict=1024 ,				lowerCAmelCase__     :	int=8 ,				**lowerCAmelCase__     :	str ,				)					->  Optional[int]:

            '''simple docstring'''




            super().__init__(temperature=lowerCAmelCase__  )

            _UpperCamelCase       					= max_fine_history_length
            _UpperCamelCase       					= max_fine_input_length
            _UpperCamelCase       					= n_fine_codebooks

class        __lowerCAmelCase  (					__magic_name__				):






       """simple docstring"""
       _snake_case			:  Tuple		       =					'bark'
       _snake_case			:  List[str]		       =					True





       def __init__(							self     :	str ,				lowerCAmelCase__     :	Dict = None ,				lowerCAmelCase__     :	Dict = None ,				lowerCAmelCase__     :	Dict = None ,				lowerCAmelCase__     :	Optional[int]=24000 ,				lowerCAmelCase__     :	Dict=1024 ,				**lowerCAmelCase__     :	Optional[Any] ,				)					->  List[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(**lowerCAmelCase__  )
            _UpperCamelCase       					= BarkCoarseGenerationConfig(**lowerCAmelCase__  )
            _UpperCamelCase       					= BarkFineGenerationConfig(**lowerCAmelCase__  )

            _UpperCamelCase       					= sample_rate
            _UpperCamelCase       					= codebook_size





       @classmethod
       def snake_case__		(							cls     :	List[Any] ,				lowerCAmelCase__     :	BarkSemanticGenerationConfig ,				lowerCAmelCase__     :	BarkCoarseGenerationConfig ,				lowerCAmelCase__     :	BarkFineGenerationConfig ,				**lowerCAmelCase__     :	List[Any] ,				)					->  Tuple:

            '''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() ,				**lowerCAmelCase__ ,				)







       def snake_case__		(							self     :	List[Any]  )					->  Optional[Any]:

            '''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 ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
)


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_pt_objects import *  # noqa F403
else:
   from .consistency_models import ConsistencyModelPipeline
   from .dance_diffusion import DanceDiffusionPipeline
   from .ddim import DDIMPipeline
   from .ddpm import DDPMPipeline
   from .dit import DiTPipeline
   from .latent_diffusion import LDMSuperResolutionPipeline
   from .latent_diffusion_uncond import LDMPipeline
   from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
   from .pndm import PNDMPipeline
   from .repaint import RePaintPipeline
   from .score_sde_ve import ScoreSdeVePipeline
   from .stochastic_karras_ve import KarrasVePipeline

try:
   if not (is_torch_available() and is_librosa_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
   from .audio_diffusion import AudioDiffusionPipeline, Mel

try:
   if not (is_torch_available() and is_transformers_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .alt_diffusion import AltDiffusionImgaImgPipeline, AltDiffusionPipeline
   from .audioldm import AudioLDMPipeline
   from .controlnet import (
       StableDiffusionControlNetImgaImgPipeline,
       StableDiffusionControlNetInpaintPipeline,
       StableDiffusionControlNetPipeline,
   )
   from .deepfloyd_if import (
       IFImgaImgPipeline,
       IFImgaImgSuperResolutionPipeline,
       IFInpaintingPipeline,
       IFInpaintingSuperResolutionPipeline,
       IFPipeline,
       IFSuperResolutionPipeline,
   )
   from .kandinsky import (
       KandinskyImgaImgPipeline,
       KandinskyInpaintPipeline,
       KandinskyPipeline,
       KandinskyPriorPipeline,
   )
   from .kandinskya_a import (
       KandinskyVaaControlnetImgaImgPipeline,
       KandinskyVaaControlnetPipeline,
       KandinskyVaaImgaImgPipeline,
       KandinskyVaaInpaintPipeline,
       KandinskyVaaPipeline,
       KandinskyVaaPriorEmbaEmbPipeline,
       KandinskyVaaPriorPipeline,
   )
   from .latent_diffusion import LDMTextToImagePipeline
   from .paint_by_example import PaintByExamplePipeline
   from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
   from .shap_e import ShapEImgaImgPipeline, ShapEPipeline
   from .stable_diffusion import (
       CycleDiffusionPipeline,
       StableDiffusionAttendAndExcitePipeline,
       StableDiffusionDepthaImgPipeline,
       StableDiffusionDiffEditPipeline,
       StableDiffusionImageVariationPipeline,
       StableDiffusionImgaImgPipeline,
       StableDiffusionInpaintPipeline,
       StableDiffusionInpaintPipelineLegacy,
       StableDiffusionInstructPixaPixPipeline,
       StableDiffusionLatentUpscalePipeline,
       StableDiffusionLDMaDPipeline,
       StableDiffusionModelEditingPipeline,
       StableDiffusionPanoramaPipeline,
       StableDiffusionParadigmsPipeline,
       StableDiffusionPipeline,
       StableDiffusionPixaPixZeroPipeline,
       StableDiffusionSAGPipeline,
       StableDiffusionUpscalePipeline,
       StableUnCLIPImgaImgPipeline,
       StableUnCLIPPipeline,
   )
   from .stable_diffusion_safe import StableDiffusionPipelineSafe
   from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
   from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
   from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
   from .versatile_diffusion import (
       VersatileDiffusionDualGuidedPipeline,
       VersatileDiffusionImageVariationPipeline,
       VersatileDiffusionPipeline,
       VersatileDiffusionTextToImagePipeline,
   )
   from .vq_diffusion import VQDiffusionPipeline


try:
   if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
   from .stable_diffusion_xl import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
   if not is_onnx_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_onnx_objects import *  # noqa F403
else:
   from .onnx_utils import OnnxRuntimeModel

try:
   if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
   from .stable_diffusion import (
       OnnxStableDiffusionImgaImgPipeline,
       OnnxStableDiffusionInpaintPipeline,
       OnnxStableDiffusionInpaintPipelineLegacy,
       OnnxStableDiffusionPipeline,
       OnnxStableDiffusionUpscalePipeline,
       StableDiffusionOnnxPipeline,
   )

try:
   if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
   from .stable_diffusion import StableDiffusionKDiffusionPipeline

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_flax_objects import *  # noqa F403
else:
   from .pipeline_flax_utils import FlaxDiffusionPipeline


try:
   if not (is_flax_available() and is_transformers_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
   from .controlnet import FlaxStableDiffusionControlNetPipeline
   from .stable_diffusion import (
       FlaxStableDiffusionImgaImgPipeline,
       FlaxStableDiffusionInpaintPipeline,
       FlaxStableDiffusionPipeline,
   )
try:
   if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
   from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline



