
'''simple docstring'''





from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)


class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE):






 def __init__(      self	:Dict						,					_A	:AutoencoderKL						,					_A	:CLIPTextModel						,					_A	:CLIPTokenizer						,					_A	:UNetaDConditionModel						,					_A	:DDIMScheduler						,					_A	:float = 0.93						,					_A	:bool = True						,					)					->      Optional[Any]:







       '''simple docstring'''



       __A													=      mixing_coeff
       __A													=      leapfrog_steps

       super().__init__()
       self.register_modules(
           vae=_A						,					text_encoder=_A						,					tokenizer=_A						,					unet=_A						,					scheduler=_A						,					)

       __A													=      2 ** (len(self.vae.config.block_out_channels      ) - 1)
       __A													=      VaeImageProcessor(vae_scale_factor=self.vae_scale_factor      )






 def 						lowercase_    (      self	:Tuple						,					_A	:str						,					_A	:Optional[str] = None						,					_A	:bool = False      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      self.tokenizer(
           _A						,					padding='max_length'						,					max_length=self.tokenizer.model_max_length						,					truncation=_A						,					return_tensors='pt'						,					)

       __A													=      self.text_encoder(text_inputs.input_ids.to(self.device      )      ).last_hidden_state

       __A													=      prompt_embeds.to(dtype=self.text_encoder.dtype						,					device=self.device      )

       if do_classifier_free_guidance:
             __A													=      '' if negative_prompt is None else negative_prompt

             __A													=      self.tokenizer(
                 _A						,					padding='max_length'						,					max_length=self.tokenizer.model_max_length						,					truncation=_A						,					return_tensors='pt'						,					)

             __A													=      self.text_encoder(uncond_input.input_ids.to(self.device      )      ).last_hidden_state

             __A													=      torch.cat([negative_prompt_embeds, prompt_embeds]      )

       return prompt_embeds






 def 						lowercase_    (      self	:Any						,					_A	:torch.Tensor						,					_A	:torch.Tensor      )					->      Tuple:







       '''simple docstring'''



       __A													=      self.mixing_coeff * x + (1 - self.mixing_coeff) * y
       __A													=      self.mixing_coeff * y + (1 - self.mixing_coeff) * x

       return [x, y]






 def 						lowercase_    (      self	:Dict						,					_A	:torch.Tensor						,					_A	:torch.Tensor      )					->      Any:







       '''simple docstring'''



       __A													=      (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
       __A													=      (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

       return [x, y]






 def 						lowercase_    (      self	:List[str]						,					_A	:torch.Tensor      )					->      Tuple:







       '''simple docstring'''



       __A													=      int(_A      )

       __A													=      self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

       return alpha_prod, 1 - alpha_prod






 def 						lowercase_    (      self	:Dict						,					_A	:torch.Tensor						,					_A	:torch.Tensor						,					_A	:torch.Tensor						,					_A	:torch.Tensor						,					)					->      str:







       '''simple docstring'''



       __A													=      timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

       __A						,   __A													=      self._get_alpha_and_beta(_A      )
       __A						,   __A													=      self._get_alpha_and_beta(_A      )

       __A													=      (alpha_prod_t_prev / alpha_prod_t) ** 0.5
       __A													=      -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

       __A													=      (base - b_t * model_output) / a_t

       return model_input, next_model_input.to(base.dtype      )






 def 						lowercase_    (      self	:Any						,					_A	:torch.Tensor						,					_A	:torch.Tensor						,					_A	:torch.Tensor						,					_A	:torch.Tensor						,					)					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

       __A						,   __A													=      self._get_alpha_and_beta(_A      )
       __A						,   __A													=      self._get_alpha_and_beta(_A      )

       __A													=      (alpha_prod_t_prev / alpha_prod_t) ** 0.5
       __A													=      -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
       __A													=      a_t * base + b_t * model_output

       return model_input, next_model_input.to(base.dtype      )






 @torch.no_grad()
 def 						lowercase_    (      self	:List[Any]						,					_A	:torch.Tensor      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      1 / self.vae.config.scaling_factor * latents
       __A													=      self.vae.decode(_A      ).sample
       __A													=      (image / 2 + 0.5).clamp(0						,					1      )
       return image






 @torch.no_grad()
 def 						lowercase_    (      self	:Any						,					_A	:Image.Image						,					_A	:torch.Tensor						,					_A	:torch.Tensor						,					_A	:float						,					_A	:Optional[torch.Generator] = None						,					)					->      List[str]:







       '''simple docstring'''



       __A													=      guidance_scale > 1.0

       __A													=      image.to(device=self.device						,					dtype=text_embeds.dtype      )
       __A													=      self.vae.encode(_A      ).latent_dist.sample(_A      )

       __A													=      self.vae.config.scaling_factor * latent

       __A													=      [latent.clone(), latent.clone()]

       for i, t in tqdm(enumerate(_A      )						,					total=len(_A      )      ):
             __A													=      self.noise_mixing_layer(x=coupled_latents[0]						,					y=coupled_latents[1]      )

             # j - model_input index, k - base index
             for j in range(2      ):
                   __A													=      j ^ 1

                   if self.leapfrog_steps:
                         if i % 2 == 0:
                               __A						,   __A													=      j, k

                   __A													=      coupled_latents[j]
                   __A													=      coupled_latents[k]

                   __A													=      torch.cat([model_input] * 2      ) if do_classifier_free_guidance else model_input

                   __A													=      self.unet(_A						,					_A						,					encoder_hidden_states=_A      ).sample

                   if do_classifier_free_guidance:
                         __A						,   __A													=      noise_pred.chunk(2      )
                         __A													=      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                   __A						,   __A													=      self.noise_step(
                       base=_A						,					model_input=_A						,					model_output=_A						,					timestep=_A						,					)

                   __A													=      model_input

       return coupled_latents






 @torch.no_grad()
 def __call__(      self	:str						,					_A	:str						,					_A	:str						,					_A	:Image.Image						,					_A	:float = 3.0						,					_A	:int = 50						,					_A	:float = 0.8						,					_A	:Optional[str] = None						,					_A	:Optional[torch.Generator] = None						,					_A	:Optional[str] = "pil"						,					)					->      List[str]:







       '''simple docstring'''



       __A													=      guidance_scale > 1.0

       __A													=      self.image_processor.preprocess(_A      )

       __A													=      self._encode_prompt(_A						,					_A						,					_A      )
       __A													=      self._encode_prompt(_A						,					_A						,					_A      )

       self.scheduler.set_timesteps(_A						,					self.device      )

       __A													=      num_inference_steps - int(num_inference_steps * strength      )
       __A													=      self.scheduler.timesteps[t_limit:]
       __A													=      fwd_timesteps.flip(0      )

       __A													=      self.prepare_latents(_A						,					_A						,					_A						,					_A						,					_A      )

       for i, t in tqdm(enumerate(_A      )						,					total=len(_A      )      ):
             # j - model_input index, k - base index
             for k in range(2      ):
                   __A													=      k ^ 1

                   if self.leapfrog_steps:
                         if i % 2 == 1:
                               __A						,   __A													=      j, k

                   __A													=      coupled_latents[j]
                   __A													=      coupled_latents[k]

                   __A													=      torch.cat([model_input] * 2      ) if do_classifier_free_guidance else model_input

                   __A													=      self.unet(_A						,					_A						,					encoder_hidden_states=_A      ).sample

                   if do_classifier_free_guidance:
                         __A						,   __A													=      noise_pred.chunk(2      )
                         __A													=      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                   __A						,   __A													=      self.denoise_step(
                       base=_A						,					model_input=_A						,					model_output=_A						,					timestep=_A						,					)

                   __A													=      model_input

             __A													=      self.denoise_mixing_layer(x=coupled_latents[0]						,					y=coupled_latents[1]      )

       # either one is fine
       __A													=      coupled_latents[0]

       if output_type not in ["latent", "pt", "np", "pil"]:
             __A													=      (
                 F'the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '
                 '`pil`, `np`, `pt`, `latent`'
             )
             deprecate('Unsupported output_type'						,					'1.0.0'						,					_A						,					standard_warn=_A      )
             __A													=      'np'

       if output_type == "latent":
             __A													=      final_latent
       else:
             __A													=      self.decode_latents(_A      )
             __A													=      self.image_processor.postprocess(_A						,					output_type=_A      )

       return image







import copy
import os
from typing import Dict, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings, logging
from ..auto import AutoConfig


UpperCAmelCase_    		=				logging.get_logger(__name__)


UpperCAmelCase_    		=				{
    '''suno/bark-small''': '''https://huggingface.co/suno/bark-small/resolve/main/config.json''',
    '''suno/bark''': '''https://huggingface.co/suno/bark/resolve/main/config.json''',
}

UpperCAmelCase_    		=				'''
    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model
    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)
    architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        block_size (`int`, *optional*, defaults to 1024):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        input_vocab_size (`int`, *optional*, defaults to 10_048):
            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with
            regards to the chosen sub-model.
        output_vocab_size (`int`, *optional*, defaults to 10_048):
            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented
            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought
            with regards to the chosen sub-model.
        num_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the given sub-model.
        num_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer architecture.
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        bias (`bool`, *optional*, defaults to `True`):
            Whether or not to use bias in the linear layers and layer norm layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
'''





class   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  List[str]		="""bark_module"""
     _SCREAMING_SNAKE_CASE  :  str		=["""past_key_values"""]

     _SCREAMING_SNAKE_CASE  :  Tuple		={
         """num_attention_heads""": """num_heads""",
         """num_hidden_layers""": """num_layers""",
         """vocab_size""": """input_vocab_size""",
         """window_size""": """block_size""",
     }



     def __init__(       self					,   lowerCAmelCase__=1024					,   lowerCAmelCase__=10048					,   lowerCAmelCase__=10048					,   lowerCAmelCase__=12					,   lowerCAmelCase__=12					,   lowerCAmelCase__=768					,   lowerCAmelCase__=0.0					,   lowerCAmelCase__=True					,   lowerCAmelCase__=0.02					,   lowerCAmelCase__=True					,   **lowerCAmelCase__					,   ):
            _A=						block_size
            _A=						input_vocab_size
            _A=						output_vocab_size
            _A=						num_layers
            _A=						num_heads
            _A=						hidden_size
            _A=						dropout
            _A=						bias
            _A=						use_cache
            _A=						initializer_range

            super().__init__(**lowerCAmelCase__							)





     @classmethod
     def       a__	(       cls					,   lowerCAmelCase__					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = False					,   lowerCAmelCase__ = False					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = "main"					,   **lowerCAmelCase__					,   ):
            _A=						cache_dir
            _A=						force_download
            _A=						local_files_only
            _A=						revision

            cls._set_token_in_kwargs(lowerCAmelCase__					,   lowerCAmelCase__							)

            _A,      _A=						cls.get_config_dict(lowerCAmelCase__					,   **lowerCAmelCase__							)

            # get the config dict if we are loading from Bark
            if config_dict.get('model_type'							) == "bark":
                   _A=						config_dict[f"{cls.model_type}_config"]

            if "model_type" in config_dict and hasattr(cls					,   'model_type'							) and config_dict["model_type"] != cls.model_type:
                   logger.warning(
                       f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                       f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."							)

            return cls.from_dict(lowerCAmelCase__					,   **lowerCAmelCase__							)





@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkSemanticConfig"""      ,		model="""BarkSemanticModel"""      )      ,		"""
    Example:

    ```python
    >>> from transformers import BarkSemanticConfig, BarkSemanticModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkSemanticConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkSemanticModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""      ,		)
class   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  Union[str, Any]		="""semantic"""





@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkCoarseConfig"""      ,		model="""BarkCoarseModel"""      )      ,		"""
    Example:

    ```python
    >>> from transformers import BarkCoarseConfig, BarkCoarseModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkCoarseConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkCoarseModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""      ,		)
class   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  str		="""coarse_acoustics"""





@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkFineConfig"""      ,		model="""BarkFineModel"""      )      ,		"""
        n_codes_total (`int`, *optional*, defaults to 8):
            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.
        n_codes_given (`int`, *optional*, defaults to 1):
            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics
            sub-models.
    Example:

    ```python
    >>> from transformers import BarkFineConfig, BarkFineModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkFineConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkFineModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""      ,		)
class   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  str		="""fine_acoustics"""



     def __init__(       self					,   lowerCAmelCase__=True					,   lowerCAmelCase__=8					,   lowerCAmelCase__=1					,   **lowerCAmelCase__							):
            _A=						n_codes_total
            _A=						n_codes_given

            super().__init__(tie_word_embeddings=lowerCAmelCase__					,   **lowerCAmelCase__							)





class   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  Optional[Any]		="""bark"""
     _SCREAMING_SNAKE_CASE  :  Any		=True



     def __init__(       self					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__=0.02					,   **lowerCAmelCase__					,   ):
            if semantic_config is None:
                   _A=						{}
                   logger.info('semantic_config is None. initializing the semantic model with default values.'							)

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

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

            if codec_config is None:
                   _A=						{}
                   logger.info('codec_config is None. initializing the codec model with default values.'							)

            _A=						BarkSemanticConfig(**lowerCAmelCase__							)
            _A=						BarkCoarseConfig(**lowerCAmelCase__							)
            _A=						BarkFineConfig(**lowerCAmelCase__							)
            _A=						AutoConfig.for_model(**lowerCAmelCase__							)

            _A=						initializer_range

            super().__init__(**lowerCAmelCase__							)



     @classmethod
     def       a__	(       cls					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   **lowerCAmelCase__					,   ):
            return cls(
                semantic_config=semantic_config.to_dict()					,   coarse_acoustics_config=coarse_acoustics_config.to_dict()					,   fine_acoustics_config=fine_acoustics_config.to_dict()					,   codec_config=codec_config.to_dict()					,   **lowerCAmelCase__					,   )





     def       a__	(       self							):
            _A=						copy.deepcopy(self.__dict__							)

            _A=						self.semantic_config.to_dict()
            _A=						self.coarse_acoustics_config.to_dict()
            _A=						self.fine_acoustics_config.to_dict()
            _A=						self.codec_config.to_dict()

            _A=						self.__class__.model_type
            return output