






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class    a   (			lowercase__       ):



      """simple docstring"""


      a  :			UNetaDModel
      a  :			PNDMScheduler
      def __init__(				self :      Union[str, Any]		,				__lowercase :      UNetaDModel		,				__lowercase :      PNDMScheduler )							->				Union[str, Any]:
        super().__init__()

        __UpperCAmelCase							:     List[Any]									=  PNDMScheduler.from_config(scheduler.config )

        self.register_modules(unet=__lowercase		,				scheduler=__lowercase )




      @torch.no_grad()
      def __call__(				self :      List[str]		,				__lowercase :      int = 1		,				__lowercase :      int = 50		,				__lowercase :      Optional[Union[torch.Generator, List[torch.Generator]]] = None		,				__lowercase :      Optional[str] = "pil"		,				__lowercase :      bool = True		,				**__lowercase :      str		,				)							->				Union[ImagePipelineOutput, Tuple]:
        __UpperCAmelCase							:     List[Any]									=  randn_tensor(
            (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,				generator=__lowercase		,				device=self.device		,				)

        self.scheduler.set_timesteps(__lowercase )
        for t in self.progress_bar(self.scheduler.timesteps ):
          __UpperCAmelCase							:     Tuple									=  self.unet(__lowercase		,				__lowercase ).sample

          __UpperCAmelCase							:     Optional[int]									=  self.scheduler.step(__lowercase		,				__lowercase		,				__lowercase ).prev_sample

        __UpperCAmelCase							:     Tuple									=  (image / 2 + 0.5).clamp(0		,				1 )
        __UpperCAmelCase							:     List[Any]									=  image.cpu().permute(0		,				2		,				3		,				1 ).numpy()
        if output_type == "pil":
          __UpperCAmelCase							:     Any									=  self.numpy_to_pil(__lowercase )

        if not return_dict:
          return (image,)

        return ImagePipelineOutput(images=__lowercase )










import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


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

a		:	int      					=			{
    "facebook/musicgen-small": "https://huggingface.co/facebook/musicgen-small/resolve/main/config.json",
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}
class    a   (			lowercase__       ):



      """simple docstring"""


      a  :			Optional[int]								=     'musicgen_decoder'
      a  :			Optional[int]								=     ['past_key_values']
      def __init__(				self :      Optional[Any]		,				__lowercase :      str=2048		,				__lowercase :      Optional[int]=2048		,				__lowercase :      List[Any]=24		,				__lowercase :      Optional[Any]=4096		,				__lowercase :      Dict=16		,				__lowercase :      List[Any]=0.0		,				__lowercase :      Tuple=True		,				__lowercase :      List[Any]="gelu"		,				__lowercase :      Any=1024		,				__lowercase :      Optional[Any]=0.1		,				__lowercase :      List[Any]=0.0		,				__lowercase :      int=0.0		,				__lowercase :      List[str]=0.02		,				__lowercase :      Dict=False		,				__lowercase :      List[str]=4		,				__lowercase :      List[str]=2048		,				__lowercase :      List[Any]=2048		,				__lowercase :      Union[str, Any]=None		,				__lowercase :      List[str]=False		,				**__lowercase :      int		,				)							->				str:
        __UpperCAmelCase							:     Union[str, Any]									=  vocab_size
        __UpperCAmelCase							:     Dict									=  max_position_embeddings
        __UpperCAmelCase							:     str									=  hidden_size
        __UpperCAmelCase							:     Optional[Any]									=  ffn_dim
        __UpperCAmelCase							:     List[Any]									=  num_hidden_layers
        __UpperCAmelCase							:     Any									=  num_attention_heads
        __UpperCAmelCase							:     List[Any]									=  dropout
        __UpperCAmelCase							:     List[str]									=  attention_dropout
        __UpperCAmelCase							:     List[Any]									=  activation_dropout
        __UpperCAmelCase							:     int									=  activation_function
        __UpperCAmelCase							:     Optional[int]									=  initializer_factor
        __UpperCAmelCase							:     List[Any]									=  layerdrop
        __UpperCAmelCase							:     Tuple									=  use_cache
        __UpperCAmelCase							:     Tuple									=  scale_embedding  # scale factor will be sqrt(d_model) if True
        __UpperCAmelCase							:     int									=  num_codebooks
        super().__init__(
            pad_token_id=__lowercase		,				bos_token_id=__lowercase		,				eos_token_id=__lowercase		,				tie_word_embeddings=__lowercase		,				**__lowercase		,				)


class    a   (			lowercase__       ):



      """simple docstring"""


      a  :			Tuple								=     'musicgen'
      a  :			int								=     True
      def __init__(				self :      Any		,				**__lowercase :      Tuple )							->				Union[str, Any]:
        super().__init__(**__lowercase )
        if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
          raise ValueError("""Config has to be initialized with text_encoder, audio_encoder and decoder config""" )

        __UpperCAmelCase							:     int									=  kwargs.pop("""text_encoder""" )
        __UpperCAmelCase							:     str									=  text_encoder_config.pop("""model_type""" )

        __UpperCAmelCase							:     Tuple									=  kwargs.pop("""audio_encoder""" )
        __UpperCAmelCase							:     Optional[Any]									=  audio_encoder_config.pop("""model_type""" )

        __UpperCAmelCase							:     Optional[Any]									=  kwargs.pop("""decoder""" )

        __UpperCAmelCase							:     Any									=  AutoConfig.for_model(__lowercase		,				**__lowercase )
        __UpperCAmelCase							:     Dict									=  AutoConfig.for_model(__lowercase		,				**__lowercase )
        __UpperCAmelCase							:     Any									=  MusicgenDecoderConfig(**__lowercase )
        __UpperCAmelCase							:     int									=  True
      @classmethod
      def      UpperCAmelCase				(				cls :      Optional[Any]		,				__lowercase :      PretrainedConfig		,				__lowercase :      PretrainedConfig		,				__lowercase :      MusicgenDecoderConfig		,				**__lowercase :      List[Any]		,				)							->				List[str]:
        return cls(
            text_encoder=text_encoder_config.to_dict()		,				audio_encoder=audio_encoder_config.to_dict()		,				decoder=decoder_config.to_dict()		,				**__lowercase		,				)




      def      UpperCAmelCase				(				self :      Any )							->				int:
        __UpperCAmelCase							:     Optional[Any]									=  copy.deepcopy(self.__dict__ )
        __UpperCAmelCase							:     List[Any]									=  self.text_encoder.to_dict()
        __UpperCAmelCase							:     str									=  self.audio_encoder.to_dict()
        __UpperCAmelCase							:     List[str]									=  self.decoder.to_dict()
        __UpperCAmelCase							:     List[str]									=  self.__class__.model_type
        return output


