





"""simple docstring"""


import copy

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


a__   :       Union[str, Any]   						=			logging.get_logger(__name__)

a__   :       List[Any]   						=			{
    """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        lowercase_			(       __lowerCAmelCase							):
							snake_case__			:							Union[str, Any]										=       """musicgen_decoder"""
							snake_case__			:							List[Any]										=       ["""past_key_values"""]

							def __init__(     self				,   __SCREAMING_SNAKE_CASE=2048				,   __SCREAMING_SNAKE_CASE=2048				,   __SCREAMING_SNAKE_CASE=24				,   __SCREAMING_SNAKE_CASE=4096				,   __SCREAMING_SNAKE_CASE=16				,   __SCREAMING_SNAKE_CASE=0.0				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE="gelu"				,   __SCREAMING_SNAKE_CASE=1024				,   __SCREAMING_SNAKE_CASE=0.1				,   __SCREAMING_SNAKE_CASE=0.0				,   __SCREAMING_SNAKE_CASE=0.0				,   __SCREAMING_SNAKE_CASE=0.02				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=4				,   __SCREAMING_SNAKE_CASE=2048				,   __SCREAMING_SNAKE_CASE=2048				,   __SCREAMING_SNAKE_CASE=None				,   __SCREAMING_SNAKE_CASE=False				,   **__SCREAMING_SNAKE_CASE				,   ):






								'''simple docstring'''

								snake_case__      							=			vocab_size
								snake_case__      							=			max_position_embeddings
								snake_case__      							=			hidden_size
								snake_case__      							=			ffn_dim
								snake_case__      							=			num_hidden_layers
								snake_case__      							=			num_attention_heads
								snake_case__      							=			dropout
								snake_case__      							=			attention_dropout
								snake_case__      							=			activation_dropout
								snake_case__      							=			activation_function
								snake_case__      							=			initializer_factor
								snake_case__      							=			layerdrop
								snake_case__      							=			use_cache
								snake_case__      							=			scale_embedding  # scale factor will be sqrt(d_model) if True
								snake_case__      							=			num_codebooks
								super().__init__(
								    pad_token_id=__SCREAMING_SNAKE_CASE				,   bos_token_id=__SCREAMING_SNAKE_CASE				,   eos_token_id=__SCREAMING_SNAKE_CASE				,   tie_word_embeddings=__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE				,   )



class        lowercase_			(       __lowerCAmelCase							):
							snake_case__			:							Dict										=       """musicgen"""
							snake_case__			:							str										=       True

							def __init__(     self				,   **__SCREAMING_SNAKE_CASE       ):






								'''simple docstring'''

								super().__init__(**__SCREAMING_SNAKE_CASE       )
								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"       )

								snake_case__      							=			kwargs.pop("text_encoder"       )
								snake_case__      							=			text_encoder_config.pop("model_type"       )

								snake_case__      							=			kwargs.pop("audio_encoder"       )
								snake_case__      							=			audio_encoder_config.pop("model_type"       )

								snake_case__      							=			kwargs.pop("decoder"       )

								snake_case__      							=			AutoConfig.for_model(__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       )
								snake_case__      							=			AutoConfig.for_model(__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       )
								snake_case__      							=			MusicgenDecoderConfig(**__SCREAMING_SNAKE_CASE       )
								snake_case__      							=			True

							@classmethod
							def 						_SCREAMING_SNAKE_CASE(     cls				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE				,   ):






								'''simple docstring'''

								return cls(
								    text_encoder=text_encoder_config.to_dict()				,   audio_encoder=audio_encoder_config.to_dict()				,   decoder=decoder_config.to_dict()				,   **__SCREAMING_SNAKE_CASE				,   )

							def 						_SCREAMING_SNAKE_CASE(     self       ):






								'''simple docstring'''

								snake_case__      							=			copy.deepcopy(self.__dict__       )
								snake_case__      							=			self.text_encoder.to_dict()
								snake_case__      							=			self.audio_encoder.to_dict()
								snake_case__      							=			self.decoder.to_dict()
								snake_case__      							=			self.__class__.model_type
								return output








"""simple docstring"""


from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)



class        lowercase_			(       __lowerCAmelCase							):

       def __init__(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        super().__init__()

        self.register_modules(vae=__SCREAMING_SNAKE_CASE				,   text_encoder=__SCREAMING_SNAKE_CASE				,   tokenizer=__SCREAMING_SNAKE_CASE				,   unet=__SCREAMING_SNAKE_CASE				,   scheduler=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        with torch.no_grad():
         snake_case__      							=			self.vae.encode(tfms.ToTensor()(__SCREAMING_SNAKE_CASE       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
         snake_case__      							=			0.18215 * latent.latent_dist.sample()
        return latent

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			(1 / 0.18215) * latent
        with torch.no_grad():
         snake_case__      							=			self.vae.decode(__SCREAMING_SNAKE_CASE       ).sample
        snake_case__      							=			(img / 2 + 0.5).clamp(0				,   1       )
        snake_case__      							=			img.detach().cpu().permute(0				,   2				,   3				,   1       ).numpy()
        snake_case__      							=			(img * 255).round().astype("uint8"       )
        return Image.fromarray(img[0]       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			self.tokenizer(
            __SCREAMING_SNAKE_CASE				,   padding="max_length"				,   max_length=self.tokenizer.model_max_length				,   truncation=__SCREAMING_SNAKE_CASE				,   return_tensors="pt"				,   )

        snake_case__      							=			self.text_encoder(text_input.input_ids.to(self.device       )       )[0]

        snake_case__      							=			self.tokenizer(
            ""				,   padding="max_length"				,   max_length=self.tokenizer.model_max_length				,   truncation=__SCREAMING_SNAKE_CASE				,   return_tensors="pt"				,   )

        snake_case__      							=			self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

        return torch.cat([uncond_embedding, text_embedding]       )

       def __call__(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE = 0.3				,   __SCREAMING_SNAKE_CASE = 0.6				,   __SCREAMING_SNAKE_CASE = 0.5				,   __SCREAMING_SNAKE_CASE = 42				,   __SCREAMING_SNAKE_CASE = 50				,   __SCREAMING_SNAKE_CASE = 7.5				,   ):






        '''simple docstring'''

        snake_case__      							=			steps - int(kmin * steps       )
        snake_case__      							=			steps - int(kmax * steps       )

        snake_case__      							=			self.prep_text(__SCREAMING_SNAKE_CASE       )

        self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE       )

        snake_case__							,				snake_case__      							=			img.size
        snake_case__      							=			self.encode(__SCREAMING_SNAKE_CASE       )

        torch.manual_seed(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)				,   ).to(self.device       )

        snake_case__      							=			self.scheduler.add_noise(
            __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   timesteps=self.scheduler.timesteps[tmax]				,   )

        snake_case__      							=			torch.cat([latents] * 2       )

        snake_case__      							=			self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   self.scheduler.timesteps[tmax]       )

        with torch.no_grad():
         snake_case__      							=			self.unet(
             __SCREAMING_SNAKE_CASE				,   self.scheduler.timesteps[tmax]				,   encoder_hidden_states=__SCREAMING_SNAKE_CASE				,   ).sample

        snake_case__							,				snake_case__      							=			pred.chunk(2       )
        snake_case__      							=			pred_uncond + guidance_scale * (pred_text - pred_uncond)

        snake_case__      							=			self.scheduler.step(__SCREAMING_SNAKE_CASE				,   self.scheduler.timesteps[tmax]				,   __SCREAMING_SNAKE_CASE       ).prev_sample

        for i, t in enumerate(tqdm(self.scheduler.timesteps       )       ):
         if i > tmax:
          if i < tmin:  # layout generation phase
           snake_case__      							=			self.scheduler.add_noise(
               __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   timesteps=__SCREAMING_SNAKE_CASE				,   )

           snake_case__      							=			(mix_factor * latents) + (
               1 - mix_factor
           ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
           snake_case__      							=			torch.cat([input] * 2       )

          else:  # content generation phase
           snake_case__      							=			torch.cat([latents] * 2       )

          snake_case__      							=			self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

          with torch.no_grad():
           snake_case__      							=			self.unet(
               __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   encoder_hidden_states=__SCREAMING_SNAKE_CASE				,   ).sample

          snake_case__							,				snake_case__      							=			pred.chunk(2       )
          snake_case__      							=			pred_uncond + guidance_scale * (pred_text - pred_uncond)

          snake_case__      							=			self.scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ).prev_sample

        return self.decode(__SCREAMING_SNAKE_CASE       )

