
'''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        SCREAMING_SNAKE_CASE__	(			snake_case_):







				def __init__(    self		,							A_		,							A_		,							A_		,							A_		,							A_		,							)->						str:







											'''simple docstring'''






											super().__init__()

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







				def 		UpperCAmelCase_  (    self		,							A_			)->						str:







											'''simple docstring'''






											with torch.no_grad():
																		UpperCamelCase					       =			self.vae.encode(tfms.ToTensor()(A_			).unsqueeze(0			).to(self.device			) * 2 - 1			)
																		UpperCamelCase					       =			0.18_215 * latent.latent_dist.sample()
											return latent







				def 		UpperCAmelCase_  (    self		,							A_			)->						Any:







											'''simple docstring'''






											UpperCamelCase					       =			(1 / 0.18_215) * latent
											with torch.no_grad():
																		UpperCamelCase					       =			self.vae.decode(A_			).sample
											UpperCamelCase					       =			(img / 2 + 0.5).clamp(0		,							1			)
											UpperCamelCase					       =			img.detach().cpu().permute(0		,							2		,							3		,							1			).numpy()
											UpperCamelCase					       =			(img * 255).round().astype('uint8'			)
											return Image.fromarray(img[0]			)







				def 		UpperCAmelCase_  (    self		,							A_			)->						Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase					       =			self.tokenizer(
											    A_		,							padding='max_length'		,							max_length=self.tokenizer.model_max_length		,							truncation=A_		,							return_tensors='pt'		,							)

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

											UpperCamelCase					       =			self.tokenizer(
											    ''		,							padding='max_length'		,							max_length=self.tokenizer.model_max_length		,							truncation=A_		,							return_tensors='pt'		,							)

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

											return torch.cat([uncond_embedding, text_embedding]			)







				def __call__(    self		,							A_		,							A_		,							A_ = 0.3		,							A_ = 0.6		,							A_ = 0.5		,							A_ = 42		,							A_ = 50		,							A_ = 7.5		,							)->						Image.Image:







											'''simple docstring'''






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

											UpperCamelCase					       =			self.prep_text(A_			)

											self.scheduler.set_timesteps(A_			)

											UpperCamelCase       ,     UpperCamelCase					       =			img.size
											UpperCamelCase					       =			self.encode(A_			)

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

											UpperCamelCase					       =			self.scheduler.add_noise(
											    A_		,							A_		,							timesteps=self.scheduler.timesteps[tmax]		,							)

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

											UpperCamelCase					       =			self.scheduler.scale_model_input(A_		,							self.scheduler.timesteps[tmax]			)

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

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

											UpperCamelCase					       =			self.scheduler.step(A_		,							self.scheduler.timesteps[tmax]		,							A_			).prev_sample

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

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

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

																									UpperCamelCase					       =			self.scheduler.scale_model_input(A_		,							A_			)

																									with torch.no_grad():
																																UpperCamelCase					       =			self.unet(
																																    A_		,							A_		,							encoder_hidden_states=A_		,							).sample

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

																									UpperCamelCase					       =			self.scheduler.step(A_		,							A_		,							A_			).prev_sample

											return self.decode(A_			)






'''simple docstring'''
import copy

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


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

lowerCAmelCase :  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        SCREAMING_SNAKE_CASE__	(			snake_case_):
				lowerCAmelCase_			       =				"""musicgen_decoder"""
				lowerCAmelCase_			       =				["""past_key_values"""]







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







											'''simple docstring'''






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


class        SCREAMING_SNAKE_CASE__	(			snake_case_):
				lowerCAmelCase_			       =				"""musicgen"""
				lowerCAmelCase_			       =				True







				def __init__(    self		,							**A_			)->						Union[str, Any]:







											'''simple docstring'''






											super().__init__(**A_			)
											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					       =			kwargs.pop('text_encoder'			)
											UpperCamelCase					       =			text_encoder_config.pop('model_type'			)

											UpperCamelCase					       =			kwargs.pop('audio_encoder'			)
											UpperCamelCase					       =			audio_encoder_config.pop('model_type'			)

											UpperCamelCase					       =			kwargs.pop('decoder'			)

											UpperCamelCase					       =			AutoConfig.for_model(A_		,							**A_			)
											UpperCamelCase					       =			AutoConfig.for_model(A_		,							**A_			)
											UpperCamelCase					       =			MusicgenDecoderConfig(**A_			)
											UpperCamelCase					       =			True







				@classmethod
				def 		UpperCAmelCase_  (    cls		,							A_		,							A_		,							A_		,							**A_		,							)->						Tuple:







											'''simple docstring'''






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







				def 		UpperCAmelCase_  (    self			)->						Optional[Any]:







											'''simple docstring'''






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




