'''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      (    __lowercase):







		"""simple docstring"""






		def __init__( self		,    __A		,    __A		,    __A		,    __A		,    __A		,    )			->  Any:
								super().__init__()

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






		def 		UpperCamelCase__							( self		,    __A					)			->  List[Any]:
								with torch.no_grad():
														_lowerCAmelCase					    =self.vae.encode(tfms.ToTensor()(__A					).unsqueeze(0					).to(self.device					) * 2 - 1					)
														_lowerCAmelCase					    =0.18_215 * latent.latent_dist.sample()
								return latent






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






		def 		UpperCamelCase__							( self		,    __A					)			->  str:
								_lowerCAmelCase					    =self.tokenizer(
								    __A		,    padding='max_length'		,    max_length=self.tokenizer.model_max_length		,    truncation=__A		,    return_tensors='pt'		,    )

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

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

								_lowerCAmelCase					    =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:
								_lowerCAmelCase					    =steps - int(kmin * steps					)
								_lowerCAmelCase					    =steps - int(kmax * steps					)

								_lowerCAmelCase					    =self.prep_text(__A					)

								self.scheduler.set_timesteps(__A					)

								_lowerCAmelCase    ,					_lowerCAmelCase					    =img.size
								_lowerCAmelCase					    =self.encode(__A					)

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

								_lowerCAmelCase					    =self.scheduler.add_noise(
								    __A		,    __A		,    timesteps=self.scheduler.timesteps[tmax]		,    )

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

								_lowerCAmelCase					    =self.scheduler.scale_model_input(__A		,    self.scheduler.timesteps[tmax]					)

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

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

								_lowerCAmelCase					    =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
																										_lowerCAmelCase					    =self.scheduler.add_noise(
																										    __A		,    __A		,    timesteps=__A		,    )

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

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

																				_lowerCAmelCase					    =self.scheduler.scale_model_input(__A		,    __A					)

																				with torch.no_grad():
																										_lowerCAmelCase					    =self.unet(
																										    __A		,    __A		,    encoder_hidden_states=__A		,    ).sample

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

																				_lowerCAmelCase					    =self.scheduler.step(__A		,    __A		,    __A					).prev_sample

								return self.decode(__A					)






'''simple docstring'''

import copy
from typing import Dict

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


lowercase_  		=			logging.get_logger(__name__)
class 	SCREAMING_SNAKE_CASE      (    __lowercase):







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






		def __init__( self		,    __A=1_0000		,    __A=True		,    __A=768		,    __A=False		,    __A=False		,    __A=False		,    __A=False		,    __A=0.7		,    __A=True		,    __A=1_0048		,    __A=12_9595		,    __A=12_9599		,    __A=1_0000		,    __A=256		,    __A=49.9		,    **__A		,    )			->  Any:
								super().__init__(
								    temperature=__A		,    do_sample=__A		,    eos_token_id=__A		,    renormalize_logits=__A		,    max_new_tokens=__A		,    output_scores=__A		,    return_dict_in_generate=__A		,    output_hidden_states=__A		,    output_attentions=__A		,    **__A		,    )

								_lowerCAmelCase					    =text_encoding_offset
								_lowerCAmelCase					    =text_pad_token
								_lowerCAmelCase					    =eos_token_id
								_lowerCAmelCase					    =semantic_infer_token
								_lowerCAmelCase					    =semantic_vocab_size
								_lowerCAmelCase					    =max_input_semantic_length
								_lowerCAmelCase					    =semantic_rate_hz

class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   Any							=				'coarse_acoustics'






		def __init__( self		,    __A=True		,    __A=False		,    __A=False		,    __A=False		,    __A=False		,    __A=0.7		,    __A=True		,    __A=1_2048		,    __A=75		,    __A=2		,    __A=1_2050		,    __A=256		,    __A = 630		,    __A = 60		,    **__A		,    )			->  Union[str, Any]:
								super().__init__(
								    temperature=__A		,    do_sample=__A		,    renormalize_logits=__A		,    output_scores=__A		,    return_dict_in_generate=__A		,    output_hidden_states=__A		,    output_attentions=__A		,    **__A		,    )

								_lowerCAmelCase					    =coarse_semantic_pad_token
								_lowerCAmelCase					    =coarse_rate_hz
								_lowerCAmelCase					    =n_coarse_codebooks
								_lowerCAmelCase					    =coarse_infer_token
								_lowerCAmelCase					    =max_coarse_input_length
								_lowerCAmelCase					    =max_coarse_history
								_lowerCAmelCase					    =sliding_window_len

class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   str							=				'fine_acoustics'






		def __init__( self		,    __A=0.5		,    __A=512		,    __A=1024		,    __A=8		,    **__A		,    )			->  int:
								super().__init__(temperature=__A					)

								_lowerCAmelCase					    =max_fine_history_length
								_lowerCAmelCase					    =max_fine_input_length
								_lowerCAmelCase					    =n_fine_codebooks

class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   int							=				'bark'
		lowercase :   Optional[Any]							=				True






		def __init__( self		,    __A = None		,    __A = None		,    __A = None		,    __A=2_4000		,    __A=1024		,    **__A		,    )			->  Optional[int]:
								if semantic_config is None:
														_lowerCAmelCase					    ={}
														logger.info('semantic_config is None. initializing the semantic model with default values.'					)

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

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

								_lowerCAmelCase					    =BarkSemanticGenerationConfig(**__A					)
								_lowerCAmelCase					    =BarkCoarseGenerationConfig(**__A					)
								_lowerCAmelCase					    =BarkFineGenerationConfig(**__A					)

								_lowerCAmelCase					    =sample_rate
								_lowerCAmelCase					    =codebook_size






		@classmethod
		def 		UpperCamelCase__							( cls		,    __A		,    __A		,    __A		,    **__A		,    )			->  List[Any]:
								return cls(
								    semantic_config=semantic_config.to_dict()		,    coarse_acoustics_config=coarse_acoustics_config.to_dict()		,    fine_acoustics_config=fine_acoustics_config.to_dict()		,    **__A		,    )






		def 		UpperCamelCase__							( self					)			->  Optional[int]:
								_lowerCAmelCase					    =copy.deepcopy(self.__dict__					)

								_lowerCAmelCase					    =self.semantic_config.to_dict()
								_lowerCAmelCase					    =self.coarse_acoustics_config.to_dict()
								_lowerCAmelCase					    =self.fine_acoustics_config.to_dict()

								_lowerCAmelCase					    =self.__class__.model_type
								return output





