





import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
					from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


UpperCamelCase__											= logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCamelCase__											= 256

class        __SCREAMING_SNAKE_CASE					(     _a       ):
							snake_case     :      Optional[Any]						     =       ["""melgan"""]






							def __init__(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , ):
														super().__init__()

														# From MELGAN
														UpperCamelCase__				     =			math.log(1E-5  )  # Matches MelGAN training.
														UpperCamelCase__				     =			4.0  # Largest value for most examples
														UpperCamelCase__				     =			128

														self.register_modules(
														    notes_encoder=__lowerCAmelCase  , continuous_encoder=__lowerCAmelCase  , decoder=__lowerCAmelCase  , scheduler=__lowerCAmelCase  , melgan=__lowerCAmelCase  , )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase=(-1.0, 1.0)  , __lowerCAmelCase=False  ):
														UpperCamelCase__     ,     UpperCamelCase__				     =			output_range
														if clip:
																					UpperCamelCase__				     =			torch.clip(__lowerCAmelCase  , self.min_value  , self.max_value  )
														# Scale to [0, 1].
														UpperCamelCase__				     =			(features - self.min_value) / (self.max_value - self.min_value)
														# Scale to [min_out, max_out].
														return zero_one * (max_out - min_out) + min_out






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase=(-1.0, 1.0)  , __lowerCAmelCase=False  ):
														UpperCamelCase__     ,     UpperCamelCase__				     =			input_range
														UpperCamelCase__				     =			torch.clip(__lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ) if clip else outputs
														# Scale to [0, 1].
														UpperCamelCase__				     =			(outputs - min_out) / (max_out - min_out)
														# Scale to [self.min_value, self.max_value].
														return zero_one * (self.max_value - self.min_value) + self.min_value






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			input_tokens > 0
														UpperCamelCase__     ,     UpperCamelCase__				     =			self.notes_encoder(
														    encoder_input_tokens=__lowerCAmelCase  , encoder_inputs_mask=__lowerCAmelCase  )

														UpperCamelCase__     ,     UpperCamelCase__				     =			self.continuous_encoder(
														    encoder_inputs=__lowerCAmelCase  , encoder_inputs_mask=__lowerCAmelCase  )

														return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			noise_time
														if not torch.is_tensor(__lowerCAmelCase  ):
																					UpperCamelCase__				     =			torch.tensor([timesteps]  , dtype=torch.long  , device=input_tokens.device  )
														elif torch.is_tensor(__lowerCAmelCase  ) and len(timesteps.shape  ) == 0:
																					UpperCamelCase__				     =			timesteps[None].to(input_tokens.device  )

														# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
														UpperCamelCase__				     =			timesteps * torch.ones(input_tokens.shape[0]  , dtype=timesteps.dtype  , device=timesteps.device  )

														UpperCamelCase__				     =			self.decoder(
														    encodings_and_masks=__lowerCAmelCase  , decoder_input_tokens=__lowerCAmelCase  , decoder_noise_time=__lowerCAmelCase  )
														return logits







							@torch.no_grad()
							def __call__(						self  , __lowerCAmelCase  , __lowerCAmelCase = None  , __lowerCAmelCase = 100  , __lowerCAmelCase = True  , __lowerCAmelCase = "numpy"  , __lowerCAmelCase = None  , __lowerCAmelCase = 1  , ):
														if (callback_steps is None) or (
														    callback_steps is not None and (not isinstance(__lowerCAmelCase  , __lowerCAmelCase  ) or callback_steps <= 0)
														):
																					raise ValueError(
																					    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
																					    f""" {type(__lowerCAmelCase  )}."""  )

														UpperCamelCase__				     =			np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]  , dtype=np.floataa  )
														UpperCamelCase__				     =			np.zeros([1, 0, self.n_dims]  , np.floataa  )
														UpperCamelCase__				     =			torch.ones((1, TARGET_FEATURE_LENGTH)  , dtype=__lowerCAmelCase  , device=self.device  )

														for i, encoder_input_tokens in enumerate(__lowerCAmelCase  ):
																					if i == 0:
																												UpperCamelCase__				     =			torch.from_numpy(pred_mel[:1].copy()  ).to(
																												    device=self.device  , dtype=self.decoder.dtype  )
																												# The first chunk has no previous context.
																												UpperCamelCase__				     =			torch.zeros((1, TARGET_FEATURE_LENGTH)  , dtype=__lowerCAmelCase  , device=self.device  )
																					else:
																												# The full song pipeline does not feed in a context feature, so the mask
																												# will be all 0s after the feature converter. Because we know we're
																												# feeding in a full context chunk from the previous prediction, set it
																												# to all 1s.
																												UpperCamelCase__				     =			ones

																					UpperCamelCase__				     =			self.scale_features(
																					    __lowerCAmelCase  , output_range=[-1.0, 1.0]  , clip=__lowerCAmelCase  )

																					UpperCamelCase__				     =			self.encode(
																					    input_tokens=torch.IntTensor([encoder_input_tokens]  ).to(device=self.device  )  , continuous_inputs=__lowerCAmelCase  , continuous_mask=__lowerCAmelCase  , )

																					# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
																					UpperCamelCase__				     =			randn_tensor(
																					    shape=encoder_continuous_inputs.shape  , generator=__lowerCAmelCase  , device=self.device  , dtype=self.decoder.dtype  , )

																					# set step values
																					self.scheduler.set_timesteps(__lowerCAmelCase  )

																					# Denoising diffusion loop
																					for j, t in enumerate(self.progress_bar(self.scheduler.timesteps  )  ):
																												UpperCamelCase__				     =			self.decode(
																												    encodings_and_masks=__lowerCAmelCase  , input_tokens=__lowerCAmelCase  , noise_time=t / self.scheduler.config.num_train_timesteps  , )

																												# Compute previous output: x_t -> x_t-1
																												UpperCamelCase__				     =			self.scheduler.step(__lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , generator=__lowerCAmelCase  ).prev_sample

																					UpperCamelCase__				     =			self.scale_to_features(__lowerCAmelCase  , input_range=[-1.0, 1.0]  )
																					UpperCamelCase__				     =			mel[:1]
																					UpperCamelCase__				     =			mel.cpu().float().numpy()

																					UpperCamelCase__				     =			np.concatenate([full_pred_mel, pred_mel[:1]]  , axis=1  )

																					# call the callback, if provided
																					if callback is not None and i % callback_steps == 0:
																												callback(__lowerCAmelCase  , __lowerCAmelCase  )

																					logger.info("""Generated segment"""  , __lowerCAmelCase  )

														if output_type == "numpy" and not is_onnx_available():
																					raise ValueError(
																					    """Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'."""  )
														elif output_type == "numpy" and self.melgan is None:
																					raise ValueError(
																					    """Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'."""  )

														if output_type == "numpy":
																					UpperCamelCase__				     =			self.melgan(input_features=full_pred_mel.astype(np.floataa  )  )
														else:
																					UpperCamelCase__				     =			full_pred_mel

														if not return_dict:
																					return (output,)

														return AudioPipelineOutput(audios=__lowerCAmelCase  )











import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
					UpperCamelCase__											= argparse.ArgumentParser()

					parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

					parser.add_argument(
					    "--txt2img_unclip",
					    default="kakaobrain/karlo-v1-alpha",
					    type=str,
					    required=False,
					    help="The pretrained txt2img unclip.",
					)

					UpperCamelCase__											= parser.parse_args()

					UpperCamelCase__											= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

					UpperCamelCase__											= CLIPImageProcessor()
					UpperCamelCase__											= CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

					UpperCamelCase__											= UnCLIPImageVariationPipeline(
					    decoder=txtaimg.decoder,
					    text_encoder=txtaimg.text_encoder,
					    tokenizer=txtaimg.tokenizer,
					    text_proj=txtaimg.text_proj,
					    feature_extractor=feature_extractor,
					    image_encoder=image_encoder,
					    super_res_first=txtaimg.super_res_first,
					    super_res_last=txtaimg.super_res_last,
					    decoder_scheduler=txtaimg.decoder_scheduler,
					    super_res_scheduler=txtaimg.super_res_scheduler,
					)

					imgaimg.save_pretrained(args.dump_path)




