




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


_lowerCamelCase  :			List[str]        =       logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowerCamelCase  :			Optional[Any]        =       256

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    int         =   ["""melgan"""]







				def __init__(							self   :					Any      ,     _lowerCamelCase   :					SpectrogramNotesEncoder      ,     _lowerCamelCase   :					SpectrogramContEncoder      ,     _lowerCamelCase   :					TaFilmDecoder      ,     _lowerCamelCase   :					DDPMScheduler      ,     _lowerCamelCase   :					OnnxRuntimeModel if is_onnx_available() else Any      ,     ):



					"""simple docstring"""

					super().__init__()

					# From MELGAN
					A_  :				List[str]	   =		math.log(1E-5       )  # Matches MelGAN training.
					A_  :				int	   =		4.0  # Largest value for most examples
					A_  :				List[Any]	   =		1_28

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







				def 					a_  (							self   :					Tuple      ,     _lowerCamelCase   :					Tuple      ,     _lowerCamelCase   :					int=(-1.0, 1.0)      ,     _lowerCamelCase   :					List[str]=False       ):



					"""simple docstring"""

					A_  :				Any	   =		output_range
					if clip:
						A_  :				List[str]	   =		torch.clip(_lowerCamelCase      ,     self.min_value      ,     self.max_value       )
					# Scale to [0, 1].
					A_  :				Optional[int]	   =		(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 					a_  (							self   :					Tuple      ,     _lowerCamelCase   :					Optional[int]      ,     _lowerCamelCase   :					Optional[Any]=(-1.0, 1.0)      ,     _lowerCamelCase   :					Any=False       ):



					"""simple docstring"""

					A_  :				List[str]	   =		input_range
					A_  :				Union[str, Any]	   =		torch.clip(_lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase       ) if clip else outputs
					# Scale to [0, 1].
					A_  :				Dict	   =		(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 					a_  (							self   :					Any      ,     _lowerCamelCase   :					Union[str, Any]      ,     _lowerCamelCase   :					Any      ,     _lowerCamelCase   :					Any       ):



					"""simple docstring"""

					A_  :				str	   =		input_tokens > 0
					A_  :				Any	   =		self.notes_encoder(
					    encoder_input_tokens=_lowerCamelCase      ,     encoder_inputs_mask=_lowerCamelCase       )

					A_  :				str	   =		self.continuous_encoder(
					    encoder_inputs=_lowerCamelCase      ,     encoder_inputs_mask=_lowerCamelCase       )

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







				def 					a_  (							self   :					List[str]      ,     _lowerCamelCase   :					str      ,     _lowerCamelCase   :					Union[str, Any]      ,     _lowerCamelCase   :					Union[str, Any]       ):



					"""simple docstring"""

					A_  :				List[Any]	   =		noise_time
					if not torch.is_tensor(_lowerCamelCase       ):
						A_  :				Any	   =		torch.tensor([timesteps]      ,     dtype=torch.long      ,     device=input_tokens.device       )
					elif torch.is_tensor(_lowerCamelCase       ) and len(timesteps.shape       ) == 0:
						A_  :				Dict	   =		timesteps[None].to(input_tokens.device       )

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

					A_  :				Any	   =		self.decoder(
					    encodings_and_masks=_lowerCamelCase      ,     decoder_input_tokens=_lowerCamelCase      ,     decoder_noise_time=_lowerCamelCase       )
					return logits







				@torch.no_grad()
				def __call__(							self   :					List[Any]      ,     _lowerCamelCase   :					List[List[int]]      ,     _lowerCamelCase   :					Optional[torch.Generator] = None      ,     _lowerCamelCase   :					int = 1_00      ,     _lowerCamelCase   :					bool = True      ,     _lowerCamelCase   :					str = "numpy"      ,     _lowerCamelCase   :					Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,     _lowerCamelCase   :					int = 1      ,     ):



					"""simple docstring"""

					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       )}."""       )

					A_  :				Tuple	   =		np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]      ,     dtype=np.floataa       )
					A_  :				str	   =		np.zeros([1, 0, self.n_dims]      ,     np.floataa       )
					A_  :				List[Any]	   =		torch.ones((1, TARGET_FEATURE_LENGTH)      ,     dtype=_lowerCamelCase      ,     device=self.device       )

					for i, encoder_input_tokens in enumerate(_lowerCamelCase       ):
						if i == 0:
							A_  :				str	   =		torch.from_numpy(pred_mel[:1].copy()       ).to(
							    device=self.device      ,     dtype=self.decoder.dtype       )
							# The first chunk has no previous context.
							A_  :				Any	   =		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.
							A_  :				Optional[int]	   =		ones

						A_  :				Optional[int]	   =		self.scale_features(
						    _lowerCamelCase      ,     output_range=[-1.0, 1.0]      ,     clip=_lowerCamelCase       )

						A_  :				Optional[Any]	   =		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
						A_  :				str	   =		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       )       ):
							A_  :				Dict	   =		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
							A_  :				Dict	   =		self.scheduler.step(_lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase      ,     generator=_lowerCamelCase       ).prev_sample

						A_  :				Optional[Any]	   =		self.scale_to_features(_lowerCamelCase      ,     input_range=[-1.0, 1.0]       )
						A_  :				Any	   =		mel[:1]
						A_  :				Optional[Any]	   =		mel.cpu().float().numpy()

						A_  :				List[str]	   =		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":
						A_  :				Union[str, Any]	   =		self.melgan(input_features=full_pred_mel.astype(np.floataa       )       )
					else:
						A_  :				Any	   =		full_pred_mel

					if not return_dict:
						return (output,)

					return AudioPipelineOutput(audios=_lowerCamelCase       )











"""simple docstring"""

import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
		_lowerCamelCase  :			Optional[int]        =       argparse.ArgumentParser()

		parser.add_argument(
		    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
		)
		# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
		parser.add_argument(
		    '--original_config_file',
		    default=None,
		    type=str,
		    help='The YAML config file corresponding to the original architecture.',
		)
		parser.add_argument(
		    '--num_in_channels',
		    default=None,
		    type=int,
		    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
		)
		parser.add_argument(
		    '--scheduler_type',
		    default='pndm',
		    type=str,
		    help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
		)
		parser.add_argument(
		    '--pipeline_type',
		    default=None,
		    type=str,
		    help=(
		        'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
		        '. If `None` pipeline will be automatically inferred.'
		    ),
		)
		parser.add_argument(
		    '--image_size',
		    default=None,
		    type=int,
		    help=(
		        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
		        ' Base. Use 768 for Stable Diffusion v2.'
		    ),
		)
		parser.add_argument(
		    '--prediction_type',
		    default=None,
		    type=str,
		    help=(
		        'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
		        ' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
		    ),
		)
		parser.add_argument(
		    '--extract_ema',
		    action='store_true',
		    help=(
		        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
		        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
		        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
		    ),
		)
		parser.add_argument(
		    '--upcast_attention',
		    action='store_true',
		    help=(
		        'Whether the attention computation should always be upcasted. This is necessary when running stable'
		        ' diffusion 2.1.'
		    ),
		)
		parser.add_argument(
		    '--from_safetensors',
		    action='store_true',
		    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
		)
		parser.add_argument(
		    '--to_safetensors',
		    action='store_true',
		    help='Whether to store pipeline in safetensors format or not.',
		)
		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
		parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
		parser.add_argument(
		    '--stable_unclip',
		    type=str,
		    default=None,
		    required=False,
		    help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
		)
		parser.add_argument(
		    '--stable_unclip_prior',
		    type=str,
		    default=None,
		    required=False,
		    help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
		)
		parser.add_argument(
		    '--clip_stats_path',
		    type=str,
		    help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
		    required=False,
		)
		parser.add_argument(
		    '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
		)
		parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
		parser.add_argument(
		    '--vae_path',
		    type=str,
		    default=None,
		    required=False,
		    help='Set to a path, hub id to an already converted vae to not convert it again.',
		)
		_lowerCamelCase  :			Optional[Any]        =       parser.parse_args()

		_lowerCamelCase  :			Optional[Any]        =       download_from_original_stable_diffusion_ckpt(
		    checkpoint_path=args.checkpoint_path,
		    original_config_file=args.original_config_file,
		    image_size=args.image_size,
		    prediction_type=args.prediction_type,
		    model_type=args.pipeline_type,
		    extract_ema=args.extract_ema,
		    scheduler_type=args.scheduler_type,
		    num_in_channels=args.num_in_channels,
		    upcast_attention=args.upcast_attention,
		    from_safetensors=args.from_safetensors,
		    device=args.device,
		    stable_unclip=args.stable_unclip,
		    stable_unclip_prior=args.stable_unclip_prior,
		    clip_stats_path=args.clip_stats_path,
		    controlnet=args.controlnet,
		    vae_path=args.vae_path,
		)

		if args.half:
				pipe.to(torch_dtype=torch.floataa)

		if args.controlnet:
				# only save the controlnet model
				pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
		else:
				pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





