"""simple docstring"""



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


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

SCREAMING_SNAKE_CASE__:Any			=	256






class 				snake_case__     (   __a   ):
 _snake_case   :			Any           =		["""melgan"""]


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

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

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


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase=(-1.0, 1.0)  ,						lowerCamelCase=False		):
        __a				, __a     				=  output_range
        if clip:
               __a     				=  torch.clip(a__  ,						self.min_value  ,						self.max_value		)
        # Scale to [0, 1].
        __a     				=  (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  ,						lowerCamelCase  ,						lowerCamelCase=(-1.0, 1.0)  ,						lowerCamelCase=False		):
        __a				, __a     				=  input_range
        __a     				=  torch.clip(a__  ,						a__  ,						a__		) if clip else outputs
        # Scale to [0, 1].
        __a     				=  (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  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  input_tokens > 0
        __a				, __a     				=  self.notes_encoder(
            encoder_input_tokens=a__  ,						encoder_inputs_mask=a__		)

        __a				, __a     				=  self.continuous_encoder(
            encoder_inputs=a__  ,						encoder_inputs_mask=a__		)

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


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

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

        __a     				=  self.decoder(
            encodings_and_masks=a__  ,						decoder_input_tokens=a__  ,						decoder_noise_time=a__		)
        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(a__  ,						a__		) or callback_steps <= 0)
        ):
               raise ValueError(
                   F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                   F" {type(a__		)}."		)

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

        for i, encoder_input_tokens in enumerate(a__		):
               if i == 0:
                      __a     				=  torch.from_numpy(pred_mel[:1].copy()		).to(
                          device=self.device  ,						dtype=self.decoder.dtype		)
                      # The first chunk has no previous context.
                      __a     				=  torch.zeros((1, TARGET_FEATURE_LENGTH)  ,						dtype=a__  ,						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     				=  ones

               __a     				=  self.scale_features(
                   a__  ,						output_range=[-1.0, 1.0]  ,						clip=a__		)

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

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

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

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

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

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

               __a     				=  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(a__  ,						a__		)

               logger.info("Generated segment"  ,						a__		)

        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     				=  self.melgan(input_features=full_pred_mel.astype(np.floataa		)		)
        else:
               __a     				=  full_pred_mel

        if not return_dict:
               return (output,)

        return AudioPipelineOutput(audios=a__		)

"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			UNetaDModel
 _snake_case   :			KarrasVeScheduler


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        super().__init__()
        self.register_modules(unet=lowerCamelCase  ,						scheduler=lowerCamelCase		)






 @torch.no_grad()
 def __call__(				self  ,						lowerCamelCase = 1  ,						lowerCamelCase = 50  ,						lowerCamelCase = None  ,						lowerCamelCase = "pil"  ,						lowerCamelCase = True  ,						**lowerCamelCase  ,						):
        __a     				=  self.unet.config.sample_size
        __a     				=  (batch_size, 3, img_size, img_size)

        __a     				=  self.unet

        # sample x_0 ~ N(0, sigma_0^2 * I)
        __a     				=  randn_tensor(lowerCamelCase  ,						generator=lowerCamelCase  ,						device=self.device		) * self.scheduler.init_noise_sigma

        self.scheduler.set_timesteps(lowerCamelCase		)

        for t in self.progress_bar(self.scheduler.timesteps		):
               # here sigma_t == t_i from the paper
               __a     				=  self.scheduler.schedule[t]
               __a     				=  self.scheduler.schedule[t - 1] if t > 0 else 0

               # 1. Select temporarily increased noise level sigma_hat
               # 2. Add new noise to move from sample_i to sample_hat
               __a				, __a     				=  self.scheduler.add_noise_to_input(lowerCamelCase  ,						lowerCamelCase  ,						generator=lowerCamelCase		)

               # 3. Predict the noise residual given the noise magnitude `sigma_hat`
               # The model inputs and output are adjusted by following eq. (213) in [1].
               __a     				=  (sigma_hat / 2) * model((sample_hat + 1) / 2  ,						sigma_hat / 2		).sample

               # 4. Evaluate dx/dt at sigma_hat
               # 5. Take Euler step from sigma to sigma_prev
               __a     				=  self.scheduler.step(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)

               if sigma_prev != 0:
                      # 6. Apply 2nd order correction
                      # The model inputs and output are adjusted by following eq. (213) in [1].
                      __a     				=  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2  ,						sigma_prev / 2		).sample
                      __a     				=  self.scheduler.step_correct(
                          lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						step_output.prev_sample  ,						step_output["derivative"]  ,						)
               __a     				=  step_output.prev_sample

        __a     				=  (sample / 2 + 0.5).clamp(0  ,						1		)
        __a     				=  sample.cpu().permute(0  ,						2  ,						3  ,						1		).numpy()
        if output_type == "pil":
               __a     				=  self.numpy_to_pil(lowerCamelCase		)

        if not return_dict:
               return (image,)

        return ImagePipelineOutput(images=lowerCamelCase		)
