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__											=   2_5_6





class 	A				(							a__	):
   __UpperCAmelCase					:					Tuple    =     ["""melgan"""]


   def __init__(self	:		Union[str, Any]			,    __UpperCAmelCase	:		Tuple			,    __UpperCAmelCase	:		Optional[Any]			,    __UpperCAmelCase	:		List[Any]			,    __UpperCAmelCase	:		Dict			,    __UpperCAmelCase	:		str			,    )		->							None:
         """simple docstring"""



         super().__init__()

         # From MELGAN
         UpperCAmelCase__								=    math.log(1E-5						)  # Matches MelGAN training.
         UpperCAmelCase__								=    4.0  # Largest value for most examples
         UpperCAmelCase__								=    1_2_8

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


   def      lowercase_ (self	:		Any			,    __UpperCAmelCase	:		int			,    __UpperCAmelCase	:		List[str]=(-1.0, 1.0)			,    __UpperCAmelCase	:		Optional[int]=False						)		->							List[Any]:
         """simple docstring"""



         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      lowercase_ (self	:		Dict			,    __UpperCAmelCase	:		Union[str, Any]			,    __UpperCAmelCase	:		List[Any]=(-1.0, 1.0)			,    __UpperCAmelCase	:		Union[str, Any]=False						)		->							Optional[Any]:
         """simple docstring"""



         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      lowercase_ (self	:		int			,    __UpperCAmelCase	:		str			,    __UpperCAmelCase	:		Any			,    __UpperCAmelCase	:		Tuple						)		->							str:
         """simple docstring"""



         UpperCAmelCase__								=    input_tokens > 0
         UpperCAmelCase__								=    self.notes_encoder(
             encoder_input_tokens=lowerCAmelCase__			,    encoder_inputs_mask=lowerCAmelCase__						)

         UpperCAmelCase__								=    self.continuous_encoder(
             encoder_inputs=lowerCAmelCase__			,    encoder_inputs_mask=lowerCAmelCase__						)

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


   def      lowercase_ (self	:		List[str]			,    __UpperCAmelCase	:		Optional[Any]			,    __UpperCAmelCase	:		str			,    __UpperCAmelCase	:		str						)		->							Any:
         """simple docstring"""



         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	:		Optional[Any]			,    __UpperCAmelCase	:		Tuple			,    __UpperCAmelCase	:		int = None			,    __UpperCAmelCase	:		int = 1_0_0			,    __UpperCAmelCase	:		Union[str, Any] = True			,    __UpperCAmelCase	:		Tuple = "numpy"			,    __UpperCAmelCase	:		Any = None			,    __UpperCAmelCase	:		Optional[int] = 1			,    )		->							Union[AudioPipelineOutput, Tuple]:
         """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__						)}."""						)

         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__						)










"""simple docstring"""




from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ..utils.dummy_pt_objects import *  # noqa F403
else:
		from .scheduling_consistency_models import CMStochasticIterativeScheduler
		from .scheduling_ddim import DDIMScheduler
		from .scheduling_ddim_inverse import DDIMInverseScheduler
		from .scheduling_ddim_parallel import DDIMParallelScheduler
		from .scheduling_ddpm import DDPMScheduler
		from .scheduling_ddpm_parallel import DDPMParallelScheduler
		from .scheduling_deis_multistep import DEISMultistepScheduler
		from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
		from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
		from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
		from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
		from .scheduling_euler_discrete import EulerDiscreteScheduler
		from .scheduling_heun_discrete import HeunDiscreteScheduler
		from .scheduling_ipndm import IPNDMScheduler
		from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
		from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
		from .scheduling_karras_ve import KarrasVeScheduler
		from .scheduling_pndm import PNDMScheduler
		from .scheduling_repaint import RePaintScheduler
		from .scheduling_sde_ve import ScoreSdeVeScheduler
		from .scheduling_sde_vp import ScoreSdeVpScheduler
		from .scheduling_unclip import UnCLIPScheduler
		from .scheduling_unipc_multistep import UniPCMultistepScheduler
		from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
		from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ..utils.dummy_flax_objects import *  # noqa F403
else:
		from .scheduling_ddim_flax import FlaxDDIMScheduler
		from .scheduling_ddpm_flax import FlaxDDPMScheduler
		from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
		from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
		from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
		from .scheduling_pndm_flax import FlaxPNDMScheduler
		from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
		from .scheduling_utils_flax import (
		    FlaxKarrasDiffusionSchedulers,
		    FlaxSchedulerMixin,
		    FlaxSchedulerOutput,
		    broadcast_to_shape_from_left,
		)


try:
		if not (is_torch_available() and is_scipy_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
		from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
		if not (is_torch_available() and is_torchsde_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
		from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler


