






'''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


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

UpperCamelCase      					=256




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    Dict			  =    ['''melgan''']
 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  ):
       super().__init__()

       # From MELGAN
       UpperCamelCase_					: Tuple            =				math.log(1E-5			)  # Matches MelGAN training.
       UpperCamelCase_					: Dict            =				4.0  # Largest value for most examples
       UpperCamelCase_					: int            =				1_28

       self.register_modules(
           notes_encoder=__lowerCAmelCase      ,  continuous_encoder=__lowerCAmelCase      ,  decoder=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase      ,  melgan=__lowerCAmelCase      ,  )
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=(-1.0, 1.0)      ,  __lowerCAmelCase=False			):
       UpperCamelCase_     ,      UpperCamelCase_					: Any            =				output_range
       if clip:
             UpperCamelCase_					: Dict            =				torch.clip(__lowerCAmelCase      ,  self.min_value      ,  self.max_value			)
       # Scale to [0, 1].
       UpperCamelCase_					: List[str]            =				(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 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=(-1.0, 1.0)      ,  __lowerCAmelCase=False			):
       UpperCamelCase_     ,      UpperCamelCase_					: List[Any]            =				input_range
       UpperCamelCase_					: Dict            =				torch.clip(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			) if clip else outputs
       # Scale to [0, 1].
       UpperCamelCase_					: 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 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       UpperCamelCase_					: List[str]            =				input_tokens > 0
       UpperCamelCase_     ,      UpperCamelCase_					: Dict            =				self.notes_encoder(
           encoder_input_tokens=__lowerCAmelCase      ,  encoder_inputs_mask=__lowerCAmelCase			)

       UpperCamelCase_     ,      UpperCamelCase_					: Tuple            =				self.continuous_encoder(
           encoder_inputs=__lowerCAmelCase      ,  encoder_inputs_mask=__lowerCAmelCase			)

       return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Dict            =				noise_time
       if not torch.is_tensor(__lowerCAmelCase			):
             UpperCamelCase_					: List[Any]            =				torch.tensor([timesteps]      ,  dtype=torch.long      ,  device=input_tokens.device			)
       elif torch.is_tensor(__lowerCAmelCase			) and len(timesteps.shape			) == 0:
             UpperCamelCase_					: Tuple            =				timesteps[None].to(input_tokens.device			)

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

       UpperCamelCase_					: List[Any]            =				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 = 1_00      ,  __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_					: Union[str, Any]            =				np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]      ,  dtype=np.floataa			)
       UpperCamelCase_					: Dict            =				np.zeros([1, 0, self.n_dims]      ,  np.floataa			)
       UpperCamelCase_					: Optional[Any]            =				torch.ones((1, TARGET_FEATURE_LENGTH)      ,  dtype=__lowerCAmelCase      ,  device=self.device			)

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

             UpperCamelCase_					: Tuple            =				self.scale_features(
                 __lowerCAmelCase      ,  output_range=[-1.0, 1.0]      ,  clip=__lowerCAmelCase			)

             UpperCamelCase_					: 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
             UpperCamelCase_					: List[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			)			):
                   UpperCamelCase_					: List[str]            =				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_					: List[Any]            =				self.scheduler.step(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  generator=__lowerCAmelCase			).prev_sample

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

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

       if not return_dict:
             return (output,)

       return AudioPipelineOutput(audios=__lowerCAmelCase			)














'''simple docstring'''

def 				snake_case       (  a_				:			list[int]     , a_				:			list[int]						)							->					tuple[float, float]:





      """simple docstring"""

      if not len(a_						) == len(a_						) == 3:
            raise ValueError("""Please enter a valid equation."""						)
      if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
            raise ValueError("""Both a & b of two equations can't be zero."""						)

      # Extract the coefficients
      UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: Optional[int]            =				equationa
      UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: List[str]            =				equationa

      # Calculate the determinants of the matrices
      UpperCamelCase_					: Union[str, Any]            =				aa * ba - aa * ba
      UpperCamelCase_					: Dict            =				ca * ba - ca * ba
      UpperCamelCase_					: Dict            =				aa * ca - aa * ca

      # Check if the system of linear equations has a solution (using Cramer's rule)
      if determinant == 0:
            if determinant_x == determinant_y == 0:
                  raise ValueError("""Infinite solutions. (Consistent system)"""						)
            else:
                  raise ValueError("""No solution. (Inconsistent system)"""						)
      else:
            if determinant_x == determinant_y == 0:
                  # Trivial solution (Inconsistent system)
                  return (0.0, 0.0)
            else:
                  UpperCamelCase_					: List[Any]            =				determinant_x / determinant
                  UpperCamelCase_					: List[str]            =				determinant_y / determinant
                  # Non-Trivial Solution (Consistent system)
                  return (x, y)






