"""simple docstring"""






import unittest

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)
class 				snake_case       ( unittest.TestCase				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							Vector([1, 2, 3]			)
									self.assertEqual(x.component(0			)    ,1			)
									self.assertEqual(x.component(2			)    ,3			)
									UpperCAmelCase__             =							Vector()







					def 		__lowerCAmelCase						(				self   :	Dict			):
									UpperCAmelCase__             =							Vector([0, 0, 0, 0, 0, 1]			)
									self.assertEqual(str(lowerCamelCase__			)    ,'(0,0,0,0,0,1)'			)







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									UpperCAmelCase__             =							Vector([1, 2, 3, 4]			)
									self.assertEqual(len(lowerCamelCase__			)    ,4			)







					def 		__lowerCAmelCase						(				self   :	Dict			):
									UpperCAmelCase__             =							Vector([1, 2]			)
									UpperCAmelCase__             =							Vector([1, 2, 3, 4, 5]			)
									UpperCAmelCase__             =							Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]			)
									UpperCAmelCase__             =							Vector([1, -1, 1, -1, 2, -3, 4, -5]			)
									self.assertAlmostEqual(x.euclidean_length()    ,2.2_3_6    ,3			)
									self.assertAlmostEqual(y.euclidean_length()    ,7.4_1_6    ,3			)
									self.assertEqual(z.euclidean_length()    ,0			)
									self.assertAlmostEqual(w.euclidean_length()    ,7.6_1_6    ,3			)







					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							Vector([1, 2, 3]			)
									UpperCAmelCase__             =							Vector([1, 1, 1]			)
									self.assertEqual((x + y).component(0			)    ,2			)
									self.assertEqual((x + y).component(1			)    ,3			)
									self.assertEqual((x + y).component(2			)    ,4			)







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									UpperCAmelCase__             =							Vector([1, 2, 3]			)
									UpperCAmelCase__             =							Vector([1, 1, 1]			)
									self.assertEqual((x - y).component(0			)    ,0			)
									self.assertEqual((x - y).component(1			)    ,1			)
									self.assertEqual((x - y).component(2			)    ,2			)







					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							Vector([1, 2, 3]			)
									UpperCAmelCase__             =							Vector([2, -1, 4]			)  # for test of dot product
									UpperCAmelCase__             =							Vector([1, -2, -1]			)
									self.assertEqual(str(x * 3.0			)    ,'(3.0,6.0,9.0)'			)
									self.assertEqual((a * b)    ,0			)







					def 		__lowerCAmelCase						(				self   :	str			):
									self.assertEqual(str(zero_vector(10			)			).count('0'			)    ,10			)







					def 		__lowerCAmelCase						(				self   :	str			):
									self.assertEqual(str(unit_basis_vector(3    ,1			)			)    ,'(0,1,0)'			)







					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							Vector([1, 2, 3]			)
									UpperCAmelCase__             =							Vector([1, 0, 1]			)
									self.assertEqual(str(axpy(2    ,lowerCamelCase__    ,lowerCamelCase__			)			)    ,'(3,4,7)'			)







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							Vector([1, 0, 0, 0, 0, 0]			)
									UpperCAmelCase__             =							x.copy()
									self.assertEqual(str(lowerCamelCase__			)    ,str(lowerCamelCase__			)			)







					def 		__lowerCAmelCase						(				self   :	str			):
									UpperCAmelCase__             =							Vector([1, 0, 0]			)
									x.change_component(0    ,0			)
									x.change_component(1    ,1			)
									self.assertEqual(str(lowerCamelCase__			)    ,'(0,1,0)'			)







					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]    ,3    ,3			)
									self.assertEqual('|1,2,3|\n|2,4,5|\n|6,7,8|\n'    ,str(lowerCamelCase__			)			)







					def 		__lowerCAmelCase						(				self   :	Dict			):
									UpperCAmelCase__             =							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]    ,3    ,3			)
									UpperCAmelCase__             =							[[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
									for x in range(a.height()			):
													for y in range(a.width()			):
																	self.assertEqual(minors[x][y]    ,a.minor(lowerCamelCase__    ,lowerCamelCase__			)			)







					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]    ,3    ,3			)
									UpperCAmelCase__             =							[[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
									for x in range(a.height()			):
													for y in range(a.width()			):
																	self.assertEqual(cofactors[x][y]    ,a.cofactor(lowerCamelCase__    ,lowerCamelCase__			)			)







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									UpperCAmelCase__             =							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]    ,3    ,3			)
									self.assertEqual(-5    ,a.determinant()			)







					def 		__lowerCAmelCase						(				self   :	Dict			):
									UpperCAmelCase__             =							Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]    ,3    ,3			)
									UpperCAmelCase__             =							Vector([1, 2, 3]			)
									self.assertEqual('(14,32,50)'    ,str(a * x			)			)
									self.assertEqual('|2,4,6|\n|8,10,12|\n|14,16,18|\n'    ,str(a * 2			)			)







					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]    ,3    ,3			)
									a.change_component(0    ,2    ,5			)
									self.assertEqual('|1,2,5|\n|2,4,5|\n|6,7,8|\n'    ,str(lowerCamelCase__			)			)







					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]    ,3    ,3			)
									self.assertEqual(7    ,a.component(2    ,1			)    ,0.0_1			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]    ,3    ,3			)
									UpperCAmelCase__             =							Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]    ,3    ,3			)
									self.assertEqual('|2,4,10|\n|4,8,10|\n|12,14,18|\n'    ,str(a + b			)			)







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]    ,3    ,3			)
									UpperCAmelCase__             =							Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]    ,3    ,3			)
									self.assertEqual('|0,0,-4|\n|0,0,0|\n|0,0,-2|\n'    ,str(a - b			)			)







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									self.assertEqual(
									    '|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n'    ,str(square_zero_matrix(5			)			)    ,)


if __name__ == "__main__":
						unittest.main()










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     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							List[str]   		=		["melgan"]




   def __init__(    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		) ->      None:






       """simple docstring"""
       super().__init__()

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

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




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__=(-1.0, 1.0),		__magic_name__=False							) ->      Any:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				str          =      output_range
       if clip:
           UpperCamelCase__							:				Union[str, Any]          =      torch.clip(__magic_name__,		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,		__magic_name__,		__magic_name__=(-1.0, 1.0),		__magic_name__=False							) ->      Optional[int]:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				List[str]          =      input_range
       UpperCamelCase__							:				Any          =      torch.clip(__magic_name__,		__magic_name__,		__magic_name__							) if clip else outputs
       # Scale to [0, 1].
       UpperCamelCase__							:				Any          =      (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,		__magic_name__,		__magic_name__,		__magic_name__							) ->      List[str]:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      input_tokens > 0
       UpperCamelCase__   ,UpperCamelCase__							:				Any          =      self.notes_encoder(
           encoder_input_tokens=__magic_name__,		encoder_inputs_mask=__magic_name__							)

       UpperCamelCase__   ,UpperCamelCase__							:				Optional[Any]          =      self.continuous_encoder(
           encoder_inputs=__magic_name__,		encoder_inputs_mask=__magic_name__							)

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




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				Any          =      noise_time
       if not torch.is_tensor(__magic_name__							):
           UpperCamelCase__							:				Tuple          =      torch.tensor([timesteps],		dtype=torch.long,		device=input_tokens.device							)
       elif torch.is_tensor(__magic_name__							) and len(timesteps.shape							) == 0:
           UpperCamelCase__							:				Union[str, Any]          =      timesteps[None].to(input_tokens.device							)

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

       UpperCamelCase__							:				List[str]          =      self.decoder(
           encodings_and_masks=__magic_name__,		decoder_input_tokens=__magic_name__,		decoder_noise_time=__magic_name__							)
       return logits




   @torch.no_grad()
   def __call__(    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = 100,		__magic_name__ = True,		__magic_name__ = "numpy",		__magic_name__ = None,		__magic_name__ = 1,		) ->      Union[AudioPipelineOutput, Tuple]:






       """simple docstring"""
       if (callback_steps is None) or (
           callback_steps is not None and (not isinstance(__magic_name__,		__magic_name__							) or callback_steps <= 0)
       ):
           raise ValueError(
               f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
               f" {type(__magic_name__							)}."							)

       UpperCamelCase__							:				Dict          =      np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims],		dtype=np.floataa							)
       UpperCamelCase__							:				Tuple          =      np.zeros([1, 0, self.n_dims],		np.floataa							)
       UpperCamelCase__							:				List[Any]          =      torch.ones((1, TARGET_FEATURE_LENGTH),		dtype=__magic_name__,		device=self.device							)

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

           UpperCamelCase__							:				int          =      self.scale_features(
               __magic_name__,		output_range=[-1.0, 1.0],		clip=__magic_name__							)

           UpperCamelCase__							:				Union[str, Any]          =      self.encode(
               input_tokens=torch.IntTensor([encoder_input_tokens]							).to(device=self.device							),		continuous_inputs=__magic_name__,		continuous_mask=__magic_name__,		)

           # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
           UpperCamelCase__							:				Optional[int]          =      randn_tensor(
               shape=encoder_continuous_inputs.shape,		generator=__magic_name__,		device=self.device,		dtype=self.decoder.dtype,		)

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

           # Denoising diffusion loop
           for j, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
               UpperCamelCase__							:				Union[str, Any]          =      self.decode(
                   encodings_and_masks=__magic_name__,		input_tokens=__magic_name__,		noise_time=t / self.scheduler.config.num_train_timesteps,		)

               # Compute previous output: x_t -> x_t-1
               UpperCamelCase__							:				List[Any]          =      self.scheduler.step(__magic_name__,		__magic_name__,		__magic_name__,		generator=__magic_name__							).prev_sample

           UpperCamelCase__							:				List[Any]          =      self.scale_to_features(__magic_name__,		input_range=[-1.0, 1.0]							)
           UpperCamelCase__							:				List[Any]          =      mel[:1]
           UpperCamelCase__							:				int          =      mel.cpu().float().numpy()

           UpperCamelCase__							:				Union[str, Any]          =      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(__magic_name__,		__magic_name__							)

           logger.info('''Generated segment''',		__magic_name__							)

       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__							:				Optional[int]          =      self.melgan(input_features=full_pred_mel.astype(np.floataa							)							)
       else:
           UpperCamelCase__							:				Any          =      full_pred_mel

       if not return_dict:
           return (output,)

       return AudioPipelineOutput(audios=__magic_name__							)

