




"""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


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

a      =							2_5_6






class       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						['melgan']


						def __init__(							self				:			Tuple						,						lowerCAmelCase				:			SpectrogramNotesEncoder						,						lowerCAmelCase				:			SpectrogramContEncoder						,						lowerCAmelCase				:			TaFilmDecoder						,						lowerCAmelCase				:			DDPMScheduler						,						lowerCAmelCase				:			OnnxRuntimeModel if is_onnx_available() else Any						,						):
										super().__init__()

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

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


						def   __lowercase (							self				:			str						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			int=(-1.0, 1.0)						,						lowerCAmelCase				:			Optional[int]=False					):
										lowerCAmelCase ,						lowerCAmelCase  =				output_range
										if clip:
														lowerCAmelCase  =				torch.clip(lowerCAmelCase						,						self.min_value						,						self.max_value					)
										# Scale to [0, 1].
										lowerCAmelCase  =				(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				:			Union[str, Any]						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			List[Any]=(-1.0, 1.0)						,						lowerCAmelCase				:			Any=False					):
										lowerCAmelCase ,						lowerCAmelCase  =				input_range
										lowerCAmelCase  =				torch.clip(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					) if clip else outputs
										# Scale to [0, 1].
										lowerCAmelCase  =				(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				:			Union[str, Any]						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			int					):
										lowerCAmelCase  =				input_tokens > 0
										lowerCAmelCase ,						lowerCAmelCase  =				self.notes_encoder(
										    encoder_input_tokens=lowerCAmelCase						,						encoder_inputs_mask=lowerCAmelCase					)

										lowerCAmelCase ,						lowerCAmelCase  =				self.continuous_encoder(
										    encoder_inputs=lowerCAmelCase						,						encoder_inputs_mask=lowerCAmelCase					)

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


						def   __lowercase (							self				:			Tuple						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			Optional[Any]						,						lowerCAmelCase				:			List[Any]					):
										lowerCAmelCase  =				noise_time
										if not torch.is_tensor(lowerCAmelCase					):
														lowerCAmelCase  =				torch.tensor([timesteps]						,						dtype=torch.long						,						device=input_tokens.device					)
										elif torch.is_tensor(lowerCAmelCase					) and len(timesteps.shape					) == 0:
														lowerCAmelCase  =				timesteps[None].to(input_tokens.device					)

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

										lowerCAmelCase  =				self.decoder(
										    encodings_and_masks=lowerCAmelCase						,						decoder_input_tokens=lowerCAmelCase						,						decoder_noise_time=lowerCAmelCase					)
										return logits




						@torch.no_grad()
						def __call__(							self				:			Optional[int]						,						lowerCAmelCase				:			List[List[int]]						,						lowerCAmelCase				:			Optional[torch.Generator] = None						,						lowerCAmelCase				:			int = 100						,						lowerCAmelCase				:			bool = True						,						lowerCAmelCase				:			str = "numpy"						,						lowerCAmelCase				:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,						lowerCAmelCase				:			int = 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					)}.'''					)

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

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

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

														lowerCAmelCase  =				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
														lowerCAmelCase  =				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					)					):
																		lowerCAmelCase  =				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
																		lowerCAmelCase  =				self.scheduler.step(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase						,						generator=lowerCAmelCase					).prev_sample

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

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

										if not return_dict:
														return (output,)

										return AudioPipelineOutput(audios=lowerCAmelCase					)











"""simple docstring"""







from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload






class       SCREAMING_SNAKE_CASE__					:


						def __init__(							self				:			Dict						,						lowerCAmelCase				:			Collection[float] | None = None					):
										if components is None:
														lowerCAmelCase  =				[]
										lowerCAmelCase  =				list(lowerCAmelCase					)


						def __len__(							self				:			List[Any]					):
										return len(self.__components					)


						def __str__(							self				:			str					):
										return "(" + ",".join(map(lowerCAmelCase						,						self.__components					)					) + ")"


						def __add__(							self				:			Any						,						lowerCAmelCase				:			Vector					):
										lowerCAmelCase  =				len(self					)
										if size == len(lowerCAmelCase					):
														lowerCAmelCase  =				[self.__components[i] + other.component(lowerCAmelCase					) for i in range(lowerCAmelCase					)]
														return Vector(lowerCAmelCase					)
										else:
														raise Exception("""must have the same size"""					)


						def __sub__(							self				:			List[Any]						,						lowerCAmelCase				:			Vector					):
										lowerCAmelCase  =				len(self					)
										if size == len(lowerCAmelCase					):
														lowerCAmelCase  =				[self.__components[i] - other.component(lowerCAmelCase					) for i in range(lowerCAmelCase					)]
														return Vector(lowerCAmelCase					)
										else:  # error case
														raise Exception("""must have the same size"""					)


						@overload
						def __mul__(							self				:			Optional[int]						,						lowerCAmelCase				:			float					):
										...


						@overload
						def __mul__(							self				:			List[Any]						,						lowerCAmelCase				:			Vector					):
										...


						def __mul__(							self				:			Dict						,						lowerCAmelCase				:			float | Vector					):
										if isinstance(lowerCAmelCase						,						(float, int)					):
														lowerCAmelCase  =				[c * other for c in self.__components]
														return Vector(lowerCAmelCase					)
										elif isinstance(lowerCAmelCase						,						lowerCAmelCase					) and len(self					) == len(lowerCAmelCase					):
														lowerCAmelCase  =				len(self					)
														lowerCAmelCase  =				[self.__components[i] * other.component(lowerCAmelCase					) for i in range(lowerCAmelCase					)]
														return sum(lowerCAmelCase					)
										else:  # error case
														raise Exception("""invalid operand!"""					)


						def   __lowercase (							self				:			Tuple					):
										return Vector(self.__components					)


						def   __lowercase (							self				:			Dict						,						lowerCAmelCase				:			int					):
										if isinstance(lowerCAmelCase						,						lowerCAmelCase					) and -len(self.__components					) <= i < len(self.__components					):
														return self.__components[i]
										else:
														raise Exception("""index out of range"""					)


						def   __lowercase (							self				:			Any						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			float					):
										assert -len(self.__components					) <= pos < len(self.__components					)
										lowerCAmelCase  =				value


						def   __lowercase (							self				:			Optional[int]					):
										if len(self.__components					) == 0:
														raise Exception("""Vector is empty"""					)
										lowerCAmelCase  =				[c**2 for c in self.__components]
										return math.sqrt(sum(lowerCAmelCase					)					)




						def   __lowercase (							self				:			str						,						lowerCAmelCase				:			Vector						,						lowerCAmelCase				:			bool = False					):
										lowerCAmelCase  =				self * other
										lowerCAmelCase  =				self.euclidean_length() * other.euclidean_length()
										if deg:
														return math.degrees(math.acos(num / den					)					)
										else:
														return math.acos(num / den					)





def 							lowercase					(snake_case__   : int )     ->  Vector:


				'''simple docstring'''






				assert isinstance(snake_case__      ,       snake_case__ )
				return Vector([0] * dimension )





def 							lowercase					(snake_case__   : int      ,       snake_case__   : int )     ->  Vector:


				'''simple docstring'''






				assert isinstance(snake_case__      ,       snake_case__ ) and (isinstance(snake_case__      ,       snake_case__ ))
				lowerCAmelCase  =				[0] * dimension
				lowerCAmelCase  =				1
				return Vector(snake_case__ )





def 							lowercase					(snake_case__   : float      ,       snake_case__   : Vector      ,       snake_case__   : Vector )     ->  Vector:


				'''simple docstring'''






				assert (
				    isinstance(snake_case__      ,       snake_case__ )
				    and isinstance(snake_case__      ,       snake_case__ )
				    and (isinstance(snake_case__      ,       (int, float) ))
				)
				return x * scalar + y





def 							lowercase					(snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : int )     ->  Vector:


				'''simple docstring'''






				random.seed(snake_case__ )
				lowerCAmelCase  =				[random.randint(snake_case__      ,       snake_case__ ) for _ in range(snake_case__ )]
				return Vector(snake_case__ )






class       SCREAMING_SNAKE_CASE__					:


						def __init__(							self				:			Any						,						lowerCAmelCase				:			list[list[float]]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										lowerCAmelCase  =				matrix
										lowerCAmelCase  =				w
										lowerCAmelCase  =				h


						def __str__(							self				:			List[str]					):
										lowerCAmelCase  =				""""""
										for i in range(self.__height					):
														ans += "|"
														for j in range(self.__width					):
																		if j < self.__width - 1:
																						ans += str(self.__matrix[i][j]					) + ","
																		else:
																						ans += str(self.__matrix[i][j]					) + "|\n"
										return ans


						def __add__(							self				:			Optional[Any]						,						lowerCAmelCase				:			Matrix					):
										if self.__width == other.width() and self.__height == other.height():
														lowerCAmelCase  =				[]
														for i in range(self.__height					):
																		lowerCAmelCase  =				[
																		    self.__matrix[i][j] + other.component(lowerCAmelCase						,						lowerCAmelCase					)
																		    for j in range(self.__width					)
																		]
																		matrix.append(lowerCAmelCase					)
														return Matrix(lowerCAmelCase						,						self.__width						,						self.__height					)
										else:
														raise Exception("""matrix must have the same dimension!"""					)


						def __sub__(							self				:			Union[str, Any]						,						lowerCAmelCase				:			Matrix					):
										if self.__width == other.width() and self.__height == other.height():
														lowerCAmelCase  =				[]
														for i in range(self.__height					):
																		lowerCAmelCase  =				[
																		    self.__matrix[i][j] - other.component(lowerCAmelCase						,						lowerCAmelCase					)
																		    for j in range(self.__width					)
																		]
																		matrix.append(lowerCAmelCase					)
														return Matrix(lowerCAmelCase						,						self.__width						,						self.__height					)
										else:
														raise Exception("""matrices must have the same dimension!"""					)


						@overload
						def __mul__(							self				:			Any						,						lowerCAmelCase				:			float					):
										...


						@overload
						def __mul__(							self				:			Optional[int]						,						lowerCAmelCase				:			Vector					):
										...


						def __mul__(							self				:			Dict						,						lowerCAmelCase				:			float | Vector					):
										if isinstance(lowerCAmelCase						,						lowerCAmelCase					):  # matrix-vector
														if len(lowerCAmelCase					) == self.__width:
																		lowerCAmelCase  =				zero_vector(self.__height					)
																		for i in range(self.__height					):
																						lowerCAmelCase  =				[
																						    self.__matrix[i][j] * other.component(lowerCAmelCase					)
																						    for j in range(self.__width					)
																						]
																						ans.change_component(lowerCAmelCase						,						sum(lowerCAmelCase					)					)
																		return ans
														else:
																		raise Exception(
																		    """vector must have the same size as the """
																		    """number of columns of the matrix!"""					)
										elif isinstance(lowerCAmelCase						,						(int, float)					):  # matrix-scalar
														lowerCAmelCase  =				[
														    [self.__matrix[i][j] * other for j in range(self.__width					)]
														    for i in range(self.__height					)
														]
														return Matrix(lowerCAmelCase						,						self.__width						,						self.__height					)
										return None


						def   __lowercase (							self				:			Any					):
										return self.__height


						def   __lowercase (							self				:			int					):
										return self.__width


						def   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										if 0 <= x < self.__height and 0 <= y < self.__width:
														return self.__matrix[x][y]
										else:
														raise Exception("""change_component: indices out of bounds"""					)


						def   __lowercase (							self				:			int						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			float					):
										if 0 <= x < self.__height and 0 <= y < self.__width:
														lowerCAmelCase  =				value
										else:
														raise Exception("""change_component: indices out of bounds"""					)


						def   __lowercase (							self				:			int						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										if self.__height != self.__width:
														raise Exception("""Matrix is not square"""					)
										lowerCAmelCase  =				self.__matrix[:x] + self.__matrix[x + 1 :]
										for i in range(len(lowerCAmelCase					)					):
														lowerCAmelCase  =				minor[i][:y] + minor[i][y + 1 :]
										return Matrix(lowerCAmelCase						,						self.__width - 1						,						self.__height - 1					).determinant()


						def   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										if self.__height != self.__width:
														raise Exception("""Matrix is not square"""					)
										if 0 <= x < self.__height and 0 <= y < self.__width:
														return (-1) ** (x + y) * self.minor(lowerCAmelCase						,						lowerCAmelCase					)
										else:
														raise Exception("""Indices out of bounds"""					)




						def   __lowercase (							self				:			List[Any]					):
										if self.__height != self.__width:
														raise Exception("""Matrix is not square"""					)
										if self.__height < 1:
														raise Exception("""Matrix has no element"""					)
										elif self.__height == 1:
														return self.__matrix[0][0]
										elif self.__height == 2:
														return (
														    self.__matrix[0][0] * self.__matrix[1][1]
														    - self.__matrix[0][1] * self.__matrix[1][0]
														)
										else:
														lowerCAmelCase  =				[
														    self.__matrix[0][y] * self.cofactor(0						,						lowerCAmelCase					) for y in range(self.__width					)
														]
														return sum(lowerCAmelCase					)





def 							lowercase					(snake_case__   : int )     ->  Matrix:


				'''simple docstring'''






				lowerCAmelCase  =				[[0] * n for _ in range(snake_case__ )]
				return Matrix(snake_case__      ,       snake_case__      ,       snake_case__ )






def 							lowercase					(snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : int )     ->  Matrix:


				'''simple docstring'''






				random.seed(snake_case__ )
				lowerCAmelCase  =				[
				    [random.randint(snake_case__      ,       snake_case__ ) for _ in range(snake_case__ )] for _ in range(snake_case__ )
				]
				return Matrix(snake_case__      ,       snake_case__      ,       snake_case__ )





