





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

__UpperCamelCase:	Tuple = 2_5_6


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				["melgan"]

	def __init__(						self:						List[Any],					lowerCamelCase_:						SpectrogramNotesEncoder,					lowerCamelCase_:						SpectrogramContEncoder,					lowerCamelCase_:						TaFilmDecoder,					lowerCamelCase_:						DDPMScheduler,					lowerCamelCase_:						OnnxRuntimeModel if is_onnx_available() else Any,					):
			super().__init__()

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

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

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

			lowercase__   ,				lowercase__					:      Optional[int]      =		self.continuous_encoder(
			    encoder_inputs=lowerCamelCase_,					encoder_inputs_mask=lowerCamelCase_			)

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

	def    snake_case__(						self:						Any,					lowerCamelCase_:						str,					lowerCamelCase_:						str,					lowerCamelCase_:						Tuple			):
			lowercase__					:      Tuple      =		noise_time
			if not torch.is_tensor(lowerCamelCase_			):
					lowercase__					:      Dict      =		torch.tensor([timesteps],					dtype=torch.long,					device=input_tokens.device			)
			elif torch.is_tensor(lowerCamelCase_			) and len(timesteps.shape			) == 0:
					lowercase__					:      Tuple      =		timesteps[None].to(input_tokens.device			)

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

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





	@torch.no_grad()
	def __call__(						self:						str,					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_			)}."""			)

			lowercase__					:      str      =		np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims],					dtype=np.floataa			)
			lowercase__					:      Dict      =		np.zeros([1, 0, self.n_dims],					np.floataa			)
			lowercase__					:      str      =		torch.ones((1, TARGET_FEATURE_LENGTH),					dtype=lowerCamelCase_,					device=self.device			)

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

					lowercase__					:      Any      =		self.scale_features(
					    lowerCamelCase_,					output_range=[-1.0, 1.0],					clip=lowerCamelCase_			)

					lowercase__					:      List[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
					lowercase__					:      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			)			):
							lowercase__					:      Optional[Any]      =		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
							lowercase__					:      int      =		self.scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					generator=lowerCamelCase_			).prev_sample

					lowercase__					:      str      =		self.scale_to_features(lowerCamelCase_,					input_range=[-1.0, 1.0]			)
					lowercase__					:      int      =		mel[:1]
					lowercase__					:      Dict      =		mel.cpu().float().numpy()

					lowercase__					:      Optional[int]      =		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":
					lowercase__					:      Optional[int]      =		self.melgan(input_features=full_pred_mel.astype(np.floataa			)			)
			else:
					lowercase__					:      str      =		full_pred_mel

			if not return_dict:
					return (output,)

			return AudioPipelineOutput(audios=lowerCamelCase_			)









from __future__ import annotations


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[float]   ,    _lowercase     : Tuple			)   ->							int:




		'''simple docstring'''
		print(f"""Vertex\tShortest Distance from vertex {src}"""			)
		for i, d in enumerate(_lowercase			):
				print(f"""{i}\t\t{d}"""			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[dict[str, int]]   ,    _lowercase     : list[float]   ,    _lowercase     : int			)   ->							Any:




		'''simple docstring'''
		for j in range(_lowercase			):
				lowercase__   ,				lowercase__   ,				lowercase__					:      Dict      =		(graph[j][k] for k in ['src', 'dst', 'weight'])
				if distance[u] != float('inf'			) and distance[u] + w < distance[v]:
						return True
		return False


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[dict[str, int]]   ,    _lowercase     : int   ,    _lowercase     : int   ,    _lowercase     : int			)   ->							list[float]:




		'''simple docstring'''
		lowercase__					:      Dict      =		[float('inf'			)] * vertex_count
		lowercase__					:      Dict      =		0.0

		for _ in range(vertex_count - 1			):
				for j in range(_lowercase			):
						lowercase__   ,				lowercase__   ,				lowercase__					:      int      =		(graph[j][k] for k in ['src', 'dst', 'weight'])

						if distance[u] != float('inf'			) and distance[u] + w < distance[v]:
								lowercase__					:      str      =		distance[u] + w

		lowercase__					:      str      =		check_negative_cycle(_lowercase   ,    _lowercase   ,    _lowercase			)
		if negative_cycle_exists:
				raise Exception('Negative cycle found'			)

		return distance


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			__UpperCamelCase:	Optional[int] = int(input("""Enter number of vertices: """).strip())
			__UpperCamelCase:	Union[str, Any] = int(input("""Enter number of edges: """).strip())

			__UpperCamelCase:	list[dict[str, int]] = [{} for _ in range(E)]

			for i in range(E):
						print("""Edge """, i + 1)
						__UpperCamelCase,	__UpperCamelCase,	__UpperCamelCase:	List[str] = (
						    int(x)
						    for x in input("""Enter source, destination, weight: """).strip().split(""" """)
						)
						__UpperCamelCase:	List[Any] = {"""src""": src, """dst""": dest, """weight""": weight}

			__UpperCamelCase:	Optional[int] = int(input("""\nEnter shortest path source:""").strip())
			__UpperCamelCase:	Dict = bellman_ford(graph, V, E, source)
			print_distance(shortest_distance, 0)


