




"""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


_lowerCamelCase  :			List[str]        =       logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowerCamelCase  :			Optional[Any]        =       256

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    int         =   ["""melgan"""]







				def __init__(							self   :					Any      ,     _lowerCamelCase   :					SpectrogramNotesEncoder      ,     _lowerCamelCase   :					SpectrogramContEncoder      ,     _lowerCamelCase   :					TaFilmDecoder      ,     _lowerCamelCase   :					DDPMScheduler      ,     _lowerCamelCase   :					OnnxRuntimeModel if is_onnx_available() else Any      ,     ):



					"""simple docstring"""

					super().__init__()

					# From MELGAN
					A_  :				List[str]	   =		math.log(1E-5       )  # Matches MelGAN training.
					A_  :				int	   =		4.0  # Largest value for most examples
					A_  :				List[Any]	   =		1_28

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







				def 					a_  (							self   :					Tuple      ,     _lowerCamelCase   :					Tuple      ,     _lowerCamelCase   :					int=(-1.0, 1.0)      ,     _lowerCamelCase   :					List[str]=False       ):



					"""simple docstring"""

					A_   , A_  :				Any	   =		output_range
					if clip:
						A_  :				List[str]	   =		torch.clip(_lowerCamelCase      ,     self.min_value      ,     self.max_value       )
					# Scale to [0, 1].
					A_  :				Optional[int]	   =		(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 					a_  (							self   :					Tuple      ,     _lowerCamelCase   :					Optional[int]      ,     _lowerCamelCase   :					Optional[Any]=(-1.0, 1.0)      ,     _lowerCamelCase   :					Any=False       ):



					"""simple docstring"""

					A_   , A_  :				List[str]	   =		input_range
					A_  :				Union[str, Any]	   =		torch.clip(_lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase       ) if clip else outputs
					# Scale to [0, 1].
					A_  :				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 					a_  (							self   :					Any      ,     _lowerCamelCase   :					Union[str, Any]      ,     _lowerCamelCase   :					Any      ,     _lowerCamelCase   :					Any       ):



					"""simple docstring"""

					A_  :				str	   =		input_tokens > 0
					A_   , A_  :				Any	   =		self.notes_encoder(
					    encoder_input_tokens=_lowerCamelCase      ,     encoder_inputs_mask=_lowerCamelCase       )

					A_   , A_  :				str	   =		self.continuous_encoder(
					    encoder_inputs=_lowerCamelCase      ,     encoder_inputs_mask=_lowerCamelCase       )

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







				def 					a_  (							self   :					List[str]      ,     _lowerCamelCase   :					str      ,     _lowerCamelCase   :					Union[str, Any]      ,     _lowerCamelCase   :					Union[str, Any]       ):



					"""simple docstring"""

					A_  :				List[Any]	   =		noise_time
					if not torch.is_tensor(_lowerCamelCase       ):
						A_  :				Any	   =		torch.tensor([timesteps]      ,     dtype=torch.long      ,     device=input_tokens.device       )
					elif torch.is_tensor(_lowerCamelCase       ) and len(timesteps.shape       ) == 0:
						A_  :				Dict	   =		timesteps[None].to(input_tokens.device       )

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

					A_  :				Any	   =		self.decoder(
					    encodings_and_masks=_lowerCamelCase      ,     decoder_input_tokens=_lowerCamelCase      ,     decoder_noise_time=_lowerCamelCase       )
					return logits







				@torch.no_grad()
				def __call__(							self   :					List[Any]      ,     _lowerCamelCase   :					List[List[int]]      ,     _lowerCamelCase   :					Optional[torch.Generator] = None      ,     _lowerCamelCase   :					int = 1_00      ,     _lowerCamelCase   :					bool = True      ,     _lowerCamelCase   :					str = "numpy"      ,     _lowerCamelCase   :					Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,     _lowerCamelCase   :					int = 1      ,     ):



					"""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       )}."""       )

					A_  :				Tuple	   =		np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]      ,     dtype=np.floataa       )
					A_  :				str	   =		np.zeros([1, 0, self.n_dims]      ,     np.floataa       )
					A_  :				List[Any]	   =		torch.ones((1, TARGET_FEATURE_LENGTH)      ,     dtype=_lowerCamelCase      ,     device=self.device       )

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

						A_  :				Optional[int]	   =		self.scale_features(
						    _lowerCamelCase      ,     output_range=[-1.0, 1.0]      ,     clip=_lowerCamelCase       )

						A_  :				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
						A_  :				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       )       ):
							A_  :				Dict	   =		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
							A_  :				Dict	   =		self.scheduler.step(_lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase      ,     generator=_lowerCamelCase       ).prev_sample

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

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

					if not return_dict:
						return (output,)

					return AudioPipelineOutput(audios=_lowerCamelCase       )











"""simple docstring"""

import random

from .binary_exp_mod import bin_exp_mod

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase=1000	):




	"""simple docstring"""






	if n < 2:
		return False

	if n % 2 == 0:
		return n == 2

	# this means n is odd
	A_  :				List[Any]	   =		n - 1
	A_  :				Dict	   =		0
	while d % 2 == 0:
		d /= 2
		exp += 1

	# n - 1=d*(2**exp)
	A_  :				Optional[Any]	   =		0
	while count < prec:
		A_  :				Optional[int]	   =		random.randint(2  ,      n - 1	)
		A_  :				Any	   =		bin_exp_mod(_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)
		if b != 1:
			A_  :				Dict	   =		True
			for _ in range(_UpperCAmelCase	):
				if b == n - 1:
					A_  :				Optional[int]	   =		False
					break
				A_  :				Any	   =		b * b
				b %= n
			if flag:
				return False
			count += 1
	return True


if __name__ == "__main__":
		_lowerCamelCase  :			List[str]        =       abs(int(input('Enter bound : ').strip()))
		print('Here\'s the list of primes:')
		print(', '.join(str(i) for i in range(n + 1) if is_prime_big(i)))





