






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[int]   		=   256



class 							SCREAMING_SNAKE_CASE__		(       __lowerCamelCase		):





					"""simple docstring"""






					a_			=	['melgan']



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

						# From MELGAN
						snake_case__				:   List[Any]     =						math.log(1e-5 )  # Matches MelGAN training.
						snake_case__				:   Any     =						4.0  # Largest value for most examples
						snake_case__				:   Optional[int]     =						1_2_8

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



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

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

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



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						int							,	__A  :						str							,	__A  :						Union[str, Any] ):
						snake_case__				:   Optional[int]     =						noise_time
						if not torch.is_tensor(UpperCamelCase_ ):
							snake_case__				:   Optional[int]     =						torch.tensor([timesteps]							,	dtype=torch.long							,	device=input_tokens.device )
						elif torch.is_tensor(UpperCamelCase_ ) and len(timesteps.shape ) == 0:
							snake_case__				:   Tuple     =						timesteps[None].to(input_tokens.device )

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

						snake_case__				:   Any     =						self.decoder(
						    encodings_and_masks=UpperCamelCase_							,	decoder_input_tokens=UpperCamelCase_							,	decoder_noise_time=UpperCamelCase_ )
						return logits






					@torch.no_grad()
					def __call__(      self  :						Optional[int]							,	__A  :						List[List[int]]							,	__A  :						Optional[torch.Generator] = None							,	__A  :						int = 1_0_0							,	__A  :						bool = True							,	__A  :						str = "numpy"							,	__A  :						Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,	__A  :						int = 1							,	):
						if (callback_steps is None) or (
						    callback_steps is not None and (not isinstance(UpperCamelCase_							,	UpperCamelCase_ ) or callback_steps <= 0)
						):
							raise ValueError(
							    f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
							    f''' {type(UpperCamelCase_ )}.''' )

						snake_case__				:   Union[str, Any]     =						np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]							,	dtype=np.floataa )
						snake_case__				:   Union[str, Any]     =						np.zeros([1, 0, self.n_dims]							,	np.floataa )
						snake_case__				:   Optional[int]     =						torch.ones((1, TARGET_FEATURE_LENGTH)							,	dtype=UpperCamelCase_							,	device=self.device )

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

							snake_case__				:   Dict     =						self.scale_features(
							    UpperCamelCase_							,	output_range=[-1.0, 1.0]							,	clip=UpperCamelCase_ )

							snake_case__				:   Dict     =						self.encode(
							    input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device )							,	continuous_inputs=UpperCamelCase_							,	continuous_mask=UpperCamelCase_							,	)

							# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
							snake_case__				:   int     =						randn_tensor(
							    shape=encoder_continuous_inputs.shape							,	generator=UpperCamelCase_							,	device=self.device							,	dtype=self.decoder.dtype							,	)

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

							# Denoising diffusion loop
							for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
								snake_case__				:   str     =						self.decode(
								    encodings_and_masks=UpperCamelCase_							,	input_tokens=UpperCamelCase_							,	noise_time=t / self.scheduler.config.num_train_timesteps							,	)

								# Compute previous output: x_t -> x_t-1
								snake_case__				:   str     =						self.scheduler.step(UpperCamelCase_							,	UpperCamelCase_							,	UpperCamelCase_							,	generator=UpperCamelCase_ ).prev_sample

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

							snake_case__				:   Optional[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(UpperCamelCase_							,	UpperCamelCase_ )

							logger.info("Generated segment"							,	UpperCamelCase_ )

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

						if not return_dict:
							return (output,)

						return AudioPipelineOutput(audios=UpperCamelCase_ )











import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							dict     ):
	return (data["data"], data["target"])







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							np.ndarray  ,						snake_case_      :							np.ndarray     ):
	snake_case__				:   Optional[int]     =						XGBClassifier()
	classifier.fit(snake_case_  ,						snake_case_     )
	return classifier







def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Any     =						load_iris()
	snake_case__,		snake_case__				:   str     =						data_handling(snake_case_     )
	snake_case__,		snake_case__,		snake_case__,		snake_case__				:   int     =						train_test_split(
	    snake_case_  ,						snake_case_  ,						test_size=0.25     )

	snake_case__				:   Dict     =						iris["target_names"]

	# Create an XGBoost Classifier from the training data
	snake_case__				:   Dict     =						xgboost(snake_case_  ,						snake_case_     )

	# Display the confusion matrix of the classifier with both training and test sets
	ConfusionMatrixDisplay.from_estimator(
	    snake_case_  ,						snake_case_  ,						snake_case_  ,						display_labels=snake_case_  ,						cmap="Blues"  ,						normalize="true"  ,						)
	plt.title("Normalized Confusion Matrix - IRIS Dataset"     )
	plt.show()


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()



