

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


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

SCREAMING_SNAKE_CASE_        =      256

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			["melgan"]


						def __init__(       self						,					A_						,					A_						,					A_						,					A_						,					A_						,					):







										'''simple docstring'''


										super().__init__()

										# From MELGAN
										_UpperCAmelCase       :		Optional[Any] 		=    math.log(1e-5	)  # Matches MelGAN training.
										_UpperCAmelCase       :		Optional[Any] 		=    4.0  # Largest value for most examples
										_UpperCAmelCase       :		str 		=    128

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


						def        _UpperCAmelCase				(       self						,					A_						,					A_=(-1.0, 1.0)						,					A_=False	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		Dict 		=    output_range
										if clip:
														_UpperCAmelCase       :		int 		=    torch.clip(A_						,					self.min_value						,					self.max_value	)
										# Scale to [0, 1].
										_UpperCAmelCase       :		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        _UpperCAmelCase				(       self						,					A_						,					A_=(-1.0, 1.0)						,					A_=False	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		List[Any] 		=    input_range
										_UpperCAmelCase       :		List[Any] 		=    torch.clip(A_						,					A_						,					A_	) 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						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    input_tokens > 0
										_UpperCAmelCase		,							_UpperCAmelCase       :		Dict 		=    self.notes_encoder(
										    encoder_input_tokens=A_						,					encoder_inputs_mask=A_	)

										_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[Any] 		=    self.continuous_encoder(
										    encoder_inputs=A_						,					encoder_inputs_mask=A_	)

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


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_	):







										'''simple docstring'''


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

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

										_UpperCAmelCase       :		List[Any] 		=    self.decoder(
										    encodings_and_masks=A_						,					decoder_input_tokens=A_						,					decoder_noise_time=A_	)
										return logits





						@torch.no_grad()
						def __call__(       self						,					A_						,					A_ = None						,					A_ = 100						,					A_ = True						,					A_ = "numpy"						,					A_ = None						,					A_ = 1						,					):







										'''simple docstring'''


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

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

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

														_UpperCAmelCase       :		Any 		=    self.scale_features(
														    A_						,					output_range=[-1.0, 1.0]						,					clip=A_	)

														_UpperCAmelCase       :		List[str] 		=    self.encode(
														    input_tokens=torch.IntTensor([encoder_input_tokens]	).to(device=self.device	)						,					continuous_inputs=A_						,					continuous_mask=A_						,					)

														# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
														_UpperCAmelCase       :		Tuple 		=    randn_tensor(
														    shape=encoder_continuous_inputs.shape						,					generator=A_						,					device=self.device						,					dtype=self.decoder.dtype						,					)

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

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

																		# Compute previous output: x_t -> x_t-1
																		_UpperCAmelCase       :		Tuple 		=    self.scheduler.step(A_						,					A_						,					A_						,					generator=A_	).prev_sample

														_UpperCAmelCase       :		Optional[Any] 		=    self.scale_to_features(A_						,					input_range=[-1.0, 1.0]	)
														_UpperCAmelCase       :		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(A_						,					A_	)

														logger.info("Generated segment"						,					A_	)

										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       :		Tuple 		=    self.melgan(input_features=full_pred_mel.astype(np.floataa	)	)
										else:
														_UpperCAmelCase       :		Tuple 		=    full_pred_mel

										if not return_dict:
														return (output,)

										return AudioPipelineOutput(audios=A_	)





from __future__ import annotations

from collections.abc import Callable

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Callable[[int | float], int | float]  ,      lowerCAmelCase:  int | float  ,      lowerCAmelCase:  int | float  ,      lowerCAmelCase:  int = 100  ,      )   ->       float:
				_UpperCAmelCase       :		str 		=    x_start
				_UpperCAmelCase       :		Union[str, Any] 		=    fnc(lowerCAmelCase )
				_UpperCAmelCase       :		Tuple 		=    0.0
				for _ in range(lowerCAmelCase ):
								# Approximates small segments of curve as linear and solve
								# for trapezoidal area
								_UpperCAmelCase       :		Union[str, Any] 		=    (x_end - x_start) / steps + xa
								_UpperCAmelCase       :		Optional[Any] 		=    fnc(lowerCAmelCase )
								area += abs(fxa + fxa ) * (xa - xa) / 2
								# Increment step
								_UpperCAmelCase       :		Any 		=    xa
								_UpperCAmelCase       :		Optional[Any] 		=    fxa
				return area


if __name__ == "__main__":

					def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str] )   ->       List[Any]:
									return x**3 + x**2

					print('f(x) = x^3 + x^2')
					print('The area between the curve, x = -5, x = 5 and the x axis is:')
					SCREAMING_SNAKE_CASE_        =      10
					while i <= 100000:
										print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
										i *= 10


