



"""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      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	Tuple 							=     ["""melgan"""]



		def __init__( self  :    Optional[int]      ,							snake_case  :    SpectrogramNotesEncoder      ,							snake_case  :    SpectrogramContEncoder      ,							snake_case  :    TaFilmDecoder      ,							snake_case  :    DDPMScheduler      ,							snake_case  :    OnnxRuntimeModel if is_onnx_available() else Any      ,							)			->						None:






									'''simple docstring'''

									super().__init__()

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

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



		def 					A_		( self  :    str      ,							snake_case  :    Tuple      ,							snake_case  :    Dict=(-1.0, 1.0)      ,							snake_case  :    Dict=False				)			->						Optional[Any]:






									'''simple docstring'''

									A      ,  A         =					output_range
									if clip:
																A         =					torch.clip(snake_case      ,							self.min_value      ,							self.max_value				)
									# Scale to [0, 1].
									A         =					(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  :    str      ,							snake_case  :    Optional[Any]      ,							snake_case  :    int=(-1.0, 1.0)      ,							snake_case  :    Optional[Any]=False				)			->						Optional[int]:






									'''simple docstring'''

									A      ,  A         =					input_range
									A         =					torch.clip(snake_case      ,							snake_case      ,							snake_case				) if clip else outputs
									# Scale to [0, 1].
									A         =					(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      ,							snake_case  :    List[str]      ,							snake_case  :    Tuple      ,							snake_case  :    Tuple				)			->						List[Any]:






									'''simple docstring'''

									A         =					input_tokens > 0
									A      ,  A         =					self.notes_encoder(
									    encoder_input_tokens=snake_case      ,							encoder_inputs_mask=snake_case				)

									A      ,  A         =					self.continuous_encoder(
									    encoder_inputs=snake_case      ,							encoder_inputs_mask=snake_case				)

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



		def 					A_		( self  :    str      ,							snake_case  :    Optional[Any]      ,							snake_case  :    Any      ,							snake_case  :    Dict				)			->						List[str]:






									'''simple docstring'''

									A         =					noise_time
									if not torch.is_tensor(snake_case				):
																A         =					torch.tensor([timesteps]      ,							dtype=torch.long      ,							device=input_tokens.device				)
									elif torch.is_tensor(snake_case				) and len(timesteps.shape				) == 0:
																A         =					timesteps[None].to(input_tokens.device				)

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

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



		@torch.no_grad()
		def __call__( self  :    Tuple      ,							snake_case  :    List[List[int]]      ,							snake_case  :    Optional[torch.Generator] = None      ,							snake_case  :    int = 100      ,							snake_case  :    bool = True      ,							snake_case  :    str = "numpy"      ,							snake_case  :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,							snake_case  :    int = 1      ,							)			->						Union[AudioPipelineOutput, Tuple]:






									'''simple docstring'''

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

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

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

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

																A         =					self.encode(
																    input_tokens=torch.IntTensor([encoder_input_tokens]				).to(device=self.device				)      ,							continuous_inputs=snake_case      ,							continuous_mask=snake_case      ,							)

																# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
																A         =					randn_tensor(
																    shape=encoder_continuous_inputs.shape      ,							generator=snake_case      ,							device=self.device      ,							dtype=self.decoder.dtype      ,							)

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

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

																							# Compute previous output: x_t -> x_t-1
																							A         =					self.scheduler.step(snake_case      ,							snake_case      ,							snake_case      ,							generator=snake_case				).prev_sample

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

																A         =					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(snake_case      ,							snake_case				)

																logger.info('Generated segment'      ,							snake_case				)

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

									if not return_dict:
																return (output,)

									return AudioPipelineOutput(audios=snake_case				)








"""simple docstring"""







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A    =			{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'MraForMaskedLM',
							    'MraForMultipleChoice',
							    'MraForQuestionAnswering',
							    'MraForSequenceClassification',
							    'MraForTokenClassification',
							    'MraLayer',
							    'MraModel',
							    'MraPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mra import (
														    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MraForMaskedLM,
														    MraForMultipleChoice,
														    MraForQuestionAnswering,
														    MraForSequenceClassification,
														    MraForTokenClassification,
														    MraLayer,
														    MraModel,
														    MraPreTrainedModel,
														)
else:
							import sys

							A    =			_LazyModule(__name__, globals()['__file__'], _import_structure)



