
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


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

lowercase__	:Dict										=					256







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : str       =['''melgan''']




							def __init__(       self     ,A__     ,A__     ,A__     ,A__     ,A__     ,):
												super().__init__()

												# From MELGAN
												lowercase					=		math.log(1E-5)  # Matches MelGAN training.
												lowercase					=		4.0  # Largest value for most examples
												lowercase					=		1_2_8

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




							def 				A__   (       self     ,A__     ,A__=(-1.0, 1.0)     ,A__=False):
												lowercase			,		lowercase					=		output_range
												if clip:
																	lowercase					=		torch.clip(A__     ,self.min_value     ,self.max_value)
												# Scale to [0, 1].
												lowercase					=		(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     ,A__     ,A__=(-1.0, 1.0)     ,A__=False):
												lowercase			,		lowercase					=		input_range
												lowercase					=		torch.clip(A__     ,A__     ,A__) if clip else outputs
												# Scale to [0, 1].
												lowercase					=		(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     ,A__     ,A__     ,A__):
												lowercase					=		input_tokens > 0
												lowercase			,		lowercase					=		self.notes_encoder(
												    encoder_input_tokens=A__     ,encoder_inputs_mask=A__)

												lowercase			,		lowercase					=		self.continuous_encoder(
												    encoder_inputs=A__     ,encoder_inputs_mask=A__)

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




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

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

												lowercase					=		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__ = 1_0_0     ,A__ = True     ,A__ = "numpy"     ,A__ = None     ,A__ = 1     ,):
												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__)}.')

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

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

																	lowercase					=		self.scale_features(
																	    A__     ,output_range=[-1.0, 1.0]     ,clip=A__)

																	lowercase					=		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
																	lowercase					=		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)):
																						lowercase					=		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
																						lowercase					=		self.scheduler.step(A__     ,A__     ,A__     ,generator=A__).prev_sample

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

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

												if not return_dict:
																	return (output,)

												return AudioPipelineOutput(audios=A__)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase__	:Tuple										=					{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__	:Union[str, Any]										=					[
							    "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "BioGptForCausalLM",
							    "BioGptForTokenClassification",
							    "BioGptForSequenceClassification",
							    "BioGptModel",
							    "BioGptPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
							from .tokenization_biogpt import BioGptTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_biogpt import (
														    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BioGptForCausalLM,
														    BioGptForSequenceClassification,
														    BioGptForTokenClassification,
														    BioGptModel,
														    BioGptPreTrainedModel,
														)


else:
							import sys

							lowercase__	:Any										=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




