


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


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

UpperCAmelCase   :    Union[str, Any]     =		2_56







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Tuple			       =		["melgan"]







		def __init__( self		,      A		,      A		,      A		,      A		,      A		,      )  -> None:







					'''simple docstring'''







					super().__init__()

					# From MELGAN
					lowerCamelCase    		=  math.log(1e-5	)  # Matches MelGAN training.
					lowerCamelCase    		=  4.0  # Largest value for most examples
					lowerCamelCase    		=  1_28

					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	)  -> Optional[Any]:







					'''simple docstring'''







					lowerCamelCase		,		lowerCamelCase    		=  output_range
					if clip:
								lowerCamelCase    		=  torch.clip(A		,      self.min_value		,      self.max_value	)
					# Scale to [0, 1].
					lowerCamelCase    		=  (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	)  -> Any:







					'''simple docstring'''







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







					'''simple docstring'''







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

					lowerCamelCase		,		lowerCamelCase    		=  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	)  -> Any:







					'''simple docstring'''







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

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

					lowerCamelCase    		=  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_00		,      A = True		,      A = "numpy"		,      A = None		,      A = 1		,      )  -> Union[AudioPipelineOutput, Tuple]:







					'''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	)}.'	)

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

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

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

								lowerCamelCase    		=  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
								lowerCamelCase    		=  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	)	):
											lowerCamelCase    		=  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
											lowerCamelCase    		=  self.scheduler.step(A		,      A		,      A		,      generator=A	).prev_sample

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

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

					if not return_dict:
								return (output,)

					return AudioPipelineOutput(audios=A	)





import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


UpperCAmelCase   :    Optional[Any]     =		logging.getLogger(__name__)







@dataclass
class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[float]			       =		field(
		    default=0.0			,							metadata={"help": "The label smoothing epsilon to apply (if not zero)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "Whether to SortishSamler or not."}  )
		UpperCamelCase     :					bool			       =		field(
		    default=a_			,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "whether to use adafactor"}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Encoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Decoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(default=a_			,							metadata={"help": "Dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Attention dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[str]			       =		field(
		    default="linear"			,							metadata={"help": f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()  )}'''}			,							)

