




'''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


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

_lowercase  =			2_56
class 						a_				(			a_						):
	lowercase_       :     Optional[Any]			  =      ['''melgan''']




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

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

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




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

							__snake_case ,       __snake_case						 =   self.continuous_encoder(
							    encoder_inputs=__SCREAMING_SNAKE_CASE       ,		encoder_inputs_mask=__SCREAMING_SNAKE_CASE      )

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




	def   lowercase__   (       self     :  List[Any]       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  str      ):
							__snake_case						 =   noise_time
							if not torch.is_tensor(__SCREAMING_SNAKE_CASE      ):
													__snake_case						 =   torch.tensor([timesteps]       ,		dtype=torch.long       ,		device=input_tokens.device      )
							elif torch.is_tensor(__SCREAMING_SNAKE_CASE      ) and len(timesteps.shape      ) == 0:
													__snake_case						 =   timesteps[None].to(input_tokens.device      )

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

							__snake_case						 =   self.decoder(
							    encodings_and_masks=__SCREAMING_SNAKE_CASE       ,		decoder_input_tokens=__SCREAMING_SNAKE_CASE       ,		decoder_noise_time=__SCREAMING_SNAKE_CASE      )
							return logits




	@torch.no_grad()
	def __call__(       self     :  Tuple       ,		__lowerCAmelCase     :  List[List[int]]       ,		__lowerCAmelCase     :  Optional[torch.Generator] = None       ,		__lowerCAmelCase     :  int = 1_0_0       ,		__lowerCAmelCase     :  bool = True       ,		__lowerCAmelCase     :  str = "numpy"       ,		__lowerCAmelCase     :  Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,		__lowerCAmelCase     :  int = 1       ,		):
							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_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(__SCREAMING_SNAKE_CASE      )}.'      )

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

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

													__snake_case						 =   self.scale_features(
													    __SCREAMING_SNAKE_CASE       ,		output_range=[-1.0, 1.0]       ,		clip=__SCREAMING_SNAKE_CASE      )

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

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

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

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

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

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

													__snake_case						 =   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(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE      )

													logger.info('Generated segment'       ,		__SCREAMING_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":
													__snake_case						 =   self.melgan(input_features=full_pred_mel.astype(np.floataa      )      )
							else:
													__snake_case						 =   full_pred_mel

							if not return_dict:
													return (output,)

							return AudioPipelineOutput(audios=__SCREAMING_SNAKE_CASE      )


import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 return (abs(source - target       ) / target) < 0.01






@pytest.mark.integration
def 	__A		(      _A       ):



 """simple docstring"""


 __a							=	_TestCommandArgs(dataset=_A				,					all_configs=_A				,					save_infos=_A       )
 __a							=	TestCommand(*_A       )
 test_command.run()
 __a							=	os.path.join(_A				,					"README.md"       )
 assert os.path.exists(_A       )
 __a							=	DatasetInfosDict.from_directory(_A       )
 __a							=	DatasetInfosDict(
     {
         "default": DatasetInfo(
             features=Features(
                 {
                     "tokens": Sequence(Value("string"       )       ),
                     "ner_tags": Sequence(
                         ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]       )       ),
                     "langs": Sequence(Value("string"       )       ),
                     "spans": Sequence(Value("string"       )       ),
                 }       )				,					splits=[
                 {
                     "name": "train",
                     "num_bytes": 235_1563,
                     "num_examples": 1_0000,
                 },
                 {
                     "name": "validation",
                     "num_bytes": 23_8418,
                     "num_examples": 1000,
                 },
             ]				,					download_size=394_0680				,					dataset_size=258_9981				,					)
     }       )
 assert dataset_infos.keys() == expected_dataset_infos.keys()
 for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
  __a							,  __a							=	getattr(dataset_infos["default"]				,					_A       ), getattr(expected_dataset_infos["default"]				,					_A       )
  if key == "num_bytes":
   assert is_apercent_close(_A				,					_A       )
  elif key == "splits":
   assert list(_A       ) == list(_A       )
   for split in result:
    assert result[split].name == expected[split].name
    assert result[split].num_examples == expected[split].num_examples
    assert is_apercent_close(result[split].num_bytes				,					expected[split].num_bytes       )
  else:
   result == expected


