

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

_UpperCAmelCase	:     Any		       = 256







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Optional[Any]     =    ['''melgan''']




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

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

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




				def 					a							(      self   ,     snake_case   ,     snake_case=(-1.0, 1.0)   ,     snake_case=False ):
											snake_case_       ,					snake_case_           =					output_range
											if clip:
																		snake_case_           =					torch.clip(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 					a							(      self   ,     snake_case   ,     snake_case=(-1.0, 1.0)   ,     snake_case=False ):
											snake_case_       ,					snake_case_           =					input_range
											snake_case_           =					torch.clip(snake_case   ,     snake_case   ,     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 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case ):
											snake_case_           =					input_tokens > 0
											snake_case_       ,					snake_case_           =					self.notes_encoder(
											    encoder_input_tokens=snake_case   ,     encoder_inputs_mask=snake_case )

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

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




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case ):
											snake_case_           =					noise_time
											if not torch.is_tensor(snake_case ):
																		snake_case_           =					torch.tensor([timesteps]   ,     dtype=torch.long   ,     device=input_tokens.device )
											elif torch.is_tensor(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=snake_case   ,     decoder_input_tokens=snake_case   ,     decoder_noise_time=snake_case )
											return logits




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

											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=snake_case   ,     device=self.device )

											for i, encoder_input_tokens in enumerate(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=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(
																		    snake_case   ,     output_range=[-1.0, 1.0]   ,     clip=snake_case )

																		snake_case_           =					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
																		snake_case_           =					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 ) ):
																									snake_case_           =					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
																									snake_case_           =					self.scheduler.step(snake_case   ,     snake_case   ,     snake_case   ,     generator=snake_case ).prev_sample

																		snake_case_           =					self.scale_to_features(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(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":
																		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=snake_case )







from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset







class 				lowercase       ( lowercase_    ):




				def 					a							(      self ):
											return [
											    {"col_1": 3, "col_2": "a"},
											    {"col_1": 2, "col_2": "b"},
											    {"col_1": 1, "col_2": "c"},
											    {"col_1": 0, "col_2": "d"},
											]




				def 					a							(      self ):
											snake_case_           =					{'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
											return Dataset.from_dict(snake_case )




				def 					a							(      self ):
											snake_case_           =					self._create_example_records()
											snake_case_           =					Dataset.from_list(snake_case )
											self.assertListEqual(dset.column_names   ,     ['col_1', 'col_2'] )
											for i, r in enumerate(snake_case ):
																		self.assertDictEqual(snake_case   ,     example_records[i] )




				def 					a							(      self ):
											snake_case_           =					self._create_example_records()
											snake_case_           =					Dataset.from_list(snake_case )
											snake_case_           =					Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]} )
											self.assertEqual(dset.info   ,     dset_from_dict.info )




				def 					a							(      self ):  # checks what happens with missing columns
											snake_case_           =					[{'col_1': 1}, {'col_2': 'x'}]
											snake_case_           =					Dataset.from_list(snake_case )
											self.assertDictEqual(dset[0]   ,     {'col_1': 1} )
											self.assertDictEqual(dset[1]   ,     {'col_1': None} )  # NB: first record is used for columns




				def 					a							(      self ):  # checks if the type can be inferred from the second record
											snake_case_           =					[{'col_1': []}, {'col_1': [1, 2]}]
											snake_case_           =					Dataset.from_list(snake_case )
											self.assertEqual(dset.info.features['col_1']   ,     Sequence(Value('int64' ) ) )




				def 					a							(      self ):
											snake_case_           =					Dataset.from_list([] )
											self.assertEqual(len(snake_case )   ,     0 )
											self.assertListEqual(dset.column_names   ,     [] )




