






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

__UpperCAmelCase							:   Tuple							=  2_5_6


class 					_snake_case						(			_A ):
	_A          =   ['melgan']






	def __init__(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,)						->    None:
					super().__init__()

					# From MELGAN
					snake_case__      :int         =   math.log(1E-5       )  # Matches MelGAN training.
					snake_case__      :List[Any]         =   4.0  # Largest value for most examples
					snake_case__      :List[str]         =   128

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






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

					snake_case__    ,						snake_case__      :List[Any]         =   self.continuous_encoder(
					    encoder_inputs=UpperCamelCase					,encoder_inputs_mask=UpperCamelCase       )

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






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    Union[str, Any]:
					snake_case__      :Tuple         =   noise_time
					if not torch.is_tensor(UpperCamelCase       ):
									snake_case__      :Tuple         =   torch.tensor([timesteps]					,dtype=torch.long					,device=input_tokens.device       )
					elif torch.is_tensor(UpperCamelCase       ) and len(timesteps.shape       ) == 0:
									snake_case__      :str         =   timesteps[None].to(input_tokens.device       )

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

					snake_case__      :str         =   self.decoder(
					    encodings_and_masks=UpperCamelCase					,decoder_input_tokens=UpperCamelCase					,decoder_noise_time=UpperCamelCase       )
					return logits






	@torch.no_grad()
	def __call__(		self					,UpperCamelCase					,UpperCamelCase = None					,UpperCamelCase = 100					,UpperCamelCase = True					,UpperCamelCase = "numpy"					,UpperCamelCase = None					,UpperCamelCase = 1					,)						->    Union[AudioPipelineOutput, Tuple]:
					if (callback_steps is None) or (
					    callback_steps is not None and (not isinstance(UpperCamelCase					,UpperCamelCase       ) or callback_steps <= 0)
					):
									raise ValueError(
									    f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
									    f' {type(UpperCamelCase       )}.'       )

					snake_case__      :List[Any]         =   np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]					,dtype=np.floataa       )
					snake_case__      :Any         =   np.zeros([1, 0, self.n_dims]					,np.floataa       )
					snake_case__      :List[str]         =   torch.ones((1, TARGET_FEATURE_LENGTH)					,dtype=UpperCamelCase					,device=self.device       )

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

									snake_case__      :List[Any]         =   self.scale_features(
									    UpperCamelCase					,output_range=[-1.0, 1.0]					,clip=UpperCamelCase       )

									snake_case__      :List[Any]         =   self.encode(
									    input_tokens=torch.IntTensor([encoder_input_tokens]       ).to(device=self.device       )					,continuous_inputs=UpperCamelCase					,continuous_mask=UpperCamelCase					,)

									# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
									snake_case__      :List[str]         =   randn_tensor(
									    shape=encoder_continuous_inputs.shape					,generator=UpperCamelCase					,device=self.device					,dtype=self.decoder.dtype					,)

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

									# Denoising diffusion loop
									for j, t in enumerate(self.progress_bar(self.scheduler.timesteps       )       ):
													snake_case__      :List[str]         =   self.decode(
													    encodings_and_masks=UpperCamelCase					,input_tokens=UpperCamelCase					,noise_time=t / self.scheduler.config.num_train_timesteps					,)

													# Compute previous output: x_t -> x_t-1
													snake_case__      :str         =   self.scheduler.step(UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,generator=UpperCamelCase       ).prev_sample

									snake_case__      :Any         =   self.scale_to_features(UpperCamelCase					,input_range=[-1.0, 1.0]       )
									snake_case__      :Union[str, Any]         =   mel[:1]
									snake_case__      :Tuple         =   mel.cpu().float().numpy()

									snake_case__      :List[Any]         =   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(UpperCamelCase					,UpperCamelCase       )

									logger.info("Generated segment"					,UpperCamelCase       )

					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__      :str         =   self.melgan(input_features=full_pred_mel.astype(np.floataa       )       )
					else:
									snake_case__      :Any         =   full_pred_mel

					if not return_dict:
									return (output,)

					return AudioPipelineOutput(audios=UpperCamelCase       )







import pytest


__UpperCAmelCase							:   int							=  "__dummy_dataset1__"

__UpperCAmelCase							:   int							=  "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n                \"ner_tags\": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            \"O\",\n                            \"B-PER\",\n                            \"I-PER\",\n                            \"B-ORG\",\n                            \"I-ORG\",\n                            \"B-LOC\",\n                            \"I-LOC\",\n                        ]\n                    )\n                ),\n                \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n                \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n"





@pytest.fixture
def 					lowercase_ (    )     -> Optional[Any]:





				'''simple docstring'''


				return DATASET_LOADING_SCRIPT_NAME





@pytest.fixture
def 					lowercase_ (    )     -> Optional[int]:





				'''simple docstring'''


				return DATASET_LOADING_SCRIPT_CODE





@pytest.fixture
def 					lowercase_ (    __snake_case     :							Optional[int]	,	__snake_case     :							List[Any]	,	__snake_case     :							Any      )     -> Dict:





				'''simple docstring'''


				snake_case__      :Optional[Any]         =   dataset_loading_script_name
				snake_case__      :Optional[Any]         =   tmp_path / "datasets" / script_name
				script_dir.mkdir(parents=__snake_case      )
				snake_case__      :List[Any]         =   script_dir / F'{script_name}.py'
				with open(__snake_case	,	"w"      ) as f:
								f.write(__snake_case      )
				return str(__snake_case      )