





'''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


__lowerCamelCase : Union[str, Any]											=			logging.get_logger(__name__)  # pylint: disable=invalid-name

__lowerCamelCase : Optional[Any]											=			256

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	Tuple 			=		['''melgan''']




		def __init__(self      :	str							,			A__      :	SpectrogramNotesEncoder							,			A__      :	SpectrogramContEncoder							,			A__      :	TaFilmDecoder							,			A__      :	DDPMScheduler							,			A__      :	OnnxRuntimeModel if is_onnx_available() else Any							,			)					-> None:
						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 				UpperCAmelCase__   (self      :	int							,			A__      :	Optional[Any]							,			A__      :	Dict=(-1.0, 1.0)							,			A__      :	List[Any]=False				)					-> List[Any]:
						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 				UpperCAmelCase__   (self      :	Union[str, Any]							,			A__      :	int							,			A__      :	Union[str, Any]=(-1.0, 1.0)							,			A__      :	Tuple=False				)					-> Dict:
						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 				UpperCAmelCase__   (self      :	Tuple							,			A__      :	Dict							,			A__      :	Tuple							,			A__      :	Any				)					-> List[Any]:
						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 				UpperCAmelCase__   (self      :	Optional[Any]							,			A__      :	List[Any]							,			A__      :	Tuple							,			A__      :	Dict				)					-> Tuple:
						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      :	Tuple							,			A__      :	List[List[int]]							,			A__      :	Optional[torch.Generator] = None							,			A__      :	int = 1_0_0							,			A__      :	bool = True							,			A__      :	str = "numpy"							,			A__      :	Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,			A__      :	int = 1							,			)					-> Union[AudioPipelineOutput, Tuple]:
						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__				)











'''simple docstring'''

import datasets

from .evaluate import evaluate


__lowerCamelCase : Optional[Any]											=			"\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n"

__lowerCamelCase : Any											=			"\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n"

__lowerCamelCase : Union[str, Any]											=			"\nComputes SQuAD scores (F1 and EM).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': the text of the answer\n    references: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair (see above),\n        - 'answers': a Dict in the SQuAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\nExamples:\n\n    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]\n    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\n    >>> squad_metric = datasets.load_metric(\"squad\")\n    >>> results = squad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0}\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,       _KWARGS_DESCRIPTION  )
class       UpperCAmelCase (						datasets.Metric  ):




		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Union[str, Any]:
						return datasets.MetricInfo(
						    description=_DESCRIPTION							,			citation=_CITATION							,			inputs_description=_KWARGS_DESCRIPTION							,			features=datasets.Features(
						        {
						            "predictions": {"id": datasets.Value("string"				), "prediction_text": datasets.Value("string"				)},
						            "references": {
						                "id": datasets.Value("string"				),
						                "answers": datasets.features.Sequence(
						                    {
						                        "text": datasets.Value("string"				),
						                        "answer_start": datasets.Value("int32"				),
						                    }				),
						            },
						        }				)							,			codebase_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]							,			reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]							,			)





		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	List[Any]							,			A__      :	List[str]				)					-> str:
						lowercase         = {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
						lowercase         = [
						    {
						        "paragraphs": [
						            {
						                "qas": [
						                    {
						                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
						                        "id": ref["id"],
						                    }
						                    for ref in references
						                ]
						            }
						        ]
						    }
						]
						lowercase         = evaluate(dataset=A__							,			predictions=A__				)
						return score




