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

__lowerCAmelCase					: str        =2_5_6





class 				_A		( lowerCAmelCase			):
      snake_case__    :     str													=			['melgan']

      def __init__(		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, ):







            """simple docstring"""



            super().__init__()

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

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

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase=(-1.0, 1.0)	, __lowerCAmelCase=False		):







            """simple docstring"""



            lowercase							,							lowercase														=			output_range
            if clip:
                  lowercase														=			torch.clip(__lowerCAmelCase	, 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        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase=(-1.0, 1.0)	, __lowerCAmelCase=False		):







            """simple docstring"""



            lowercase							,							lowercase														=			input_range
            lowercase														=			torch.clip(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		) 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        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			input_tokens > 0
            lowercase							,							lowercase														=			self.notes_encoder(
                encoder_input_tokens=__lowerCAmelCase	, encoder_inputs_mask=__lowerCAmelCase		)

            lowercase							,							lowercase														=			self.continuous_encoder(
                encoder_inputs=__lowerCAmelCase	, encoder_inputs_mask=__lowerCAmelCase		)

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

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			noise_time
            if not torch.is_tensor(__lowerCAmelCase		):
                  lowercase														=			torch.tensor([timesteps]	, dtype=torch.long	, device=input_tokens.device		)
            elif torch.is_tensor(__lowerCAmelCase		) 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=__lowerCAmelCase	, decoder_input_tokens=__lowerCAmelCase	, decoder_noise_time=__lowerCAmelCase		)
            return logits







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







            """simple docstring"""



            if (callback_steps is None) or (
                callback_steps is not None and (not isinstance(__lowerCAmelCase	, __lowerCAmelCase		) or callback_steps <= 0)
            ):
                  raise ValueError(
                      f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
                      f' {type(__lowerCAmelCase		)}.'		)

            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=__lowerCAmelCase	, device=self.device		)

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

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

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

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

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

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

                  lowercase														=			self.scale_to_features(__lowerCAmelCase	, 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(__lowerCAmelCase	, __lowerCAmelCase		)

                  logger.info("""Generated segment"""	, __lowerCAmelCase		)

            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=__lowerCAmelCase		)




"""simple docstring"""






import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


__lowerCAmelCase					: str        ="""\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

__lowerCAmelCase					: str        ="""\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

__lowerCAmelCase					: Optional[int]        ="""
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""





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

      def        A__ (		self		):







            """simple docstring"""



            return datasets.MetricInfo(
                description=_DESCRIPTION	, citation=_CITATION	, inputs_description=_KWARGS_DESCRIPTION	, features=datasets.Features(
                    {
                        """predictions""": datasets.Value("""string"""	, id="""sequence"""		),
                        """references""": datasets.Value("""string"""	, id="""sequence"""		),
                    }		)	, codebase_urls=["""https://github.com/google-research/google-research/tree/master/rouge"""]	, reference_urls=[
                    """https://en.wikipedia.org/wiki/ROUGE_(metric)""",
                    """https://github.com/google-research/google-research/tree/master/rouge""",
                ]	, )







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase=None	, __lowerCAmelCase=True	, __lowerCAmelCase=False		):







            """simple docstring"""



            if rouge_types is None:
                  lowercase														=			["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]

            lowercase														=			rouge_scorer.RougeScorer(rouge_types=__lowerCAmelCase	, use_stemmer=__lowerCAmelCase		)
            if use_aggregator:
                  lowercase														=			scoring.BootstrapAggregator()
            else:
                  lowercase														=			[]

            for ref, pred in zip(__lowerCAmelCase	, __lowerCAmelCase		):
                  lowercase														=			scorer.score(__lowerCAmelCase	, __lowerCAmelCase		)
                  if use_aggregator:
                        aggregator.add_scores(__lowerCAmelCase		)
                  else:
                        scores.append(__lowerCAmelCase		)

            if use_aggregator:
                  lowercase														=			aggregator.aggregate()
            else:
                  lowercase														=			{}
                  for key in scores[0]:
                        lowercase														=			[score[key] for score in scores]

            return result



