


'''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							:   Optional[Any]  						=	logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase							:   Dict  						=	256
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     List[Any]  					=							["""melgan"""]



      def __init__(self    :    Tuple   ,						UpperCamelCase    :    SpectrogramNotesEncoder   ,						UpperCamelCase    :    SpectrogramContEncoder   ,						UpperCamelCase    :    TaFilmDecoder   ,						UpperCamelCase    :    DDPMScheduler   ,						UpperCamelCase    :    OnnxRuntimeModel if is_onnx_available() else Any   ,						):

             '''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=UpperCamelCase   ,						continuous_encoder=UpperCamelCase   ,						decoder=UpperCamelCase   ,						scheduler=UpperCamelCase   ,						melgan=UpperCamelCase   ,						)



      def       UpperCamelCase__		(self    :    str   ,						UpperCamelCase    :    Dict   ,						UpperCamelCase    :    Tuple=(-1.0, 1.0)   ,						UpperCamelCase    :    int=False ):

             '''simple docstring'''







             lowercase__			,lowercase__      			= output_range
             if clip:
                    lowercase__      			= torch.clip(UpperCamelCase   ,						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]   ,						UpperCamelCase    :    List[str]   ,						UpperCamelCase    :    Union[str, Any]=(-1.0, 1.0)   ,						UpperCamelCase    :    Dict=False ):

             '''simple docstring'''







             lowercase__			,lowercase__      			= input_range
             lowercase__      			= torch.clip(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase ) 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    :    Dict   ,						UpperCamelCase    :    List[Any]   ,						UpperCamelCase    :    List[str]   ,						UpperCamelCase    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__      			= input_tokens > 0
             lowercase__			,lowercase__      			= self.notes_encoder(
                 encoder_input_tokens=UpperCamelCase   ,						encoder_inputs_mask=UpperCamelCase )

             lowercase__			,lowercase__      			= self.continuous_encoder(
                 encoder_inputs=UpperCamelCase   ,						encoder_inputs_mask=UpperCamelCase )

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



      def       UpperCamelCase__		(self    :    Dict   ,						UpperCamelCase    :    Union[str, Any]   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    Any ):

             '''simple docstring'''







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






      @torch.no_grad()
      def __call__(self    :    List[Any]   ,						UpperCamelCase    :    List[List[int]]   ,						UpperCamelCase    :    Optional[torch.Generator] = None   ,						UpperCamelCase    :    int = 100   ,						UpperCamelCase    :    bool = True   ,						UpperCamelCase    :    str = "numpy"   ,						UpperCamelCase    :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None   ,						UpperCamelCase    :    int = 1   ,						):

             '''simple docstring'''







             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 )}." )

             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=UpperCamelCase   ,						device=self.device )

             for i, encoder_input_tokens in enumerate(UpperCamelCase ):
                    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=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.
                           lowercase__      			= ones

                    lowercase__      			= self.scale_features(
                        UpperCamelCase   ,						output_range=[-1.0, 1.0]   ,						clip=UpperCamelCase )

                    lowercase__      			= 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
                    lowercase__      			= 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 ) ):
                           lowercase__      			= 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
                           lowercase__      			= self.scheduler.step(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase   ,						generator=UpperCamelCase ).prev_sample

                    lowercase__      			= self.scale_to_features(UpperCamelCase   ,						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(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":
                    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=UpperCamelCase )









"""simple docstring"""
import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline

def       _snake_case						(						UpperCamelCase						:		Dataset			,		UpperCamelCase						:		Dict[str, str]						):
 UpperCAmelCase	:					Any	=	args.log_outputs
 UpperCAmelCase	:					Any	=	"""_""".join(args.dataset.split("""/"""						) + [args.config, args.split]						)

 # load metric
 UpperCAmelCase	:					List[Any]	=	load_metric("""wer"""						)
 UpperCAmelCase	:					Any	=	load_metric("""cer"""						)

 # compute metrics
 UpperCAmelCase	:					int	=	wer.compute(references=result["""target"""]			,		predictions=result["""prediction"""]						)
 UpperCAmelCase	:					str	=	cer.compute(references=result["""target"""]			,		predictions=result["""prediction"""]						)

 # print & log results
 UpperCAmelCase	:					Tuple	=	F"WER: {wer_result}\nCER: {cer_result}"
 print(UpperCamelCase						)

 with open(F"{dataset_id}_eval_results.txt"			,		"""w"""						) as f:
  f.write(UpperCamelCase						)

 # log all results in text file. Possibly interesting for analysis
 if log_outputs is not None:
  UpperCAmelCase	:					str	=	F"log_{dataset_id}_predictions.txt"
  UpperCAmelCase	:					Tuple	=	F"log_{dataset_id}_targets.txt"

  with open(UpperCamelCase			,		"""w"""						) as p, open(UpperCamelCase			,		"""w"""						) as t:
   # mapping function to write output
   def write_to_file(UpperCamelCase						:		List[Any]			,		UpperCamelCase						:		List[Any]						):
    p.write(F"{i}" + """\n"""						)
    p.write(batch["""prediction"""] + """\n"""						)
    t.write(F"{i}" + """\n"""						)
    t.write(batch["""target"""] + """\n"""						)

   result.map(UpperCamelCase			,		with_indices=UpperCamelCase						)

def       _snake_case						(						UpperCamelCase						:		str						):
 UpperCAmelCase	:					List[str]	=	"""[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

 UpperCAmelCase	:					Dict	=	re.sub(UpperCamelCase			,		""""""			,		text.lower()						)

 # In addition, we can normalize the target text, e.g. removing new lines characters etc...
 # note that order is important here!
 UpperCAmelCase	:					List[str]	=	["""\n\n""", """\n""", """   """, """  """]

 for t in token_sequences_to_ignore:
  UpperCAmelCase	:					Optional[Any]	=	""" """.join(text.split(UpperCamelCase						)						)

 return text

def       _snake_case						(						UpperCamelCase						:		Tuple						):
 # load dataset
 UpperCAmelCase	:					Union[str, Any]	=	load_dataset(args.dataset			,		args.config			,		split=args.split			,		use_auth_token=UpperCamelCase						)

 # for testing: only process the first two examples as a test
 # dataset = dataset.select(range(10))

 # load processor
 UpperCAmelCase	:					Optional[int]	=	AutoFeatureExtractor.from_pretrained(args.model_id						)
 UpperCAmelCase	:					Any	=	feature_extractor.sampling_rate

 # resample audio
 UpperCAmelCase	:					List[str]	=	dataset.cast_column("""audio"""			,		Audio(sampling_rate=UpperCamelCase						)						)

 # load eval pipeline
 if args.device is None:
  UpperCAmelCase	:					Optional[int]	=	0 if torch.cuda.is_available() else -1
 UpperCAmelCase	:					Tuple	=	pipeline("""automatic-speech-recognition"""			,		model=args.model_id			,		device=args.device						)

 # map function to decode audio
 def map_to_pred(UpperCamelCase						:		Any						):
  UpperCAmelCase	:					Any	=	asr(
      batch["""audio"""]["""array"""]			,		chunk_length_s=args.chunk_length_s			,		stride_length_s=args.stride_length_s						)

  UpperCAmelCase	:					Tuple	=	prediction["""text"""]
  UpperCAmelCase	:					List[str]	=	normalize_text(batch["""sentence"""]						)
  return batch

 # run inference on all examples
 UpperCAmelCase	:					int	=	dataset.map(UpperCamelCase			,		remove_columns=dataset.column_names						)

 # compute and log_results
 # do not change function below
 log_results(UpperCamelCase			,		UpperCamelCase						)


if __name__ == "__main__":
      A:   List[Any]								=		argparse.ArgumentParser()

      parser.add_argument(
          "--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
      )
      parser.add_argument(
          "--dataset",
          type=str,
          required=True,
          help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
      )
      parser.add_argument(
          "--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'`  for Common Voice"
      )
      parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
      parser.add_argument(
          "--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
      )
      parser.add_argument(
          "--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
      )
      parser.add_argument(
          "--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
      )
      parser.add_argument(
          "--device",
          type=int,
          default=None,
          help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
      )
      A:   Union[str, Any]								=		parser.parse_args()

      main(args)
