





import gc
import unittest

import numpy as np
import torch

from diffusers import DDPMScheduler, MidiProcessor, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
from diffusers.utils import require_torch_gpu, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_note_seq, require_onnxruntime

from ..pipeline_params import TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS, TOKENS_TO_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


_UpperCamelCase       :				Tuple           =			"""./tests/fixtures/elise_format0.mid"""







@unittest.skip('''The note-seq package currently throws an error on import''')
class 		_lowerCAmelCase(		_a						,       unittest.TestCase):

  """simple docstring"""







  lowerCamelCase__     	=       SpectrogramDiffusionPipeline
  lowerCamelCase__     	=       PipelineTesterMixin.required_optional_params - {
      '''callback''',
      '''latents''',
      '''callback_steps''',
      '''output_type''',
      '''num_images_per_prompt''',
  }
  lowerCamelCase__     	=       False
  lowerCamelCase__     	=       False
  lowerCamelCase__     	=       TOKENS_TO_AUDIO_GENERATION_PARAMS
  lowerCamelCase__     	=       TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Tuple:
     torch.manual_seed(0      )
     __A    =  SpectrogramNotesEncoder(
         max_length=20_48    ,							vocab_size=15_36    ,							d_model=7_68    ,							dropout_rate=0.1    ,							num_layers=1    ,							num_heads=1    ,							d_kv=4    ,							d_ff=20_48    ,							feed_forward_proj='''gated-gelu'''    ,							)

     __A    =  SpectrogramContEncoder(
         input_dims=1_28    ,							targets_context_length=2_56    ,							d_model=7_68    ,							dropout_rate=0.1    ,							num_layers=1    ,							num_heads=1    ,							d_kv=4    ,							d_ff=20_48    ,							feed_forward_proj='''gated-gelu'''    ,							)

     __A    =  TaFilmDecoder(
         input_dims=1_28    ,							targets_length=2_56    ,							max_decoder_noise_time=20000.0    ,							d_model=7_68    ,							num_layers=1    ,							num_heads=1    ,							d_kv=4    ,							d_ff=20_48    ,							dropout_rate=0.1    ,							)

     __A    =  DDPMScheduler()

     __A    =  {
         '''notes_encoder''': notes_encoder.eval(),
         '''continuous_encoder''': continuous_encoder.eval(),
         '''decoder''': decoder.eval(),
         '''scheduler''': scheduler,
         '''melgan''': None,
     }
     return components

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase=0      )->   Union[str, Any]:
     if str(UpperCAmelCase      ).startswith('''mps'''      ):
        __A    =  torch.manual_seed(UpperCAmelCase      )
     else:
        __A    =  torch.Generator(device=UpperCAmelCase      ).manual_seed(UpperCAmelCase      )
     __A    =  {
         '''input_tokens''': [
             [11_34, 90, 11_35, 11_33, 10_80, 1_12, 11_32, 10_80, 11_33, 10_79, 1_33, 11_32, 10_79, 11_33, 1] + [0] * 20_33
         ],
         '''generator''': generator,
         '''num_inference_steps''': 4,
         '''output_type''': '''mel''',
     }
     return inputs

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   str:
     __A    =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
     __A    =  self.get_dummy_components()
     __A    =  SpectrogramDiffusionPipeline(**UpperCAmelCase      )
     __A    =  pipe.to(UpperCAmelCase      )
     pipe.set_progress_bar_config(disable=UpperCAmelCase      )

     __A    =  self.get_dummy_inputs(UpperCAmelCase      )
     __A    =  pipe(**UpperCAmelCase      )
     __A    =  output.audios

     __A    =  mel[0, -3:, -3:]

     assert mel_slice.shape == (3, 3)
     __A    =  np.array(
         [-11.512925, -4.788215, -0.46172905, -2.051715, -10.539147, -10.970963, -9.091634, 4.0, 4.0]      )
     assert np.abs(mel_slice.flatten() - expected_slice      ).max() < 1e-2

  @skip_mps
  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Optional[Any]:
     return super().test_save_load_local()

  @skip_mps
  def 						SCREAMING_SNAKE_CASE__			(   self      )->   List[str]:
     return super().test_dict_tuple_outputs_equivalent()

  @skip_mps
  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Any:
     return super().test_save_load_optional_components()

  @skip_mps
  def 						SCREAMING_SNAKE_CASE__			(   self      )->   str:
     return super().test_attention_slicing_forward_pass()

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Dict:
     pass

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Union[str, Any]:
     pass





  @skip_mps
  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Optional[Any]:
     return super().test_progress_bar()







@slow
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class 		_lowerCAmelCase(		unittest.TestCase):

  """simple docstring"""







  def 						SCREAMING_SNAKE_CASE__			(   self      )->   int:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Dict:
     # TODO - test that pipeline can decode tokens in a callback
     # so that music can be played live
     __A    =  torch_device

     __A    =  SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''      )
     __A    =  pipe.melgan
     __A    =  None

     __A    =  pipe.to(UpperCAmelCase      )
     pipe.set_progress_bar_config(disable=UpperCAmelCase      )

     def callback(UpperCAmelCase    ,							UpperCAmelCase      ):
        # decode mel to audio
        __A    =  melgan(input_features=mel_output.astype(np.floataa      )      )[0]
        assert len(audio[0]      ) == 8_19_20 * (step + 1)
        # simulate that audio is played
        return audio

     __A    =  MidiProcessor()
     __A    =  processor(UpperCAmelCase      )

     __A    =  input_tokens[:3]
     __A    =  torch.manual_seed(0      )
     pipe(UpperCAmelCase    ,							num_inference_steps=5    ,							generator=UpperCAmelCase    ,							callback=UpperCAmelCase    ,							output_type='''mel'''      )

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Union[str, Any]:
     __A    =  torch_device

     __A    =  SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''      )
     __A    =  pipe.to(UpperCAmelCase      )
     pipe.set_progress_bar_config(disable=UpperCAmelCase      )
     __A    =  MidiProcessor()

     __A    =  processor(UpperCAmelCase      )
     # just run two denoising loops
     __A    =  input_tokens[:2]

     __A    =  torch.manual_seed(0      )
     __A    =  pipe(UpperCAmelCase    ,							num_inference_steps=2    ,							generator=UpperCAmelCase      )

     __A    =  output.audios[0]

     assert abs(np.abs(UpperCAmelCase      ).sum() - 3612.841      ) < 1e-1





  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Union[str, Any]:
     __A    =  torch_device

     __A    =  SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''      )
     __A    =  pipe.to(UpperCAmelCase      )
     pipe.set_progress_bar_config(disable=UpperCAmelCase      )

     __A    =  MidiProcessor()

     __A    =  processor(UpperCAmelCase      )

     # just run 4 denoising loops
     __A    =  input_tokens[:4]

     __A    =  torch.manual_seed(0      )
     __A    =  pipe(UpperCAmelCase    ,							num_inference_steps=1_00    ,							generator=UpperCAmelCase      )

     __A    =  output.audios[0]
     assert abs(np.abs(UpperCAmelCase      ).sum() - 9389.1111      ) < 5e-2








import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
    from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
    _UpperCamelCase       :				List[Any]           =			True
    from torch.cuda.amp import autocast


_UpperCamelCase       :				Optional[Any]           =			logging.getLogger(__name__)







@dataclass
class 		_lowerCAmelCase:

  """simple docstring"""







  lowerCamelCase__     	=       field(
      metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''})
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}						,       )
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''})
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''Whether to log verbose messages or not.'''}						,       )
def  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		int:


   '''simple docstring'''







   logging.basicConfig(
       format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''							,			datefmt='''%m/%d/%Y %H:%M:%S'''							,			handlers=[logging.StreamHandler(sys.stdout      )]							,			)
   __A    =  logging.WARNING
   if model_args.verbose_logging:
      __A    =  logging.DEBUG
   elif trainer_utils.is_main_process(training_args.local_rank      ):
      __A    =  logging.INFO
   logger.setLevel(snake_case      )







@dataclass
class 		_lowerCAmelCase:

  """simple docstring"""







  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''})
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''})
  lowerCamelCase__     	=       field(
      default='''train'''						,       metadata={
          '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
      }						,       )
  lowerCamelCase__     	=       field(
      default='''validation'''						,       metadata={
          '''help''': (
              '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
          )
      }						,       )
  lowerCamelCase__     	=       field(
      default='''text'''						,       metadata={'''help''': '''Column in the dataset that contains label (target text). Defaults to \'text\''''}						,       )
  lowerCamelCase__     	=       field(
      default='''file'''						,       metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''}						,       )
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''}						,       )
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''Filters out examples longer than specified. Defaults to no filtering.'''}						,       )
  lowerCamelCase__     	=       field(
      default='''librispeech'''						,       metadata={
          '''help''': (
              '''Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'''
              ''' \'buckwalter\'.'''
          )
      }						,       )
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''})
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''The number of processes to use for the preprocessing.'''}						,       )







@dataclass
class 		_lowerCAmelCase:

  """simple docstring"""







  lowerCamelCase__     	=       False
  lowerCamelCase__     	=       None
  lowerCamelCase__     	=       "|"
  lowerCamelCase__     	=       field(default_factory=_a)
  lowerCamelCase__     	=       field(default_factory=_a)
  lowerCamelCase__     	=       None

  @classmethod
  def 						SCREAMING_SNAKE_CASE__			(   cls    ,							UpperCAmelCase      )->   Optional[int]:
     if name == "librispeech":
        return cls()
     if name == "timit":
        return cls(
            do_lower_case=UpperCAmelCase    ,							translation_table=str.maketrans({'''-''': ''' '''}      )    ,							)
     if name == "buckwalter":
        __A    =  {
            '''-''': ''' ''',  # sometimes used to represent pauses
            '''^''': '''v''',  # fixing "tha" in arabic_speech_corpus dataset
        }
        return cls(
            vocab_file=pathlib.Path(UpperCAmelCase      ).parent.joinpath('''vocab/buckwalter.json'''      )    ,							word_delimiter_token='''/'''    ,							translation_table=str.maketrans(UpperCAmelCase      )    ,							words_to_remove={'''sil'''}    ,							untransliterator=arabic.buckwalter.untransliterate    ,							)
     raise ValueError(f"Unsupported orthography: '{name}'."      )

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   str:
     # TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
     if len(self.translation_table      ) > 0:
        __A    =  text.translate(self.translation_table      )
     if len(self.words_to_remove      ) == 0:
        __A    =  ''' '''.join(text.split()      )  # clean up whitespaces
     else:
        __A    =  ''' '''.join(w for w in text.split() if w not in self.words_to_remove      )  # and clean up whilespaces
     return text





  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   WavaVecaProcessor:
     __A    =  WavaVecaFeatureExtractor.from_pretrained(
         model_args.model_name_or_path    ,							cache_dir=model_args.cache_dir      )
     if self.vocab_file:
        __A    =  WavaVecaCTCTokenizer(
            self.vocab_file    ,							cache_dir=model_args.cache_dir    ,							do_lower_case=self.do_lower_case    ,							word_delimiter_token=self.word_delimiter_token    ,							)
     else:
        __A    =  WavaVecaCTCTokenizer.from_pretrained(
            model_args.model_name_or_path    ,							cache_dir=model_args.cache_dir    ,							do_lower_case=self.do_lower_case    ,							word_delimiter_token=self.word_delimiter_token    ,							)
     return WavaVecaProcessor(UpperCAmelCase    ,							UpperCAmelCase      )







@dataclass
class 		_lowerCAmelCase:

  """simple docstring"""







  lowerCamelCase__     	=       42
  lowerCamelCase__     	=       True
  lowerCamelCase__     	=       None
  lowerCamelCase__     	=       None
  lowerCamelCase__     	=       None
  lowerCamelCase__     	=       None

  def __call__(   self    ,							UpperCAmelCase      )->   Dict[str, torch.Tensor]:
     # split inputs and labels since they have to be of different lenghts and need
     # different padding methods
     __A    =  [{'''input_values''': feature['''input_values''']} for feature in features]
     __A    =  [{'''input_ids''': feature['''labels''']} for feature in features]

     __A    =  self.processor.pad(
         UpperCAmelCase    ,							padding=self.padding    ,							max_length=self.max_length    ,							pad_to_multiple_of=self.pad_to_multiple_of    ,							return_tensors='''pt'''    ,							)
     __A    =  self.processor.pad(
         labels=UpperCAmelCase    ,							padding=self.padding    ,							max_length=self.max_length_labels    ,							pad_to_multiple_of=self.pad_to_multiple_of_labels    ,							return_tensors='''pt'''    ,							)

     # replace padding with -100 to ignore loss correctly
     __A    =  labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1      )    ,							-1_00      )

     __A    =  labels

     return batch







class 		_lowerCAmelCase(		_a):

  """simple docstring"""







  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   torch.Tensor:
     model.train()
     __A    =  self._prepare_inputs(UpperCAmelCase      )

     if self.use_amp:
        with autocast():
           __A    =  self.compute_loss(UpperCAmelCase    ,							UpperCAmelCase      )
     else:
        __A    =  self.compute_loss(UpperCAmelCase    ,							UpperCAmelCase      )

     if self.args.n_gpu > 1:
        if model.module.config.ctc_loss_reduction == "mean":
           __A    =  loss.mean()
        elif model.module.config.ctc_loss_reduction == "sum":
           __A    =  loss.sum() / (inputs['''labels'''] >= 0).sum()
        else:
           raise ValueError(f"{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"      )

     if self.args.gradient_accumulation_steps > 1:
        __A    =  loss / self.args.gradient_accumulation_steps

     if self.use_amp:
        self.scaler.scale(UpperCAmelCase      ).backward()
     elif self.use_apex:
        with amp.scale_loss(UpperCAmelCase    ,							self.optimizer      ) as scaled_loss:
           scaled_loss.backward()
     elif self.deepspeed:
        self.deepspeed.backward(UpperCAmelCase      )
     else:
        loss.backward()

     return loss.detach()
def  __UpperCamelCase    (						)    ->		Any:


   '''simple docstring'''







   __A    =  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )

   __A   ,		__A   ,		__A    =  parser.parse_args_into_dataclasses()
   configure_logger(snake_case							,			snake_case      )

   __A    =  Orthography.from_name(data_args.orthography.lower()      )
   __A    =  orthography.create_processor(snake_case      )
   __A    =  WavaVecaForCTC.from_pretrained(
       model_args.model_name_or_path							,			cache_dir=model_args.cache_dir							,			gradient_checkpointing=training_args.gradient_checkpointing							,			vocab_size=len(processor.tokenizer      )							,			)

   __A    =  datasets.load_dataset(
       data_args.dataset_name							,			data_args.dataset_config_name							,			split=data_args.train_split_name      )
   __A    =  datasets.load_dataset(
       data_args.dataset_name							,			data_args.dataset_config_name							,			split=data_args.validation_split_name      )

   __A    =  datasets.load_metric('''wer'''      )
   __A    =  processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
   __A    =  ''''''.join(t for t in processor.tokenizer.get_vocab().keys() if len(snake_case      ) == 1      )
   __A    =  re.compile(  # remove characters not in vocabulary
       RF"[^\s{re.escape(snake_case      )}]"							,			flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0							,			)
   __A    =  []

   def prepare_example(snake_case      ):  # TODO(elgeish) make use of multiprocessing?
      __A   ,		__A    =  librosa.load(example[data_args.speech_file_column]							,			sr=snake_case      )
      if data_args.max_duration_in_seconds is not None:
         __A    =  len(example['''speech''']      ) / example['''sampling_rate''']
      # Normalize and clean up text; order matters!
      __A    =  orthography.preprocess_for_training(example[data_args.target_text_column]      )
      __A    =  vocabulary_text_cleaner.sub(''''''							,			snake_case      )
      if updated_text != example[data_args.target_text_column]:
         text_updates.append((example[data_args.target_text_column], updated_text)      )
         __A    =  updated_text
      return example

   __A    =  train_dataset.map(snake_case							,			remove_columns=[data_args.speech_file_column]      )
   __A    =  val_dataset.map(snake_case							,			remove_columns=[data_args.speech_file_column]      )

   if data_args.max_duration_in_seconds is not None:

      def filter_by_max_duration(snake_case      ):
         return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

      __A    =  len(snake_case      )
      __A    =  len(snake_case      )
      __A    =  train_dataset.filter(snake_case							,			remove_columns=['''duration_in_seconds''']      )
      __A    =  val_dataset.filter(snake_case							,			remove_columns=['''duration_in_seconds''']      )
      if len(snake_case      ) > old_train_size:
         logger.warning(
             F"Filtered out {len(snake_case      ) - old_train_size} train example(s) longer than"
             F" {data_args.max_duration_in_seconds} second(s)."      )
      if len(snake_case      ) > old_val_size:
         logger.warning(
             F"Filtered out {len(snake_case      ) - old_val_size} validation example(s) longer than"
             F" {data_args.max_duration_in_seconds} second(s)."      )
   logger.info(F"Split sizes: {len(snake_case      )} train and {len(snake_case      )} validation."      )

   logger.warning(F"Updated {len(snake_case      )} transcript(s) using '{data_args.orthography}' orthography rules."      )
   if logger.isEnabledFor(logging.DEBUG      ):
      for original_text, updated_text in text_updates:
         logger.debug(F"Updated text: \"{original_text}\" -> \"{updated_text}\""      )
   __A    =  None

   def prepare_dataset(snake_case      ):
      # check that all files have the correct sampling rate
      assert (
          len(set(batch['''sampling_rate''']      )      ) == 1
      ), F"Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."

      __A    =  processor(
          audio=batch['''speech''']							,			text=batch[data_args.target_text_column]							,			sampling_rate=batch['''sampling_rate'''][0]      )
      batch.update(snake_case      )
      return batch

   __A    =  train_dataset.map(
       snake_case							,			batch_size=training_args.per_device_train_batch_size							,			batched=snake_case							,			num_proc=data_args.preprocessing_num_workers							,			)
   __A    =  val_dataset.map(
       snake_case							,			batch_size=training_args.per_device_train_batch_size							,			batched=snake_case							,			num_proc=data_args.preprocessing_num_workers							,			)

   __A    =  DataCollatorCTCWithPadding(processor=snake_case							,			padding=snake_case      )

   def compute_metrics(snake_case      ):
      __A    =  pred.predictions
      __A    =  np.argmax(snake_case							,			axis=-1      )

      __A    =  processor.tokenizer.pad_token_id

      __A    =  processor.batch_decode(snake_case      )
      # we do not want to group tokens when computing the metrics
      __A    =  processor.batch_decode(pred.label_ids							,			group_tokens=snake_case      )
      if logger.isEnabledFor(logging.DEBUG      ):
         for reference, predicted in zip(snake_case							,			snake_case      ):
            logger.debug(F"reference: \"{reference}\""      )
            logger.debug(F"predicted: \"{predicted}\""      )
            if orthography.untransliterator is not None:
               logger.debug(F"reference (untransliterated): \"{orthography.untransliterator(snake_case      )}\""      )
               logger.debug(F"predicted (untransliterated): \"{orthography.untransliterator(snake_case      )}\""      )

      __A    =  wer_metric.compute(predictions=snake_case							,			references=snake_case      )

      return {"wer": wer}

   if model_args.freeze_feature_extractor:
      model.freeze_feature_extractor()

   __A    =  CTCTrainer(
       model=snake_case							,			data_collator=snake_case							,			args=snake_case							,			compute_metrics=snake_case							,			train_dataset=snake_case							,			eval_dataset=snake_case							,			tokenizer=processor.feature_extractor							,			)

   trainer.train()


if __name__ == "__main__":
    main()

