
"""simple docstring"""
import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
    pass

if is_speech_available():
    from transformers import EncodecFeatureExtractor, MusicgenProcessor


_a   :      Optional[int]       =				random.Random()
def 					a__ ( a :					int							,  a :					List[Any]=1.0							,  a :					Tuple=None							,  a :					str=None		):

  """simple docstring"""







  if rng is None:
    _snake_case  :    Dict            = global_rng

  _snake_case  :    Union[str, Any]            = []
  for batch_idx in range(shape[0]		):
    values.append([]		)
    for _ in range(shape[1]		):
      values[-1].append(rng.random() * scale		)

  return values






@require_torch
@require_sentencepiece
class   _UpperCAmelCase					(       unittest.TestCase):
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[str]            = "facebook/musicgen-small"
      _snake_case  :    Dict            = tempfile.mkdtemp()
    def        lowerCamelCase__						(     self    ,     **snake_case_  ):
      return TaTokenizer.from_pretrained(self.checkpoint    ,     **snake_case_  )
    def        lowerCamelCase__						(     self    ,     **snake_case_  ):
      return EncodecFeatureExtractor.from_pretrained(self.checkpoint    ,     **snake_case_  )
    def        lowerCamelCase__						(     self  ):
      shutil.rmtree(self.tmpdirname  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Optional[int]            = self.get_tokenizer()
      _snake_case  :    Any            = self.get_feature_extractor()

      _snake_case  :    List[str]            = MusicgenProcessor(tokenizer=snake_case_    ,     feature_extractor=snake_case_  )

      processor.save_pretrained(self.tmpdirname  )
      _snake_case  :    Optional[Any]            = MusicgenProcessor.from_pretrained(self.tmpdirname  )

      self.assertEqual(processor.tokenizer.get_vocab()    ,     tokenizer.get_vocab()  )
      self.assertIsInstance(processor.tokenizer    ,     snake_case_  )

      self.assertEqual(processor.feature_extractor.to_json_string()    ,     feature_extractor.to_json_string()  )
      self.assertIsInstance(processor.feature_extractor    ,     snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Optional[int]            = MusicgenProcessor(tokenizer=self.get_tokenizer()    ,     feature_extractor=self.get_feature_extractor()  )
      processor.save_pretrained(self.tmpdirname  )

      _snake_case  :    Tuple            = self.get_tokenizer(bos_token="(BOS)"    ,     eos_token="(EOS)"  )
      _snake_case  :    List[str]            = self.get_feature_extractor(do_normalize=snake_case_    ,     padding_value=1.0  )

      _snake_case  :    int            = MusicgenProcessor.from_pretrained(
          self.tmpdirname    ,     bos_token="(BOS)"    ,     eos_token="(EOS)"    ,     do_normalize=snake_case_    ,     padding_value=1.0  )

      self.assertEqual(processor.tokenizer.get_vocab()    ,     tokenizer_add_kwargs.get_vocab()  )
      self.assertIsInstance(processor.tokenizer    ,     snake_case_  )

      self.assertEqual(processor.feature_extractor.to_json_string()    ,     feature_extractor_add_kwargs.to_json_string()  )
      self.assertIsInstance(processor.feature_extractor    ,     snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    str            = self.get_feature_extractor()
      _snake_case  :    List[Any]            = self.get_tokenizer()

      _snake_case  :    List[str]            = MusicgenProcessor(tokenizer=snake_case_    ,     feature_extractor=snake_case_  )

      _snake_case  :    List[str]            = floats_list((3, 10_00)  )

      _snake_case  :    str            = feature_extractor(snake_case_    ,     return_tensors="np"  )
      _snake_case  :    List[Any]            = processor(snake_case_    ,     return_tensors="np"  )

      for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()    ,     input_processor[key].sum()    ,     delta=1E-2  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Union[str, Any]            = self.get_feature_extractor()
      _snake_case  :    Optional[Any]            = self.get_tokenizer()

      _snake_case  :    Tuple            = MusicgenProcessor(tokenizer=snake_case_    ,     feature_extractor=snake_case_  )

      _snake_case  :    str            = "This is a test string"

      _snake_case  :    List[Any]            = processor(text=snake_case_  )

      _snake_case  :    Any            = tokenizer(snake_case_  )

      for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]    ,     encoded_processor[key]  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Dict            = self.get_feature_extractor()
      _snake_case  :    str            = self.get_tokenizer()

      _snake_case  :    Union[str, Any]            = MusicgenProcessor(tokenizer=snake_case_    ,     feature_extractor=snake_case_  )

      _snake_case  :    Dict            = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      _snake_case  :    int            = processor.batch_decode(sequences=snake_case_  )
      _snake_case  :    Tuple            = tokenizer.batch_decode(snake_case_  )

      self.assertListEqual(snake_case_    ,     snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Dict            = self.get_feature_extractor()
      _snake_case  :    Tuple            = self.get_tokenizer()

      _snake_case  :    Union[str, Any]            = MusicgenProcessor(tokenizer=snake_case_    ,     feature_extractor=snake_case_  )

      self.assertListEqual(
          processor.model_input_names    ,     feature_extractor.model_input_names    ,     msg="`processor` and `feature_extractor` model input names do not match"    ,     )


    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[str]            = self.get_feature_extractor(padding_side="left"  )
      _snake_case  :    Tuple            = self.get_tokenizer()

      _snake_case  :    Tuple            = MusicgenProcessor(tokenizer=snake_case_    ,     feature_extractor=snake_case_  )

      _snake_case  :    Tuple            = [floats_list((1, x)  )[0] for x in range(5    ,     20    ,     5  )]
      _snake_case  :    List[str]            = processor(snake_case_  ).padding_mask

      _snake_case  :    Dict            = np.asarray(floats_list((3, 20)  )  )[:, None, :]
      _snake_case  :    Tuple            = processor.batch_decode(snake_case_    ,     padding_mask=snake_case_  )

      self.assertIsInstance(snake_case_    ,     snake_case_  )

      for audio in decoded_audios:
        self.assertIsInstance(snake_case_    ,     np.ndarray  )

      self.assertTrue(decoded_audios[0].shape == (1, 10)  )
      self.assertTrue(decoded_audios[1].shape == (1, 15)  )
      self.assertTrue(decoded_audios[2].shape == (1, 20)  )







import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json



def    UpperCAmelCase__	(  lowerCamelCase ):
  lowercase     :Optional[Any]   						=    filter(lambda lowerCamelCase : p.requires_grad,      model.parameters() )
  lowercase     :Optional[int]   						=    sum([np.prod(p.size() ) for p in model_parameters] )
  return params


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




class  __lowerCAmelCase							(				pl.Callback):






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       List[Any]							,       _lowerCAmelCase:       Union[str, Any]					):
         lowercase     :Dict   						=    {F"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups					)}
         pl_module.logger.log_metrics(_lowerCAmelCase					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       Optional[Any]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       pl.LightningModule							,       _lowerCAmelCase:       str							,       _lowerCAmelCase:       List[str]=True					):
         logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"					)
         lowercase     :Tuple   						=    trainer.callback_metrics
         trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]}					)
         # Log results
         lowercase     :Optional[int]   						=    Path(pl_module.hparams.output_dir					)
         if type_path == "test":
           lowercase     :List[Any]   						=    od / "test_results.txt"
           lowercase     :Optional[int]   						=    od / "test_generations.txt"
         else:
           # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
           # If people want this it will be easy enough to add back.
           lowercase     :int   						=    od / F"{type_path}_results/{trainer.global_step:05d}.txt"
           lowercase     :Tuple   						=    od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
           results_file.parent.mkdir(exist_ok=_lowerCAmelCase					)
           generations_file.parent.mkdir(exist_ok=_lowerCAmelCase					)
         with open(_lowerCAmelCase							,       "a+"					) as writer:
           for key in sorted(_lowerCAmelCase					):
             if key in ["log", "progress_bar", "preds"]:
               continue
             lowercase     :int   						=    metrics[key]
             if isinstance(_lowerCAmelCase							,       torch.Tensor					):
               lowercase     :int   						=    val.item()
             lowercase     :Optional[Any]   						=    F"{key}: {val:.6f}\n"
             writer.write(_lowerCAmelCase					)

         if not save_generations:
           return

         if "preds" in metrics:
           lowercase     :str   						=    "\n".join(metrics["preds"]					)
           generations_file.open("w+"					).write(_lowerCAmelCase					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       Optional[int]							,       _lowerCAmelCase:       Tuple							,       _lowerCAmelCase:       Tuple					):
         try:
           lowercase     :Optional[int]   						=    pl_module.model.model.num_parameters()
         except AttributeError:
           lowercase     :Dict   						=    pl_module.model.num_parameters()

         lowercase     :Any   						=    count_trainable_parameters(_lowerCAmelCase					)
         # mp stands for million parameters
         trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6}					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       pl.LightningModule					):
         save_json(pl_module.metrics							,       pl_module.metrics_save_path					)
         return self._write_logs(_lowerCAmelCase							,       _lowerCAmelCase							,       "test"					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       List[str]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       Dict					):
         save_json(pl_module.metrics							,       pl_module.metrics_save_path					)
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")







def    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase,      lowerCamelCase=1,      lowerCamelCase=False ):
  if metric == "rouge2":
    lowercase     :str   						=    "{val_avg_rouge2:.4f}-{step_count}"
  elif metric == "bleu":
    lowercase     :int   						=    "{val_avg_bleu:.4f}-{step_count}"
  elif metric == "loss":
    lowercase     :List[str]   						=    "{val_avg_loss:.4f}-{step_count}"
  else:
    raise NotImplementedError(
        F"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"
        " this function." )

  lowercase     :int   						=    ModelCheckpoint(
      dirpath=lowerCamelCase,      filename=lowerCamelCase,      monitor=F"val_{metric}",      mode="min" if "loss" in metric else "max",      save_top_k=lowerCamelCase,      )
  return checkpoint_callback







def    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase ):
  return EarlyStopping(
      monitor=F"val_{metric}",      mode="min" if "loss" in metric else "max",      patience=lowerCamelCase,      verbose=lowerCamelCase,      )



