


import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow





@require_torch
class   __a      (   unittest.TestCase				):
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       Any        =       'ylacombe/bark-small'
        lowercase__:       int        =       tempfile.mkdtemp()
        lowercase__:       List[Any]        =       'en_speaker_1'
        lowercase__:       int        =       'This is a test string'
        lowercase__:       Any        =       'speaker_embeddings_path.json'
        lowercase__:       List[str]        =       'speaker_embeddings'
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			**lowerCAmelCase__			) ->    int:




        '''simple docstring'''
        return AutoTokenizer.from_pretrained(self.checkpoint    ,			**__lowerCAmelCase			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        shutil.rmtree(self.tmpdirname			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       Tuple        =       self.get_tokenizer()

        lowercase__:       Union[str, Any]        =       BarkProcessor(tokenizer=__lowerCAmelCase			)

        processor.save_pretrained(self.tmpdirname			)
        lowercase__:       List[str]        =       BarkProcessor.from_pretrained(self.tmpdirname			)

        self.assertEqual(processor.tokenizer.get_vocab()    ,			tokenizer.get_vocab()			)
 @slow
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        lowercase__:       int        =       BarkProcessor.from_pretrained(
            pretrained_processor_name_or_path=self.checkpoint    ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,			)
        processor.save_pretrained(
            self.tmpdirname    ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,			speaker_embeddings_directory=self.speaker_embeddings_directory    ,			)

        lowercase__:       Optional[int]        =       self.get_tokenizer(bos_token='(BOS)'    ,			eos_token='(EOS)'			)

        lowercase__:       int        =       BarkProcessor.from_pretrained(
            self.tmpdirname    ,			self.speaker_embeddings_dict_path    ,			bos_token='(BOS)'    ,			eos_token='(EOS)'    ,			)

        self.assertEqual(processor.tokenizer.get_vocab()    ,			tokenizer_add_kwargs.get_vocab()			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       str        =       BarkProcessor.from_pretrained(
            pretrained_processor_name_or_path=self.checkpoint    ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,			)

        lowercase__:       Tuple        =       35
        lowercase__:       List[str]        =       2
        lowercase__:       Any        =       8

        lowercase__:       Optional[int]        =       {
            'semantic_prompt': np.ones(__lowerCAmelCase			),
            'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len)			),
            'fine_prompt': np.ones((nb_codebooks_total, seq_len)			),
        }

        # test providing already loaded voice_preset
        lowercase__:       Any        =       processor(text=self.input_string    ,			voice_preset=__lowerCAmelCase			)

        lowercase__:       List[str]        =       inputs['history_prompt']
        for key in voice_preset:
               self.assertListEqual(voice_preset[key].tolist()    ,			processed_voice_preset.get(__lowerCAmelCase    ,			np.array([]			)			).tolist()			)

        # test loading voice preset from npz file
        lowercase__:       str        =       os.path.join(self.tmpdirname    ,			'file.npz'			)
        np.savez(__lowerCAmelCase    ,			**__lowerCAmelCase			)
        lowercase__:       Tuple        =       processor(text=self.input_string    ,			voice_preset=__lowerCAmelCase			)
        lowercase__:       List[Any]        =       inputs['history_prompt']

        for key in voice_preset:
               self.assertListEqual(voice_preset[key].tolist()    ,			processed_voice_preset.get(__lowerCAmelCase    ,			np.array([]			)			).tolist()			)

        # test loading voice preset from the hub
        lowercase__:       Tuple        =       processor(text=self.input_string    ,			voice_preset=self.voice_preset			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       self.get_tokenizer()

        lowercase__:       int        =       BarkProcessor(tokenizer=__lowerCAmelCase			)

        lowercase__:       Union[str, Any]        =       processor(text=self.input_string			)

        lowercase__:       Any        =       tokenizer(
            self.input_string    ,			padding='max_length'    ,			max_length=256    ,			add_special_tokens=__lowerCAmelCase    ,			return_attention_mask=__lowerCAmelCase    ,			return_token_type_ids=__lowerCAmelCase    ,			)

        for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]    ,			encoded_processor[key].squeeze().tolist()			)







from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


__lowerCAmelCase			=							logging.get_logger(__name__)


if is_torch_available():
 import torch
 from torch import nn
 from torch.utils.data import Dataset

 from transformers import Trainer





 class   __a      (   __UpperCamelCase				):
  def __init__(       self    ,			lowerCAmelCase__ = 101			) ->    Any:




         '''simple docstring'''
         lowercase__:       Any        =       length
  def __len__(       self			) ->    List[Any]:




         '''simple docstring'''
         return self.length
  def __getitem__(       self    ,			lowerCAmelCase__			) ->    int:




         '''simple docstring'''
         return i





 class   __a      :
  def __call__(       self    ,			lowerCAmelCase__			) ->    List[str]:




         '''simple docstring'''
         return {"input_ids": torch.tensor(lowerCAmelCase__			), "labels": torch.tensor(lowerCAmelCase__			)}





 class   __a      (   nn.Module				):
  def __init__(       self			) ->    Tuple:




         '''simple docstring'''
         super().__init__()
         # Add some (unused) params otherwise DDP will complain.
         lowercase__:       List[str]        =       nn.Linear(120    ,			80			)
  def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=None			) ->    int:




         '''simple docstring'''
         if labels is not None:
                return torch.tensor(0.0    ,			device=input_ids.device			), input_ids
         else:
                return input_ids





class   __a      (   __UpperCamelCase				):
 @require_torch_neuroncore
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       int        =       F'--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
        lowercase__:       Tuple        =       self.get_auto_remove_tmp_dir()
        lowercase__:       Optional[int]        =       F'--output_dir {output_dir}'.split()
        lowercase__:       int        =       ['torchrun'] + distributed_args + args
        execute_subprocess_async(lowerCAmelCase__    ,			env=self.get_env()			)
        # successful return here == success - any errors would have caused an error in the sub-call





class   __a      (   __UpperCamelCase				):
 @require_torch_multi_gpu
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        lowercase__:       List[str]        =       F'--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
        lowercase__:       Tuple        =       self.get_auto_remove_tmp_dir()
        lowercase__:       List[str]        =       F'--output_dir {output_dir}'.split()
        lowercase__:       int        =       ['torchrun'] + distributed_args + args
        execute_subprocess_async(lowerCAmelCase__    ,			env=self.get_env()			)
        # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
 # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
 #
 # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

 __lowerCAmelCase			=							HfArgumentParser((TrainingArguments,))
 __lowerCAmelCase			=							parser.parse_args_into_dataclasses()[0]

 logger.warning(
     F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
     F'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
 )

 # Essentially, what we want to verify in the distributed case is that we get all samples back,
 # in the right order. (this is crucial for prediction for instance)
 for dataset_length in [1_01, 40, 7]:
  __lowerCAmelCase			=							DummyDataset(dataset_length)




  def        snake_case_					(  snake_case					)  ->    Dict:
         lowercase__:       str        =       list(range(len(snake_case					)					)					)
         lowercase__:       Tuple        =       p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
         if not success and training_args.local_rank == 0:
                logger.warning(
                    'Predictions and/or labels do not match expected results:\n  - predictions: '
                    f'{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}'					)
         return {"success": success}

  __lowerCAmelCase			=							Trainer(
      model=DummyModel(),
      args=training_args,
      data_collator=DummyDataCollator(),
      eval_dataset=dataset,
      compute_metrics=compute_metrics,
  )
  __lowerCAmelCase			=							trainer.evaluate()
  logger.info(metrics)
  if metrics["eval_success"] is not True:
   logger.error(metrics)
   exit(1)

  __lowerCAmelCase			=							trainer.predict(dataset)
  logger.info(p.metrics)
  if p.metrics["test_success"] is not True:
   logger.error(p.metrics)
   exit(1)

  __lowerCAmelCase			=							2

  __lowerCAmelCase			=							trainer.evaluate()
  logger.info(metrics)
  if metrics["eval_success"] is not True:
   logger.error(metrics)
   exit(1)

  __lowerCAmelCase			=							trainer.predict(dataset)
  logger.info(p.metrics)
  if p.metrics["test_success"] is not True:
   logger.error(p.metrics)
   exit(1)




  __lowerCAmelCase			=							None


