





'''simple docstring'''






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 		UpperCAmelCase   (				unittest.TestCase):


  """simple docstring"""







  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						int:
    _UpperCamelCase       			='''ylacombe/bark-small'''
    _UpperCamelCase       			=tempfile.mkdtemp()
    _UpperCamelCase       			='''en_speaker_1'''
    _UpperCamelCase       			='''This is a test string'''
    _UpperCamelCase       			='''speaker_embeddings_path.json'''
    _UpperCamelCase       			='''speaker_embeddings'''


  def   UpperCamelCase__      (				self    :					int							,	**UpperCamelCase__    :					str  )     ->						Optional[int]:
    return AutoTokenizer.from_pretrained(self.checkpoint							,	**UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						Dict:
    shutil.rmtree(self.tmpdirname  )


  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						Optional[int]:
    _UpperCamelCase       			=self.get_tokenizer()

    _UpperCamelCase       			=BarkProcessor(tokenizer=UpperCamelCase__  )

    processor.save_pretrained(self.tmpdirname  )
    _UpperCamelCase       			=BarkProcessor.from_pretrained(self.tmpdirname  )

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


  @slow
  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						Optional[Any]:
    _UpperCamelCase       			=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							,	)

    _UpperCamelCase       			=self.get_tokenizer(bos_token='''(BOS)'''							,	eos_token='''(EOS)'''  )

    _UpperCamelCase       			=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   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						str:
    _UpperCamelCase       			=BarkProcessor.from_pretrained(
        pretrained_processor_name_or_path=self.checkpoint							,	speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,	)

    _UpperCamelCase       			=35
    _UpperCamelCase       			=2
    _UpperCamelCase       			=8

    _UpperCamelCase       			={
        '''semantic_prompt''': np.ones(UpperCamelCase__  ),
        '''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len)  ),
        '''fine_prompt''': np.ones((nb_codebooks_total, seq_len)  ),
    }

    # test providing already loaded voice_preset
    _UpperCamelCase       			=processor(text=self.input_string							,	voice_preset=UpperCamelCase__  )

    _UpperCamelCase       			=inputs['''history_prompt''']
    for key in voice_preset:
      self.assertListEqual(voice_preset[key].tolist()							,	processed_voice_preset.get(UpperCamelCase__							,	np.array([]  )  ).tolist()  )

    # test loading voice preset from npz file
    _UpperCamelCase       			=os.path.join(self.tmpdirname							,	'''file.npz'''  )
    np.savez(UpperCamelCase__							,	**UpperCamelCase__  )
    _UpperCamelCase       			=processor(text=self.input_string							,	voice_preset=UpperCamelCase__  )
    _UpperCamelCase       			=inputs['''history_prompt''']

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

    # test loading voice preset from the hub
    _UpperCamelCase       			=processor(text=self.input_string							,	voice_preset=self.voice_preset  )




  def   UpperCamelCase__      (				self    :					Dict  )     ->						str:
    _UpperCamelCase       			=self.get_tokenizer()

    _UpperCamelCase       			=BarkProcessor(tokenizer=UpperCamelCase__  )

    _UpperCamelCase       			=processor(text=self.input_string  )

    _UpperCamelCase       			=tokenizer(
        self.input_string							,	padding='''max_length'''							,	max_length=256							,	add_special_tokens=UpperCamelCase__							,	return_attention_mask=UpperCamelCase__							,	return_token_type_ids=UpperCamelCase__							,	)

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













'''simple docstring'''






import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  return EnvironmentCommand()


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  return EnvironmentCommand(args.accelerate_config_file   )







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  @staticmethod
  def   UpperCamelCase__      (				UpperCamelCase__    :					ArgumentParser  )     ->						Union[str, Any]:
    _UpperCamelCase       			=parser.add_parser('''env'''  )
    download_parser.set_defaults(func=UpperCamelCase__  )
    download_parser.add_argument(
        '''--accelerate-config_file'''							,	default=UpperCamelCase__							,	help='''The accelerate config file to use for the default values in the launching script.'''							,	)
    download_parser.set_defaults(func=UpperCamelCase__  )


  def __init__(				self    :					Tuple							,	UpperCamelCase__    :					Dict							,	*UpperCamelCase__    :					str  )     ->						None:
    _UpperCamelCase       			=accelerate_config_file


  def   UpperCamelCase__      (				self    :					str  )     ->						int:
    _UpperCamelCase       			='''not installed'''
    if is_safetensors_available():
      import safetensors

      _UpperCamelCase       			=safetensors.__version__
    elif importlib.util.find_spec('''safetensors'''  ) is not None:
      import safetensors

      _UpperCamelCase       			=F'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			=_UpperCamelCase       			='''not found'''
    if is_accelerate_available():
      import accelerate
      from accelerate.commands.config import default_config_file, load_config_from_file

      _UpperCamelCase       			=accelerate.__version__
      # Get the default from the config file.
      if self._accelerate_config_file is not None or os.path.isfile(UpperCamelCase__  ):
        _UpperCamelCase       			=load_config_from_file(self._accelerate_config_file  ).to_dict()

      _UpperCamelCase       			=(
          '''\n'''.join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]  )
          if isinstance(UpperCamelCase__							,	UpperCamelCase__  )
          else F'''\t{accelerate_config}'''
      )

    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''NA'''
    if is_torch_available():
      import torch

      _UpperCamelCase       			=torch.__version__
      _UpperCamelCase       			=torch.cuda.is_available()

    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''NA'''
    if is_tf_available():
      import tensorflow as tf

      _UpperCamelCase       			=tf.__version__
      try:
        # deprecated in v2.1
        _UpperCamelCase       			=tf.test.is_gpu_available()
      except AttributeError:
        # returns list of devices, convert to bool
        _UpperCamelCase       			=bool(tf.config.list_physical_devices('''GPU'''  )  )

    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''NA'''
    if is_flax_available():
      import flax
      import jax
      import jaxlib

      _UpperCamelCase       			=flax.__version__
      _UpperCamelCase       			=jax.__version__
      _UpperCamelCase       			=jaxlib.__version__
      _UpperCamelCase       			=jax.lib.xla_bridge.get_backend().platform

    _UpperCamelCase       			={
        '''`transformers` version''': version,
        '''Platform''': platform.platform(),
        '''Python version''': platform.python_version(),
        '''Huggingface_hub version''': huggingface_hub.__version__,
        '''Safetensors version''': F'''{safetensors_version}''',
        '''Accelerate version''': F'''{accelerate_version}''',
        '''Accelerate config''': F'''{accelerate_config_str}''',
        '''PyTorch version (GPU?)''': F'''{pt_version} ({pt_cuda_available})''',
        '''Tensorflow version (GPU?)''': F'''{tf_version} ({tf_cuda_available})''',
        '''Flax version (CPU?/GPU?/TPU?)''': F'''{flax_version} ({jax_backend})''',
        '''Jax version''': F'''{jax_version}''',
        '''JaxLib version''': F'''{jaxlib_version}''',
        '''Using GPU in script?''': '''<fill in>''',
        '''Using distributed or parallel set-up in script?''': '''<fill in>''',
    }

    print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'''  )
    print(self.format_dict(UpperCamelCase__  )  )

    return info




  @staticmethod
  def   UpperCamelCase__      (				UpperCamelCase__    :					Optional[Any]  )     ->						Dict:
    return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]  ) + "\n"






