


'''simple docstring'''




from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: Optional[int]							  =		"""ClapFeatureExtractor"""
 __lowerCAmelCase						: str							  =		("""RobertaTokenizer""", """RobertaTokenizerFast""")




 def __init__(    self		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    Union[str, Any]:
    super().__init__(lowerCamelCase_		,      lowerCamelCase_      )




 def __call__(    self		,      lowerCamelCase_=None		,      lowerCamelCase_=None		,      lowerCamelCase_=None		,      **lowerCamelCase_      )      ->    List[Any]:
    _a					:  Any						=  kwargs.pop('sampling_rate'		,      lowerCamelCase_      )

    if text is None and audios is None:
       raise ValueError('You have to specify either text or audios. Both cannot be none.'      )

    if text is not None:
       _a					:  Union[str, Any]						=  self.tokenizer(lowerCamelCase_		,      return_tensors=lowerCamelCase_		,      **lowerCamelCase_      )

    if audios is not None:
       _a					:  List[str]						=  self.feature_extractor(
           lowerCamelCase_		,      sampling_rate=lowerCamelCase_		,      return_tensors=lowerCamelCase_		,      **lowerCamelCase_      )

    if text is not None and audios is not None:
       _a					:  List[Any]						=  audio_features.input_features
       return encoding
    elif text is not None:
       return encoding
    else:
       return BatchEncoding(data=dict(**lowerCamelCase_      )		,      tensor_type=lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    Tuple:
    return self.tokenizer.batch_decode(*lowerCamelCase_		,      **lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    List[str]:
    return self.tokenizer.decode(*lowerCamelCase_		,      **lowerCamelCase_      )




 @property
 def   __UpperCamelCase   (    self      )      ->    Tuple:
    _a					:  Dict						=  self.tokenizer.model_input_names
    _a					:  Tuple						=  self.feature_extractor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names      )      )






'''simple docstring'''




import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler







@dataclass
class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: int							  =		0
 __lowerCAmelCase						: bool							  =		False
 __lowerCAmelCase						: float							  =		3.0







class 					a			(				unittest.TestCase  ):
 '''simple docstring'''




 def   __UpperCamelCase   (    self      )      ->    Dict:
    # If no defaults are changed, `to_kwargs` returns an empty dict.
    self.assertDictEqual(MockClass().to_kwargs()		,      {}      )
    self.assertDictEqual(MockClass(a=2      ).to_kwargs()		,      {'a': 2}      )
    self.assertDictEqual(MockClass(a=2		,      b=lowerCamelCase_      ).to_kwargs()		,      {'a': 2, 'b': True}      )
    self.assertDictEqual(MockClass(a=2		,      c=2.25      ).to_kwargs()		,      {'a': 2, 'c': 2.25}      )




 @require_cuda
 def   __UpperCamelCase   (    self      )      ->    int:
    # If no defaults are changed, `to_kwargs` returns an empty dict.
    _a					:  List[str]						=  GradScalerKwargs(init_scale=1_0_2_4		,      growth_factor=2      )
    AcceleratorState._reset_state()
    _a					:  Optional[int]						=  Accelerator(mixed_precision='fp16'		,      kwargs_handlers=[scaler_handler]      )
    print(accelerator.use_fpaa      )
    _a					:  Tuple						=  accelerator.scaler

    # Check the kwargs have been applied
    self.assertEqual(scaler._init_scale		,      1024.0      )
    self.assertEqual(scaler._growth_factor		,      2.0      )

    # Check the other values are at the default
    self.assertEqual(scaler._backoff_factor		,      0.5      )
    self.assertEqual(scaler._growth_interval		,      2_0_0_0      )
    self.assertEqual(scaler._enabled		,      lowerCamelCase_      )




 @require_multi_gpu
 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    _a					:  List[str]						=  ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__      )]
    execute_subprocess_async(lowerCamelCase_		,      env=os.environ.copy()      )


if __name__ == "__main__":
 UpperCAmelCase_      :		Tuple          =		DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
 UpperCAmelCase_      :		str          =		Accelerator(kwargs_handlers=[ddp_scaler])
 UpperCAmelCase_      :		List[Any]          =		torch.nn.Linear(100, 200)
 UpperCAmelCase_      :		Tuple          =		accelerator.prepare(model)

 # Check the values changed in kwargs
 UpperCAmelCase_      :		List[str]          =		""
 UpperCAmelCase_      :		Any          =		model.bucket_bytes_cap // (1024 * 1024)
 if observed_bucket_cap_map != 15:
  error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
 if model.find_unused_parameters is not True:
  error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

 # Check the values of the defaults
 if model.dim != 0:
  error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
 if model.broadcast_buffers is not True:
  error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
 if model.gradient_as_bucket_view is not False:
  error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

 # Raise error at the end to make sure we don't stop at the first failure.
 if len(error_msg) > 0:
  raise ValueError(error_msg)


