


"""simple docstring"""


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment



class 			lowerCamelCase__						(   unittest.TestCase			):







      def __a						(							self   :    Dict		):
        A					       = inspect.getfile(accelerate.test_utils		)
        A					       = os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ['scripts', 'test_script.py']		)
        A					       = os.path.sep.join(
            mod_file.split(os.path.sep		)[:-1] + ['scripts', 'test_distributed_data_loop.py']		)
        A					       = os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ['scripts', 'test_ops.py']		)







      @require_multi_gpu
      def __a						(							self   :    List[str]		):
        print(f'Found {torch.cuda.device_count()} devices.'		)
        A					       = ['torchrun', f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
        with patch_environment(omp_num_threads=1		):
          execute_subprocess_async(UpperCamelCase__			,			env=os.environ.copy()		)







      @require_multi_gpu
      def __a						(							self   :    int		):
        print(f'Found {torch.cuda.device_count()} devices.'		)
        A					       = ['torchrun', f'--nproc_per_node={torch.cuda.device_count()}', self.operation_file_path]
        print(f'Command: {cmd}'		)
        with patch_environment(omp_num_threads=1		):
          execute_subprocess_async(UpperCamelCase__			,			env=os.environ.copy()		)







      @require_multi_gpu
      def __a						(							self   :    List[Any]		):
        A					       = ['torchrun', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__		)]
        with patch_environment(omp_num_threads=1		):
          execute_subprocess_async(UpperCamelCase__			,			env=os.environ.copy()		)







      @require_multi_gpu
      def __a						(							self   :    Dict		):
        print(f'Found {torch.cuda.device_count()} devices, using 2 devices only'		)
        A					       = ['torchrun', f'--nproc_per_node={torch.cuda.device_count()}', self.data_loop_file_path]
        with patch_environment(omp_num_threads=1			,			cuda_visible_devices='0,1'		):
          execute_subprocess_async(UpperCamelCase__			,			env=os.environ.copy()		)


if __name__ == "__main__":
 UpperCamelCase						:				Optional[Any]             =      Accelerator()
 UpperCamelCase						:				Tuple             =      (accelerator.state.process_index + 2, 10)
 UpperCamelCase						:				Dict             =      torch.randint(0, 10, shape).to(accelerator.device)

 UpperCamelCase						:				Union[str, Any]             =      ""

 UpperCamelCase						:				List[Any]             =      accelerator.pad_across_processes(tensor)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
  error_msg += "Padding was not done with the right value (0)."

 UpperCamelCase						:				Union[str, Any]             =      accelerator.pad_across_processes(tensor, pad_first=True)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 UpperCamelCase						:				Tuple             =      accelerator.state.num_processes - accelerator.state.process_index - 1
 if not torch.equal(tensora[index:], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[:index] == 0):
  error_msg += "Padding was not done with the right value (0)."

 # 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)









"""simple docstring"""


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



class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     """ClapFeatureExtractor"""
						lowerCAmelCase       =     ("""RobertaTokenizer""", """RobertaTokenizerFast""")







						def __init__(							self   :    Dict			,			_lowercase   :    List[str]			,			_lowercase   :    Optional[int]		):
								super().__init__(_lowercase			,			_lowercase		)







						def __call__(							self   :    Optional[int]			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Dict=None			,			_lowercase   :    str=None			,			**_lowercase   :    Union[str, Any]		):
								A					       = kwargs.pop('sampling_rate'			,			_lowercase		)

								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					       = self.tokenizer(_lowercase			,			return_tensors=_lowercase			,			**_lowercase		)

								if audios is not None:
										A					       = self.feature_extractor(
										    _lowercase			,			sampling_rate=_lowercase			,			return_tensors=_lowercase			,			**_lowercase		)

								if text is not None and audios is not None:
										A					       = audio_features.input_features
										return encoding
								elif text is not None:
										return encoding
								else:
										return BatchEncoding(data=dict(**_lowercase		)			,			tensor_type=_lowercase		)







						def __a						(							self   :    List[Any]			,			*_lowercase   :    Dict			,			**_lowercase   :    Optional[int]		):
								return self.tokenizer.batch_decode(*_lowercase			,			**_lowercase		)







						def __a						(							self   :    List[str]			,			*_lowercase   :    str			,			**_lowercase   :    Optional[int]		):
								return self.tokenizer.decode(*_lowercase			,			**_lowercase		)







						@property
						def __a						(							self   :    Tuple		):
								A					       = self.tokenizer.model_input_names
								A					       = self.feature_extractor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names		)		)





