


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     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""


     def 	snake_case_  (	self    ):
        __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 	snake_case_  (	self    ):
        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(__A  ,       env=os.environ.copy()    )


     @require_multi_gpu
     def 	snake_case_  (	self    ):
        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(__A  ,       env=os.environ.copy()    )


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


     @require_multi_gpu
     def 	snake_case_  (	self    ):
        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(__A  ,       env=os.environ.copy()    )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE							    =							Accelerator()
  SCREAMING_SNAKE_CASE							    =							(accelerator.state.process_index + 2, 1_0)
  SCREAMING_SNAKE_CASE							    =							torch.randint(0, 1_0, shape).to(accelerator.device)

  SCREAMING_SNAKE_CASE							    =							''

  SCREAMING_SNAKE_CASE							    =							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)."

  SCREAMING_SNAKE_CASE							    =							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."
  SCREAMING_SNAKE_CASE							    =							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)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE							    =							{'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'WavLMForAudioFrameClassification',
      'WavLMForCTC',
      'WavLMForSequenceClassification',
      'WavLMForXVector',
      'WavLMModel',
      'WavLMPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE							    =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



