


"""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 						snake_case_(				unittest.TestCase	):






     def    lowerCamelCase__   (		self     :    int ):
           lowerCAmelCase    :		Tuple				=      inspect.getfile(accelerate.test_utils )
           lowerCAmelCase    :		Optional[Any]				=      os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_script.py'''] )
           lowerCAmelCase    :		Optional[int]				=      os.path.sep.join(
               mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_distributed_data_loop.py'''] )
           lowerCAmelCase    :		Union[str, Any]				=      os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_ops.py'''] )






     @require_multi_gpu
     def    lowerCamelCase__   (		self     :    int ):
           print(F'''Found {torch.cuda.device_count()} devices.''' )
           lowerCAmelCase    :		str				=      ['''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    lowerCamelCase__   (		self     :    List[Any] ):
           print(F'''Found {torch.cuda.device_count()} devices.''' )
           lowerCAmelCase    :		List[Any]				=      ['''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    lowerCamelCase__   (		self     :    Dict ):
           lowerCAmelCase    :		Optional[int]				=      ['''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    lowerCamelCase__   (		self     :    Dict ):
           print(F'''Found {torch.cuda.device_count()} devices, using 2 devices only''' )
           lowerCAmelCase    :		Tuple				=      ['''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__":
 snake_case__						:   List[str]			 = Accelerator()
 snake_case__						:   Dict			 = (accelerator.state.process_index + 2, 10)
 snake_case__						:   Dict			 = torch.randint(0, 10, shape).to(accelerator.device)

 snake_case__						:   Any			 = ''''''

 snake_case__						:   Tuple			 = 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)."

 snake_case__						:   Dict			 = 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."
 snake_case__						:   int			 = 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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_UpperCamelCase											=						{'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						['SpeechEncoderDecoderModel']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						['FlaxSpeechEncoderDecoderModel']

if TYPE_CHECKING:
   from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
   import sys

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






