
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   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   inspect.getfile(accelerate.test_utils)
   lowercase__	: Tuple     		=   os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ["""scripts""", """test_script.py"""])
   lowercase__	: Optional[Any]     		=   os.path.sep.join(
       mod_file.split(os.path.sep)[:-1] + ["""scripts""", """test_distributed_data_loop.py"""])
   lowercase__	: int     		=   os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ["""scripts""", """test_ops.py"""])


 @require_multi_gpu
 def   lowercase__  (       self):


   '''simple docstring'''




   print(f'Found {torch.cuda.device_count()} devices.')
   lowercase__	: Any     		=   ["""torchrun""", f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
   with patch_environment(omp_num_threads=1):
     execute_subprocess_async(SCREAMING_SNAKE_CASE_       ,	env=os.environ.copy())


 @require_multi_gpu
 def   lowercase__  (       self):


   '''simple docstring'''




   print(f'Found {torch.cuda.device_count()} devices.')
   lowercase__	: Tuple     		=   ["""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(SCREAMING_SNAKE_CASE_       ,	env=os.environ.copy())


 @require_multi_gpu
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Any     		=   ["""torchrun""", f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__)]
   with patch_environment(omp_num_threads=1):
     execute_subprocess_async(SCREAMING_SNAKE_CASE_       ,	env=os.environ.copy())



 @require_multi_gpu
 def   lowercase__  (       self):


   '''simple docstring'''




   print(f'Found {torch.cuda.device_count()} devices, using 2 devices only')
   lowercase__	: Optional[Any]     		=   ["""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(SCREAMING_SNAKE_CASE_       ,	env=os.environ.copy())


if __name__ == "__main__":
     lowerCamelCase__     :		Any        = Accelerator()
     lowerCamelCase__     :		Union[str, Any]        = (accelerator.state.process_index + 2, 1_0)
     lowerCamelCase__     :		Tuple        = torch.randint(0, 1_0, shape).to(accelerator.device)

     lowerCamelCase__     :		List[Any]        = """"""

     lowerCamelCase__     :		str        = 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)."

     lowerCamelCase__     :		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."
     lowerCamelCase__     :		List[str]        = 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_tokenizers_available, is_torch_available


__magic_name__  :  List[str]					=       {
    """configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
    """tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  Union[str, Any]					=       [
          """M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """M2M100ForConditionalGeneration""",
          """M2M100Model""",
          """M2M100PreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
      from .tokenization_mam_aaa import MaMaaaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mam_aaa import (
                M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
                MaMaaaForConditionalGeneration,
                MaMaaaModel,
                MaMaaaPreTrainedModel,
            )


else:
      import sys

      __magic_name__  :  List[str]					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

