





"""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   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


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

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

  lowerCamelCase__						= """"""

  lowerCamelCase__						= 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__						= 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__						= 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)


lowerCamelCase__     :		List[str]        = """
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

lowerCamelCase__     :		List[Any]        = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
lowerCamelCase__     :		int        = {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}






