




'''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   _a							(		unittest.TestCase				):






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






     @require_multi_gpu
     def 							_lowercase      (					self       )				->     str:
          print(f"""Found {torch.cuda.device_count()} devices."""       )
          _snake_case				= ["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       )				->     Optional[Any]:
          print(f"""Found {torch.cuda.device_count()} devices."""       )
          _snake_case				= ["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       )				->     Tuple:
          _snake_case				= ["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       )				->     Optional[int]:
          print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only"""       )
          _snake_case				= ["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__":
   UpperCamelCase_		:     Union[str, Any]          =		Accelerator()
   UpperCamelCase_		:     Union[str, Any]          =		(accelerator.state.process_index + 2, 10)
   UpperCamelCase_		:     int          =		torch.randint(0, 10, shape).to(accelerator.device)

   UpperCamelCase_		:     Dict          =		''''''

   UpperCamelCase_		:     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)."

   UpperCamelCase_		:     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_		:     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_torch_available


UpperCamelCase_		:     List[Any]          =		{
    '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
    '''processing_git''': ['''GitProcessor'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     Optional[Any]          =		[
       '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''GitForCausalLM''',
       '''GitModel''',
       '''GitPreTrainedModel''',
       '''GitVisionModel''',
   ]

if TYPE_CHECKING:
   from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
   from .processing_git import GitProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_git import (
          GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          GitForCausalLM,
          GitModel,
          GitPreTrainedModel,
          GitVisionModel,
      )

else:
   import sys

   UpperCamelCase_		:     List[Any]          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


