

'''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 	lowerCAmelCase  (   unittest.TestCase ):







       def       snake_case (				self      :			int   ):







            """simple docstring"""







            __lowercase      		=inspect.getfile(accelerate.test_utils   )
            __lowercase      		=os.path.sep.join(mod_file.split(os.path.sep   )[:-1] + ['scripts', 'test_script.py']   )
            __lowercase      		=os.path.sep.join(
                mod_file.split(os.path.sep   )[:-1] + ['scripts', 'test_distributed_data_loop.py']   )
            __lowercase      		=os.path.sep.join(mod_file.split(os.path.sep   )[:-1] + ['scripts', 'test_ops.py']   )







       @require_multi_gpu
       def       snake_case (				self      :			int   ):







            """simple docstring"""







            print(f'''Found {torch.cuda.device_count()} devices.'''   )
            __lowercase      		=["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      :			str   ):







            """simple docstring"""







            print(f'''Found {torch.cuda.device_count()} devices.'''   )
            __lowercase      		=["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      :			Optional[Any]   ):







            """simple docstring"""







            __lowercase      		=["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      :			str   ):







            """simple docstring"""







            print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only'''   )
            __lowercase      		=["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__":
       UpperCAmelCase					 =	Accelerator()
       UpperCAmelCase					 =	(accelerator.state.process_index + 2, 10)
       UpperCAmelCase					 =	torch.randint(0, 10, shape).to(accelerator.device)

       UpperCAmelCase					 =	""""""

       UpperCAmelCase					 =	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					 =	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					 =	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)









import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format



class    A__  :
      _UpperCAmelCase      :Union[str, Any]		       =  None





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   int			      =  self.feature_extraction_class(**self.feat_extract_dict  )
       UpperCamelCase      :   Union[str, Any]			      =  json.loads(feat_extract.to_json_string()  )
       for key, value in self.feat_extract_dict.items():
        self.assertEqual(obj[key]			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   str			      =  self.feature_extraction_class(**self.feat_extract_dict  )

       with tempfile.TemporaryDirectory() as tmpdirname:
        UpperCamelCase      :   List[Any]			      =  os.path.join(A_			,				"feat_extract.json"  )
        feat_extract_first.to_json_file(A_  )
        UpperCamelCase      :   str			      =  self.feature_extraction_class.from_json_file(A_  )

       self.assertEqual(feat_extract_second.to_dict()			,				feat_extract_first.to_dict()  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Union[str, Any]			      =  self.feature_extraction_class(**self.feat_extract_dict  )

       with tempfile.TemporaryDirectory() as tmpdirname:
        UpperCamelCase      :   int			      =  feat_extract_first.save_pretrained(A_  )[0]
        check_json_file_has_correct_format(A_  )
        UpperCamelCase      :   Optional[Any]			      =  self.feature_extraction_class.from_pretrained(A_  )

       self.assertEqual(feat_extract_second.to_dict()			,				feat_extract_first.to_dict()  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Any			      =  self.feature_extraction_class()
       self.assertIsNotNone(A_  )
