




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  ):






    """simple docstring"""





    def 				a__ ( self    )					->						Dict:
         UpperCAmelCase_     :				Optional[int]  =     inspect.getfile(accelerate.test_utils    )
         UpperCAmelCase_     :				List[Any]  =     os.path.sep.join(mod_file.split(os.path.sep    )[:-1] + ['''scripts''', '''test_script.py''']    )
         UpperCAmelCase_     :				int  =     os.path.sep.join(
             mod_file.split(os.path.sep    )[:-1] + ['''scripts''', '''test_distributed_data_loop.py''']    )
         UpperCAmelCase_     :				Dict  =     os.path.sep.join(mod_file.split(os.path.sep    )[:-1] + ['''scripts''', '''test_ops.py''']    )





    @require_multi_gpu
    def 				a__ ( self    )					->						Union[str, Any]:
         print(f'''Found {torch.cuda.device_count()} devices.'''    )
         UpperCAmelCase_     :				List[Any]  =     ['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
         with patch_environment(omp_num_threads=1    ):
              execute_subprocess_async(_lowercase  ,env=os.environ.copy()    )





    @require_multi_gpu
    def 				a__ ( self    )					->						str:
         print(f'''Found {torch.cuda.device_count()} devices.'''    )
         UpperCAmelCase_     :				Optional[int]  =     ['''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(_lowercase  ,env=os.environ.copy()    )





    @require_multi_gpu
    def 				a__ ( self    )					->						int:
         UpperCAmelCase_     :				List[str]  =     ['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__    )]
         with patch_environment(omp_num_threads=1    ):
              execute_subprocess_async(_lowercase  ,env=os.environ.copy()    )





    @require_multi_gpu
    def 				a__ ( self    )					->						Any:
         print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only'''    )
         UpperCAmelCase_     :				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(_lowercase  ,env=os.environ.copy()    )


if __name__ == "__main__":
     __a							     =					Accelerator()
     __a							     =					(accelerator.state.process_index + 2, 10)
     __a							     =					torch.randint(0, 10, shape).to(accelerator.device)

     __a							     =					""

     __a							     =					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)."

     __a							     =					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."
     __a							     =					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'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     ["""image_processor""", """tokenizer"""]
				_a    	=     """CLIPImageProcessor"""
				_a    	=     ("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")



				def __init__(    self,     _lowercase=None,     _lowercase=None,     **_lowercase       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																		    ' instead.',     _lowercase,     )
																		SCREAMING_SNAKE_CASE_				=				kwargs.pop('feature_extractor'       )

											SCREAMING_SNAKE_CASE_				=				image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																		raise ValueError('You need to specify an `image_processor`.'       )
											if tokenizer is None:
																		raise ValueError('You need to specify a `tokenizer`.'       )

											super().__init__(_lowercase,     _lowercase       )



				def __call__(    self,     _lowercase=None,     _lowercase=None,     _lowercase=None,     **_lowercase       )     ->							str:

											if text is None and images is None:
																		raise ValueError('You have to specify either text or images. Both cannot be none.'       )

											if text is not None:
																		SCREAMING_SNAKE_CASE_				=				self.tokenizer(_lowercase,     return_tensors=_lowercase,     **_lowercase       )

											if images is not None:
																		SCREAMING_SNAKE_CASE_				=				self.image_processor(_lowercase,     return_tensors=_lowercase,     **_lowercase       )

											if text is not None and images is not None:
																		SCREAMING_SNAKE_CASE_				=				image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**_lowercase       ),     tensor_type=_lowercase       )



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							Optional[int]:
											return self.tokenizer.batch_decode(*_lowercase,     **_lowercase       )



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							Optional[Any]:
											return self.tokenizer.decode(*_lowercase,     **_lowercase       )





				@property
				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				self.tokenizer.model_input_names
											SCREAMING_SNAKE_CASE_				=				self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )





