





import warnings

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




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					["image_processor", "tokenizer"]
							lowerCamelCase_           =					"CLIPImageProcessor"
							lowerCamelCase_           =					("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")


							def __init__(							self    :Optional[int]							,				__A    :Dict=None							,				__A    :str=None							,				**__A    :str		)   ->   int:



									"""simple docstring"""



									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."""							,				__A							,				)
											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__(__A							,				__A		)


							def __call__(							self    :Any							,				__A    :Dict=None							,				__A    :List[str]=None							,				__A    :int=None							,				**__A    :int		)   ->   Any:



									"""simple docstring"""



									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(__A							,				return_tensors=__A							,				**__A		)

									if images is not None:
											SCREAMING_SNAKE_CASE__			=      self.image_processor(__A							,				return_tensors=__A							,				**__A		)

									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(**__A		)							,				tensor_type=__A		)


							def      _snake_case  (							self    :Union[str, Any]							,				*__A    :List[Any]							,				**__A    :Optional[int]		)   ->   Optional[int]:



									"""simple docstring"""



									return self.tokenizer.batch_decode(*__A							,				**__A		)


							def      _snake_case  (							self    :Union[str, Any]							,				*__A    :Dict							,				**__A    :Tuple		)   ->   Optional[int]:



									"""simple docstring"""



									return self.tokenizer.decode(*__A							,				**__A		)


							@property
							def      _snake_case  (							self    :int		)   ->   str:



									"""simple docstring"""



									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		)		)






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   UpperCamelCase_       (     unittest.TestCase    ):


							def      _snake_case  (							self    :Any		)   ->   int:



									"""simple docstring"""



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


							@require_multi_gpu
							def      _snake_case  (							self    :Optional[Any]		)   ->   Tuple:



									"""simple docstring"""



									print(f'''Found {torch.cuda.device_count()} devices.'''		)
									SCREAMING_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(__A							,				env=os.environ.copy()		)


							@require_multi_gpu
							def      _snake_case  (							self    :Tuple		)   ->   Optional[Any]:



									"""simple docstring"""



									print(f'''Found {torch.cuda.device_count()} devices.'''		)
									SCREAMING_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(__A							,				env=os.environ.copy()		)


							@require_multi_gpu
							def      _snake_case  (							self    :Optional[Any]		)   ->   Union[str, Any]:



									"""simple docstring"""



									SCREAMING_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(__A							,				env=os.environ.copy()		)


							@require_multi_gpu
							def      _snake_case  (							self    :Optional[int]		)   ->   str:



									"""simple docstring"""



									print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only'''		)
									SCREAMING_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(__A							,				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)