






"""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    lowerCamelCase(self							):
		A_		:						List[str]	    =   inspect.getfile(accelerate.test_utils							)
		A_		:						Tuple	    =   os.path.sep.join(mod_file.split(os.path.sep							)[:-1] + ["""scripts""", """test_script.py"""]							)
		A_		:						Tuple	    =   os.path.sep.join(
		    mod_file.split(os.path.sep							)[:-1] + ["""scripts""", """test_distributed_data_loop.py"""]							)
		A_		:						str	    =   os.path.sep.join(mod_file.split(os.path.sep							)[:-1] + ["""scripts""", """test_ops.py"""]							)





	@require_multi_gpu
	def    lowerCamelCase(self							):
		print(f"""Found {torch.cuda.device_count()} devices."""							)
		A_		:						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(lowerCAmelCase_     ,     env=os.environ.copy()							)





	@require_multi_gpu
	def    lowerCamelCase(self							):
		print(f"""Found {torch.cuda.device_count()} devices."""							)
		A_		:						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(lowerCAmelCase_     ,     env=os.environ.copy()							)





	@require_multi_gpu
	def    lowerCamelCase(self							):
		A_		:						str	    =   ["""torchrun""", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__							)]
		with patch_environment(omp_num_threads=1							):
			execute_subprocess_async(lowerCAmelCase_     ,     env=os.environ.copy()							)





	@require_multi_gpu
	def    lowerCamelCase(self							):
		print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only"""							)
		A_		:						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(lowerCAmelCase_     ,     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)









"""simple docstring"""
import warnings

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



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[int]    =				["""image_processor""", """tokenizer"""]
	_A       :				List[Any]    =				"""ViTImageProcessor"""
	_A       :				Optional[Any]    =				("""CLIPTokenizer""", """CLIPTokenizerFast""")





	def __init__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     **lowerCAmelCase_							):
		A_		:						Union[str, Any]	    =   None
		if "feature_extractor" in kwargs:
			warnings.warn(
			    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
			    """ instead."""     ,     lowerCAmelCase_     ,     )
			A_		:						Union[str, Any]	    =   kwargs.pop("""feature_extractor"""							)

		A_		:						Optional[int]	    =   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__(lowerCAmelCase_     ,     lowerCAmelCase_							)





	def __call__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     **lowerCAmelCase_							):
		if text is None and visual_prompt is None and images is None:
			raise ValueError("""You have to specify either text, visual prompt or images."""							)

		if text is not None and visual_prompt is not None:
			raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""							)

		if text is not None:
			A_		:						Tuple	    =   self.tokenizer(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if visual_prompt is not None:
			A_		:						Union[str, Any]	    =   self.image_processor(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if images is not None:
			A_		:						Any	    =   self.image_processor(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if visual_prompt is not None and images is not None:
			A_		:						List[str]	    =   {
			    """pixel_values""": image_features.pixel_values,
			    """conditional_pixel_values""": prompt_features.pixel_values,
			}
			return encoding
		elif text is not None and images is not None:
			A_		:						str	    =   image_features.pixel_values
			return encoding
		elif text is not None:
			return encoding
		elif visual_prompt is not None:
			A_		:						Any	    =   {
			    """conditional_pixel_values""": prompt_features.pixel_values,
			}
			return encoding
		else:
			return BatchEncoding(data=dict(**lowerCAmelCase_							)     ,     tensor_type=lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.batch_decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	@property
	def    lowerCamelCase(self							):
		warnings.warn(
		    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""     ,     lowerCAmelCase_     ,     )
		return self.image_processor_class





	@property
	def    lowerCamelCase(self							):
		warnings.warn(
		    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""     ,     lowerCAmelCase_     ,     )
		return self.image_processor

