



import torch

from accelerate import Accelerator, DistributedType





class 		__SCREAMING_SNAKE_CASE      :






				"""simple docstring"""

				def __enter__(				self      ):






					"""simple docstring"""

					if self.enabled:
						_snake_case					:							str					     =   self.model.no_sync()
						self.model_sync_obj.__enter__()

					return self







				def __exit__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					if self.enabled:
						# Average all models on exit
						self._sync_and_avg_model_params()
						self.model_sync_obj.__exit__(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__      )







				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = True      ):






					"""simple docstring"""

					if accelerator.distributed_type not in [
					    DistributedType.NO,
					    DistributedType.MULTI_CPU,
					    DistributedType.MULTI_GPU,
					]:
						raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"""      )
					_snake_case					:							Union[str, Any]					     =   enabled and accelerator.distributed_type != DistributedType.NO
					_snake_case					:							Dict					     =   0
					if self.enabled:
						_snake_case					:							Any					     =   accelerator
						_snake_case					:							List[str]					     =   model
						_snake_case					:							Dict					     =   local_sgd_steps







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					self.num_steps += 1
					if not self.enabled:
						return

					if self.num_steps % self.local_sgd_steps == 0:
						self._sync_and_avg_model_params()







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					self.accelerator.wait_for_everyone()
					with self.accelerator.autocast():
						for param in self.model.parameters():
							_snake_case					:							Optional[int]					     =   self.accelerator.reduce(param.data						,	reduction="""mean"""      )







import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
					from PIL import Image







class 			UpperCamelCase__		(     unittest.TestCase,      SCREAMING_SNAKE_CASE_ ):







	'''simple docstring'''





	def 	__snake_case					(      self		):
					A__	:						Dict								=				load_tool('''image-question-answering'''		)
					self.tool.setup()
					A__	:						List[Any]								=				load_tool('''image-question-answering'''					,							remote=UpperCamelCase__		)





	def 	__snake_case					(      self		):
					A__	:						str								=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		)
					A__	:						List[str]								=				self.tool(UpperCamelCase__					,							'''How many cats are sleeping on the couch?'''		)
					self.assertEqual(UpperCamelCase__					,							'''2'''		)





	def 	__snake_case					(      self		):
					A__	:						Tuple								=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		)
					A__	:						Dict								=				self.remote_tool(UpperCamelCase__					,							'''How many cats are sleeping on the couch?'''		)
					self.assertEqual(UpperCamelCase__					,							'''2'''		)





	def 	__snake_case					(      self		):
					A__	:						Dict								=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		)
					A__	:						int								=				self.tool(image=UpperCamelCase__					,							question='''How many cats are sleeping on the couch?'''		)
					self.assertEqual(UpperCamelCase__					,							'''2'''		)





	def 	__snake_case					(      self		):
					A__	:						Tuple								=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		)
					A__	:						str								=				self.remote_tool(image=UpperCamelCase__					,							question='''How many cats are sleeping on the couch?'''		)
					self.assertEqual(UpperCamelCase__					,							'''2'''		)