



import warnings

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





class    __lowerCAmelCase					(	lowerCamelCase__			):
				__lowerCamelCase					=							['''image_processor''', '''tokenizer''']
				__lowerCamelCase					=							'''AutoImageProcessor'''
				__lowerCamelCase					=							'''AutoTokenizer'''




				def __init__(				self		,							_snake_case=None		,							_snake_case=None		,							**_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       None
									if "feature_extractor" in kwargs:
														warnings.warn(
														    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
														    """ instead."""		,							_snake_case		,							)
														_lowerCAmelCase         =       kwargs.pop("""feature_extractor"""						)

									_lowerCAmelCase         =       image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
														raise ValueError("""You have to specify an image_processor."""						)
									if tokenizer is None:
														raise ValueError("""You have to specify a tokenizer."""						)

									super().__init__(_snake_case		,							_snake_case						)
									_lowerCAmelCase         =       self.image_processor




				def __call__(				self		,							_snake_case=None		,							_snake_case=None		,							_snake_case=None		,							**_snake_case						):



									"""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:
														_lowerCAmelCase         =       self.tokenizer(_snake_case		,							return_tensors=_snake_case		,							**_snake_case						)

									if images is not None:
														_lowerCAmelCase         =       self.image_processor(_snake_case		,							return_tensors=_snake_case		,							**_snake_case						)

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




				def 				snake_case  (				self		,							*_snake_case		,							**_snake_case						):



									"""simple docstring"""





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




				def 				snake_case  (				self		,							*_snake_case		,							**_snake_case						):



									"""simple docstring"""





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




				@property
				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.tokenizer.model_input_names
									_lowerCAmelCase         =       self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)




				@property
				def 				snake_case  (				self						):



									"""simple docstring"""





									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""		,							_snake_case		,							)
									return self.image_processor_class




				@property
				def 				snake_case  (				self						):



									"""simple docstring"""





									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""		,							_snake_case		,							)
									return self.image_processor











import torch

from accelerate import Accelerator, DistributedType

class        snake_case_				:







   """simple docstring"""



   def __enter__(     self:						int						)	->		Tuple:


       """simple docstring"""



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

       return self

   def __exit__(     self:						str ,							snake_case__:						Tuple ,							snake_case__:						int ,							snake_case__:						Tuple						)	->		Any:


       """simple docstring"""



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

   def __init__(     self:						int ,							snake_case__:						Accelerator ,							snake_case__:						torch.nn.Module ,							snake_case__:						int ,							snake_case__:						bool = True						)	->		Optional[Any]:


       """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)'						)
       _lowercase     			=						enabled and accelerator.distributed_type != DistributedType.NO
       _lowercase     			=						0
       if self.enabled:
           _lowercase     			=						accelerator
           _lowercase     			=						model
           _lowercase     			=						local_sgd_steps

   def       SCREAMING_SNAKE_CASE__					(     self:						str						)	->		List[Any]:


       """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       SCREAMING_SNAKE_CASE__					(     self:						Optional[int]						)	->		Dict:


       """simple docstring"""



       self.accelerator.wait_for_everyone()
       with self.accelerator.autocast():
           for param in self.model.parameters():
               _lowercase     			=						self.accelerator.reduce(param.data ,							reduction='mean'						)
