





import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm

def      __snake_case						(					_lowerCAmelCase : List[str]				,				_lowerCAmelCase : Dict	) ->						Tuple:
   if isinstance(_lowerCAmelCase				,				(list, tuple)	):
      return honor_type(_lowerCAmelCase				,				(move_to_device(_lowerCAmelCase				,				_lowerCAmelCase	) for t in state)	)
   elif isinstance(_lowerCAmelCase				,				_lowerCAmelCase	):
      return type(_lowerCAmelCase	)({k: move_to_device(_lowerCAmelCase				,				_lowerCAmelCase	) for k, v in state.items()}	)
   elif isinstance(_lowerCAmelCase				,				torch.Tensor	):
      return state.to(_lowerCAmelCase	)
   return state





class 					__magic_name__  (							torch.optim.Optimizer							):





     """simple docstring"""



     def __init__(							self       :str			,      snake_case       :str			,      snake_case       :Dict=True			,      snake_case       :str=None  ):





        '''simple docstring'''




        A_					:				Union[str, Any]          =			optimizer
        A_					:				Optional[int]          =			scaler
        A_					:				int          =			AcceleratorState()
        A_					:				Optional[int]          =			GradientState()
        A_					:				Dict          =			device_placement
        A_					:				Union[str, Any]          =			False
        A_					:				Tuple          =			None

        # Handle device placement
        if device_placement:
           A_					:				List[Any]          =			self.optimizer.state_dict()
           if self.accelerator_state.distributed_type == DistributedType.TPU:
              xm.send_cpu_data_to_device(snake_case			,      self.accelerator_state.device  )
           else:
              A_					:				Optional[int]          =			move_to_device(snake_case			,      self.accelerator_state.device  )
           self.optimizer.load_state_dict(snake_case  )



     @property
     def 						SCREAMING_SNAKE_CASE		(							self       :str  ):





        '''simple docstring'''




        return self.optimizer.state



     @state.setter
     def 						SCREAMING_SNAKE_CASE		(							self       :str			,      snake_case       :Dict  ):





        '''simple docstring'''




        A_					:				int          =			state



     @property
     def 						SCREAMING_SNAKE_CASE		(							self       :int  ):





        '''simple docstring'''




        return self.optimizer.param_groups



     @param_groups.setter
     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]			,      snake_case       :List[str]  ):





        '''simple docstring'''




        A_					:				Dict          =			param_groups



     @property
     def 						SCREAMING_SNAKE_CASE		(							self       :Union[str, Any]  ):





        '''simple docstring'''




        return self.optimizer.defaults



     @defaults.setter
     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]			,      snake_case       :int  ):





        '''simple docstring'''




        A_					:				Optional[Any]          =			defaults



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]			,      snake_case       :List[str]  ):





        '''simple docstring'''




        self.optimizer.add_param_group(snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Dict			,      snake_case       :Optional[Any]  ):





        '''simple docstring'''




        if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
           xm.send_cpu_data_to_device(snake_case			,      self.accelerator_state.device  )
        self.optimizer.load_state_dict(snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :str  ):





        '''simple docstring'''




        return self.optimizer.state_dict()



     def 						SCREAMING_SNAKE_CASE		(							self       :List[str]			,      snake_case       :Optional[Any]=None  ):





        '''simple docstring'''




        if self.gradient_state.sync_gradients:
           A_					:				List[Any]          =			"set_to_none" in inspect.signature(self.optimizer.zero_grad  ).parameters
           if accept_arg:
              if set_to_none is None:
                 A_					:				Union[str, Any]          =			False
              self.optimizer.zero_grad(set_to_none=snake_case  )
           else:
              if set_to_none is not None:
                 raise ValueError("`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer."  )
              self.optimizer.zero_grad()



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]			,      snake_case       :Optional[Any]=None  ):





        '''simple docstring'''




        if self.gradient_state.sync_gradients:
           if self.accelerator_state.distributed_type == DistributedType.TPU:
              A_					:				int          =			{"closure": closure} if closure is not None else {}
              xm.optimizer_step(self.optimizer			,      optimizer_args=snake_case  )
           elif self.scaler is not None:
              A_					:				Optional[int]          =			False
              if self._last_scale is None:
                 # `get_scale` is an async operation requiring full synchronization
                 # on CPU and GPUs before finishing. As a result, we store away
                 # the prior one to reduce the call overhead
                 A_					:				str          =			self.scaler.get_scale()
                 A_					:				Optional[Any]          =			True
              self.scaler.step(self.optimizer			,      snake_case  )
              self.scaler.update()
              A_					:				Union[str, Any]          =			self.scaler.get_scale()
              if not new_scale:
                 # If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
                 A_					:				Any          =			scale_after < self._last_scale
              A_					:				List[str]          =			scale_after

           else:
              self.optimizer.step(snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]			,      snake_case       :str  ):





        '''simple docstring'''




        for param_group in self.optimizer.param_groups:
           A_					:				Any          =			[parameters_map.get(snake_case			,      snake_case  ) for p in param_group["params"]]



     @property
     def 						SCREAMING_SNAKE_CASE		(							self       :Any  ):





        '''simple docstring'''




        warnings.warn(
            "The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use "
            "`optimizer.step_was_skipped` instead."			,      snake_case			,      )
        return self._is_overflow



     @property
     def 						SCREAMING_SNAKE_CASE		(							self       :Dict  ):





        '''simple docstring'''




        return self._is_overflow



     def __getstate__(							self       :Optional[Any]  ):





        '''simple docstring'''




        return self.__dict__.copy()





     def __setstate__(							self       :Optional[int]			,      snake_case       :Optional[int]  ):





        '''simple docstring'''




        self.__dict__.update(snake_case  )







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import ViltImageProcessor


class 					__magic_name__  (							unittest.TestCase							):





     """simple docstring"""



     def __init__(							self       :Dict			,      snake_case       :str			,      snake_case       :Tuple=7			,      snake_case       :Union[str, Any]=3			,      snake_case       :Any=18			,      snake_case       :Tuple=30			,      snake_case       :List[str]=400			,      snake_case       :Optional[Any]=True			,      snake_case       :Optional[int]=None			,      snake_case       :str=2			,      snake_case       :Tuple=True			,      snake_case       :List[str]=[0.5, 0.5, 0.5]			,      snake_case       :int=[0.5, 0.5, 0.5]			,      ):





        '''simple docstring'''




        A_					:				Optional[Any]          =			size if size is not None else {"shortest_edge": 30}
        A_					:				Tuple          =			parent
        A_					:				Tuple          =			batch_size
        A_					:				Any          =			num_channels
        A_					:				Dict          =			image_size
        A_					:				int          =			min_resolution
        A_					:				Optional[Any]          =			max_resolution
        A_					:				Union[str, Any]          =			do_resize
        A_					:				List[Any]          =			size
        A_					:				List[str]          =			size_divisor
        A_					:				str          =			do_normalize
        A_					:				int          =			image_mean
        A_					:				str          =			image_std



     def 						SCREAMING_SNAKE_CASE		(							self       :List[Any]  ):





        '''simple docstring'''




        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }





     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]			,      snake_case       :Tuple			,      snake_case       :List[str]=False  ):





        '''simple docstring'''




        if not batched:
           A_					:				List[Any]          =			self.size["shortest_edge"]
           A_					:				Tuple          =			image_inputs[0]
           if isinstance(snake_case			,      Image.Image  ):
              A_      ,						A_					:				str          =			image.size
           else:
              A_      ,						A_					:				Optional[int]          =			image.shape[1], image.shape[2]
           A_					:				int          =			size / min(snake_case			,      snake_case  )
           if h < w:
              A_      ,						A_					:				Dict          =			size, scale * w
           else:
              A_      ,						A_					:				Tuple          =			scale * h, size

           A_					:				List[Any]          =			int((1_333 / 800) * size  )
           if max(snake_case			,      snake_case  ) > max_size:
              A_					:				Union[str, Any]          =			max_size / max(snake_case			,      snake_case  )
              A_					:				Tuple          =			newh * scale
              A_					:				List[str]          =			neww * scale

           A_      ,						A_					:				Optional[int]          =			int(newh + 0.5  ), int(neww + 0.5  )
           A_      ,						A_					:				Tuple          =			(
               newh // self.size_divisor * self.size_divisor,
               neww // self.size_divisor * self.size_divisor,
           )

        else:
           A_					:				List[Any]          =			[]
           for image in image_inputs:
              A_      ,						A_					:				str          =			self.get_expected_values([image]  )
              expected_values.append((expected_height, expected_width)  )
           A_					:				Optional[Any]          =			max(snake_case			,      key=lambda snake_case  : item[0]  )[0]
           A_					:				Optional[int]          =			max(snake_case			,      key=lambda snake_case  : item[1]  )[1]

        return expected_height, expected_width




@require_torch
@require_vision
class 					__magic_name__  (							lowerCamelCase__    ,				unittest.TestCase							):





     """simple docstring"""



     __UpperCamelCase					    =     ViltImageProcessor if is_vision_available() else None



     def 						SCREAMING_SNAKE_CASE		(							self       :Union[str, Any]  ):





        '''simple docstring'''




        A_					:				Dict          =			ViltImageProcessingTester(self  )



     @property
     def 						SCREAMING_SNAKE_CASE		(							self       :Union[str, Any]  ):





        '''simple docstring'''




        return self.image_processor_tester.prepare_image_processor_dict()



     def 						SCREAMING_SNAKE_CASE		(							self       :List[Any]  ):





        '''simple docstring'''




        A_					:				Optional[int]          =			self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(snake_case			,      "image_mean"  )  )
        self.assertTrue(hasattr(snake_case			,      "image_std"  )  )
        self.assertTrue(hasattr(snake_case			,      "do_normalize"  )  )
        self.assertTrue(hasattr(snake_case			,      "do_resize"  )  )
        self.assertTrue(hasattr(snake_case			,      "size"  )  )
        self.assertTrue(hasattr(snake_case			,      "size_divisor"  )  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Tuple  ):





        '''simple docstring'''




        A_					:				Tuple          =			self.image_processing_class.from_dict(self.image_processor_dict  )
        self.assertEqual(image_processor.size			,      {"shortest_edge": 30}  )

        A_					:				Any          =			self.image_processing_class.from_dict(self.image_processor_dict			,      size=42  )
        self.assertEqual(image_processor.size			,      {"shortest_edge": 42}  )



     def 						SCREAMING_SNAKE_CASE		(							self       :List[str]  ):





        '''simple docstring'''




        pass



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]  ):





        '''simple docstring'''




        A_					:				Dict          =			self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        A_					:				str          =			prepare_image_inputs(self.image_processor_tester			,      equal_resolution=snake_case  )
        for image in image_inputs:
           self.assertIsInstance(snake_case			,      Image.Image  )

        # Test not batched input
        A_					:				Optional[Any]          =			image_processing(image_inputs[0]			,      return_tensors="pt"  ).pixel_values

        A_      ,						A_					:				str          =			self.image_processor_tester.get_expected_values(snake_case  )
        self.assertEqual(
            encoded_images.shape			,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,      )

        # Test batched
        A_					:				int          =			image_processing(snake_case			,      return_tensors="pt"  ).pixel_values

        A_      ,						A_					:				Dict          =			self.image_processor_tester.get_expected_values(snake_case			,      batched=snake_case  )
        self.assertEqual(
            encoded_images.shape			,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )			,      )



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]  ):





        '''simple docstring'''




        A_					:				Any          =			self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        A_					:				List[str]          =			prepare_image_inputs(self.image_processor_tester			,      equal_resolution=snake_case			,      numpify=snake_case  )
        for image in image_inputs:
           self.assertIsInstance(snake_case			,      np.ndarray  )

        # Test not batched input
        A_					:				List[Any]          =			image_processing(image_inputs[0]			,      return_tensors="pt"  ).pixel_values

        A_      ,						A_					:				int          =			self.image_processor_tester.get_expected_values(snake_case  )
        self.assertEqual(
            encoded_images.shape			,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,      )

        # Test batched
        A_					:				Dict          =			image_processing(snake_case			,      return_tensors="pt"  ).pixel_values

        A_      ,						A_					:				str          =			self.image_processor_tester.get_expected_values(snake_case			,      batched=snake_case  )
        self.assertEqual(
            encoded_images.shape			,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )			,      )





     def 						SCREAMING_SNAKE_CASE		(							self       :str  ):





        '''simple docstring'''




        A_					:				Any          =			self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        A_					:				Optional[Any]          =			prepare_image_inputs(self.image_processor_tester			,      equal_resolution=snake_case			,      torchify=snake_case  )
        for image in image_inputs:
           self.assertIsInstance(snake_case			,      torch.Tensor  )

        # Test not batched input
        A_					:				int          =			image_processing(image_inputs[0]			,      return_tensors="pt"  ).pixel_values

        A_      ,						A_					:				Union[str, Any]          =			self.image_processor_tester.get_expected_values(snake_case  )
        self.assertEqual(
            encoded_images.shape			,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,      )

        # Test batched
        A_					:				List[str]          =			image_processing(snake_case			,      return_tensors="pt"  ).pixel_values

        A_      ,						A_					:				Optional[int]          =			self.image_processor_tester.get_expected_values(snake_case			,      batched=snake_case  )
        self.assertEqual(
            encoded_images.shape			,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )			,      )
