

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase__							=			logging.get_logger(__name__)






class     __magic_name__     (							__lowerCAmelCase     ):
 UpperCAmelCase				     =    ["""pixel_values"""]


 def __init__(    self		:			str      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Optional[Dict[str, int]] = None      ,    lowerCAmelCase__		:			PILImageResampling = PILImageResampling.BILINEAR      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Union[int, float] = 1 / 2_5_5      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Optional[Union[float, List[float]]] = None      ,    lowerCAmelCase__		:			Optional[Union[float, List[float]]] = None      ,    **lowerCAmelCase__		:			Any      ,    )     ->   None:
   super().__init__(**_UpperCamelCase     )
   UpperCAmelCase          =	size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
   UpperCAmelCase          =	get_size_dict(_UpperCamelCase     )
   UpperCAmelCase          =	do_resize
   UpperCAmelCase          =	do_rescale
   UpperCAmelCase          =	do_normalize
   UpperCAmelCase          =	size
   UpperCAmelCase          =	resample
   UpperCAmelCase          =	rescale_factor
   UpperCAmelCase          =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   UpperCAmelCase          =	image_std if image_std is not None else IMAGENET_STANDARD_STD


 def _UpperCamelCase						(    self		:			Any      ,    lowerCAmelCase__		:			np.ndarray      ,    lowerCAmelCase__		:			Dict[str, int]      ,    lowerCAmelCase__		:			PILImageResampling = PILImageResampling.BILINEAR      ,    lowerCAmelCase__		:			Optional[Union[str, ChannelDimension]] = None      ,    **lowerCAmelCase__		:			Dict      ,    )     ->   np.ndarray:
   UpperCAmelCase          =	get_size_dict(_UpperCamelCase     )
   if "height" not in size or "width" not in size:
     raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"     )
   return resize(
       _UpperCamelCase      ,    size=(size["height"], size["width"])      ,    resample=_UpperCamelCase      ,    data_format=_UpperCamelCase      ,    **_UpperCamelCase     )


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			np.ndarray      ,    lowerCAmelCase__		:			float      ,    lowerCAmelCase__		:			Optional[Union[str, ChannelDimension]] = None      ,    **lowerCAmelCase__		:			Optional[Any]     )     ->   np.ndarray:
   return rescale(_UpperCamelCase      ,    scale=_UpperCamelCase      ,    data_format=_UpperCamelCase      ,    **_UpperCamelCase     )


 def _UpperCamelCase						(    self		:			str      ,    lowerCAmelCase__		:			np.ndarray      ,    lowerCAmelCase__		:			Union[float, List[float]]      ,    lowerCAmelCase__		:			Union[float, List[float]]      ,    lowerCAmelCase__		:			Optional[Union[str, ChannelDimension]] = None      ,    **lowerCAmelCase__		:			int      ,    )     ->   np.ndarray:
   return normalize(_UpperCamelCase      ,    mean=_UpperCamelCase      ,    std=_UpperCamelCase      ,    data_format=_UpperCamelCase      ,    **_UpperCamelCase     )


 def _UpperCamelCase						(    self		:			Any      ,    lowerCAmelCase__		:			ImageInput      ,    lowerCAmelCase__		:			Optional[bool] = None      ,    lowerCAmelCase__		:			Dict[str, int] = None      ,    lowerCAmelCase__		:			PILImageResampling = None      ,    lowerCAmelCase__		:			Optional[bool] = None      ,    lowerCAmelCase__		:			Optional[float] = None      ,    lowerCAmelCase__		:			Optional[bool] = None      ,    lowerCAmelCase__		:			Optional[Union[float, List[float]]] = None      ,    lowerCAmelCase__		:			Optional[Union[float, List[float]]] = None      ,    lowerCAmelCase__		:			Optional[Union[str, TensorType]] = None      ,    lowerCAmelCase__		:			Union[str, ChannelDimension] = ChannelDimension.FIRST      ,    **lowerCAmelCase__		:			Dict      ,    )     ->   Union[str, Any]:
   UpperCAmelCase          =	do_resize if do_resize is not None else self.do_resize
   UpperCAmelCase          =	do_rescale if do_rescale is not None else self.do_rescale
   UpperCAmelCase          =	do_normalize if do_normalize is not None else self.do_normalize
   UpperCAmelCase          =	resample if resample is not None else self.resample
   UpperCAmelCase          =	rescale_factor if rescale_factor is not None else self.rescale_factor
   UpperCAmelCase          =	image_mean if image_mean is not None else self.image_mean
   UpperCAmelCase          =	image_std if image_std is not None else self.image_std

   UpperCAmelCase          =	size if size is not None else self.size
   UpperCAmelCase          =	get_size_dict(_UpperCamelCase     )

   UpperCAmelCase          =	make_list_of_images(_UpperCamelCase     )

   if not valid_images(_UpperCamelCase     ):
     raise ValueError(
         "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
         "torch.Tensor, tf.Tensor or jax.ndarray."     )

   if do_resize and size is None:
     raise ValueError("Size must be specified if do_resize is True."     )

   if do_rescale and rescale_factor is None:
     raise ValueError("Rescale factor must be specified if do_rescale is True."     )

   # All transformations expect numpy arrays.
   UpperCAmelCase          =	[to_numpy_array(_UpperCamelCase     ) for image in images]

   if do_resize:
     UpperCAmelCase          =	[self.resize(image=_UpperCamelCase      ,    size=_UpperCamelCase      ,    resample=_UpperCamelCase     ) for image in images]

   if do_rescale:
     UpperCAmelCase          =	[self.rescale(image=_UpperCamelCase      ,    scale=_UpperCamelCase     ) for image in images]

   if do_normalize:
     UpperCAmelCase          =	[self.normalize(image=_UpperCamelCase      ,    mean=_UpperCamelCase      ,    std=_UpperCamelCase     ) for image in images]

   UpperCAmelCase          =	[to_channel_dimension_format(_UpperCamelCase      ,    _UpperCamelCase     ) for image in images]

   UpperCAmelCase          =	{"""pixel_values""": images}
   return BatchFeature(data=_UpperCamelCase      ,    tensor_type=_UpperCamelCase     )




import torch

from accelerate import Accelerator, DistributedType




class 						__snake_case						:





      '''simple docstring'''






      def __enter__(       self  :					Union[str, Any])					->Dict:


          """simple docstring"""


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

          return self



      def __exit__(       self  :					List[str]     ,	_UpperCamelCase  :					Any     ,	_UpperCamelCase  :					Union[str, Any]     ,	_UpperCamelCase  :					int)					->Optional[Any]:


          """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  :					Union[str, Any]     ,	_UpperCamelCase  :					Accelerator     ,	_UpperCamelCase  :					torch.nn.Module     ,	_UpperCamelCase  :					int     ,	_UpperCamelCase  :					bool = True)					->int:


          """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)""")
          _lowerCamelCase  :		str			    =    enabled and accelerator.distributed_type != DistributedType.NO
          _lowerCamelCase  :		Optional[int]			    =    0
          if self.enabled:
              _lowerCamelCase  :		Union[str, Any]			    =    accelerator
              _lowerCamelCase  :		int			    =    model
              _lowerCamelCase  :		int			    =    local_sgd_steps



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any])					->Union[str, 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[Any])					->List[str]:


          """simple docstring"""


          self.accelerator.wait_for_everyone()
          with self.accelerator.autocast():
              for param in self.model.parameters():
                  _lowerCamelCase  :		Union[str, Any]			    =    self.accelerator.reduce(param.data     ,	reduction="""mean""")




