


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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


_A			=						logging.get_logger(__name__)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
     __UpperCamelCase   							=b.T
     __UpperCamelCase   							=np.sum(np.square(__lowerCAmelCase    )      ,					axis=1    )
     __UpperCamelCase   							=np.sum(np.square(__lowerCAmelCase    )      ,					axis=0    )
     __UpperCamelCase   							=np.matmul(__lowerCAmelCase      ,					__lowerCAmelCase    )
     __UpperCamelCase   							=aa[:, None] - 2 * ab + ba[None, :]
     return d

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[Any]      ,					SCREAMING_SNAKE_CASE__      : str    ):
     __UpperCamelCase   							=x.reshape(-1      ,					3    )
     __UpperCamelCase   							=squared_euclidean_distance(__lowerCAmelCase      ,					__lowerCAmelCase    )
     return np.argmin(__lowerCAmelCase      ,					axis=1    )






class    UpperCAmelCase__	( lowerCamelCase_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[Any]              =  ['''pixel_values''']






    def __init__(      self ,       A_ = None ,       A_ = True ,       A_ = None ,       A_ = PILImageResampling.BILINEAR ,       A_ = True ,       A_ = True ,       **A_ ,       )  ->	None:
         super().__init__(**__snake_case   )
         __UpperCamelCase   							=size if size is not None else {'height': 256, 'width': 256}
         __UpperCamelCase   							=get_size_dict(__snake_case   )
         __UpperCamelCase   							=np.array(__snake_case   ) if clusters is not None else None
         __UpperCamelCase   							=do_resize
         __UpperCamelCase   							=size
         __UpperCamelCase   							=resample
         __UpperCamelCase   							=do_normalize
         __UpperCamelCase   							=do_color_quantize






    def _a (      self ,       A_ ,       A_ ,       A_ = PILImageResampling.BILINEAR ,       A_ = None ,       **A_ ,       )  ->	np.ndarray:
         __UpperCamelCase   							=get_size_dict(__snake_case   )
         if "height" not in size or "width" not in size:
              raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}'   )
         return resize(
             __snake_case ,       size=(size['height'], size['width']) ,       resample=__snake_case ,       data_format=__snake_case ,       **__snake_case   )






    def _a (      self ,       A_ ,       A_ = None ,       )  ->	np.ndarray:
         __UpperCamelCase   							=rescale(image=__snake_case ,       scale=1 / 127.5 ,       data_format=__snake_case   )
         __UpperCamelCase   							=image - 1
         return image






    def _a (      self ,       A_ ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = ChannelDimension.FIRST ,       **A_ ,       )  ->	PIL.Image.Image:
         __UpperCamelCase   							=do_resize if do_resize is not None else self.do_resize
         __UpperCamelCase   							=size if size is not None else self.size
         __UpperCamelCase   							=get_size_dict(__snake_case   )
         __UpperCamelCase   							=resample if resample is not None else self.resample
         __UpperCamelCase   							=do_normalize if do_normalize is not None else self.do_normalize
         __UpperCamelCase   							=do_color_quantize if do_color_quantize is not None else self.do_color_quantize
         __UpperCamelCase   							=clusters if clusters is not None else self.clusters
         __UpperCamelCase   							=np.array(__snake_case   )

         __UpperCamelCase   							=make_list_of_images(__snake_case   )

         if not valid_images(__snake_case   ):
              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 or resample is None:
              raise ValueError('Size and resample must be specified if do_resize is True.'   )

         if do_color_quantize and clusters is None:
              raise ValueError('Clusters must be specified if do_color_quantize is True.'   )

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

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

         if do_normalize:
              __UpperCamelCase   							=[self.normalize(image=__snake_case   ) for image in images]

         if do_color_quantize:
              __UpperCamelCase   							=[to_channel_dimension_format(__snake_case ,       ChannelDimension.LAST   ) for image in images]
              # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
              __UpperCamelCase   							=np.array(__snake_case   )
              __UpperCamelCase   							=color_quantize(__snake_case ,       __snake_case   ).reshape(images.shape[:-1]   )

              # flatten to (batch_size, height*width)
              __UpperCamelCase   							=images.shape[0]
              __UpperCamelCase   							=images.reshape(__snake_case ,       -1   )

              # We need to convert back to a list of images to keep consistent behaviour across processors.
              __UpperCamelCase   							=list(__snake_case   )
         else:
              __UpperCamelCase   							=[to_channel_dimension_format(__snake_case ,       __snake_case   ) for image in images]

         __UpperCamelCase   							={'input_ids': images}
         return BatchFeature(data=__snake_case ,       tensor_type=__snake_case   )









import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler






@dataclass
class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		int              =  0
    UpperCAmelCase__						:		bool              =  False
    UpperCAmelCase__						:		float              =  3.0






class    UpperCAmelCase__	( unittest.TestCase		):



    """simple docstring"""






    def _a (      self   )  ->	Any:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         self.assertDictEqual(MockClass().to_kwargs() ,       {}   )
         self.assertDictEqual(MockClass(a=2   ).to_kwargs() ,       {'a': 2}   )
         self.assertDictEqual(MockClass(a=2 ,       b=A_   ).to_kwargs() ,       {'a': 2, 'b': True}   )
         self.assertDictEqual(MockClass(a=2 ,       c=2.25   ).to_kwargs() ,       {'a': 2, 'c': 2.25}   )






    @require_cuda
    def _a (      self   )  ->	Union[str, Any]:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         __UpperCamelCase   							=GradScalerKwargs(init_scale=1024 ,       growth_factor=2   )
         AcceleratorState._reset_state()
         __UpperCamelCase   							=Accelerator(mixed_precision='fp16' ,       kwargs_handlers=[scaler_handler]   )
         print(accelerator.use_fpaa   )
         __UpperCamelCase   							=accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale ,       1024.0   )
         self.assertEqual(scaler._growth_factor ,       2.0   )

         # Check the other values are at the default
         self.assertEqual(scaler._backoff_factor ,       0.5   )
         self.assertEqual(scaler._growth_interval ,       2000   )
         self.assertEqual(scaler._enabled ,       A_   )






    @require_multi_gpu
    def _a (      self   )  ->	Optional[int]:
         __UpperCamelCase   							=['torchrun', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__   )]
         execute_subprocess_async(A_ ,       env=os.environ.copy()   )


if __name__ == "__main__":
   _A			=						DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
   _A			=						Accelerator(kwargs_handlers=[ddp_scaler])
   _A			=						torch.nn.Linear(100, 200)
   _A			=						accelerator.prepare(model)

   # Check the values changed in kwargs
   _A			=						''
   _A			=						model.bucket_bytes_cap // (1024 * 1024)
   if observed_bucket_cap_map != 15:
      error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
   if model.find_unused_parameters is not True:
      error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

   # Check the values of the defaults
   if model.dim != 0:
      error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
   if model.broadcast_buffers is not True:
      error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
   if model.gradient_as_bucket_view is not False:
      error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

   # 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)





