'''simple docstring'''





from typing import Dict, Iterable, List, Optional, Union

import numpy as np

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


_lowerCAmelCase  :Optional[Any]  				=    logging.get_logger(__name__)






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Dict							=						["pixel_values"]






    def __init__(       self    ,							lowercase__ = True    ,							lowercase__ = None    ,							lowercase__ = PILImageResampling.BICUBIC    ,							lowercase__ = True    ,							lowercase__ = None    ,							lowercase__ = True    ,							lowercase__ = 1 / 255    ,							lowercase__ = True    ,							lowercase__ = IMAGENET_DEFAULT_MEAN    ,							lowercase__ = IMAGENET_DEFAULT_STD    ,							**lowercase__    ,							)							-> None:
         super().__init__(**lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       size if size is not None else {'shortest_edge': 224}
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       get_size_dict(lowercase__    ,							default_to_square=lowercase__ )
         SCREAMING_SNAKE_CASE					:			int    	=       crop_size if crop_size is not None else {'height': 224, 'width': 224}
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       get_size_dict(lowercase__    ,							param_name='crop_size' )

         SCREAMING_SNAKE_CASE					:			str    	=       do_resize
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       size
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       resample
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       do_center_crop
         SCREAMING_SNAKE_CASE					:			str    	=       crop_size
         SCREAMING_SNAKE_CASE					:			str    	=       do_rescale
         SCREAMING_SNAKE_CASE					:			str    	=       rescale_factor
         SCREAMING_SNAKE_CASE					:			Dict    	=       do_normalize
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         SCREAMING_SNAKE_CASE					:			Dict    	=       image_std if image_std is not None else IMAGENET_DEFAULT_STD






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ = PILImageResampling.BICUBIC    ,							lowercase__ = None    ,							**lowercase__    ,							)							-> np.ndarray:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       get_size_dict(lowercase__    ,							default_to_square=lowercase__ )
         # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
         if "shortest_edge" in size:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       int((256 / 224) * size['shortest_edge'] )
              SCREAMING_SNAKE_CASE					:			str    	=       get_resize_output_image_size(lowercase__    ,							size=lowercase__    ,							default_to_square=lowercase__ )
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       {'height': output_size[0], 'width': output_size[1]}
         if "height" not in size_dict or "width" not in size_dict:
              raise ValueError(
                  F"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}""" )
         return resize(
             lowercase__    ,							size=(size_dict['height'], size_dict['width'])    ,							resample=lowercase__    ,							data_format=lowercase__    ,							**lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ = None    ,							**lowercase__    ,							)							-> np.ndarray:
         SCREAMING_SNAKE_CASE					:			Any    	=       get_size_dict(lowercase__ )
         if "height" not in size or "width" not in size:
              raise ValueError(F"""Size dict must have keys 'height' and 'width'. Got {size.keys()}""" )
         return center_crop(lowercase__    ,							size=(size['height'], size['width'])    ,							data_format=lowercase__    ,							**lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ = None    ,							**lowercase__    ,							)							-> np.ndarray:
         return rescale(lowercase__    ,							scale=lowercase__    ,							data_format=lowercase__    ,							**lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ = None    ,							**lowercase__    ,							)							-> np.ndarray:
         return normalize(lowercase__    ,							mean=lowercase__    ,							std=lowercase__    ,							data_format=lowercase__    ,							**lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = ChannelDimension.FIRST    ,							**lowercase__    ,							)							-> BatchFeature:
         SCREAMING_SNAKE_CASE					:			Any    	=       do_resize if do_resize is not None else self.do_resize
         SCREAMING_SNAKE_CASE					:			str    	=       resample if resample is not None else self.resample
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       do_center_crop if do_center_crop is not None else self.do_center_crop
         SCREAMING_SNAKE_CASE					:			Any    	=       do_rescale if do_rescale is not None else self.do_rescale
         SCREAMING_SNAKE_CASE					:			List[str]    	=       rescale_factor if rescale_factor is not None else self.rescale_factor
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       do_normalize if do_normalize is not None else self.do_normalize
         SCREAMING_SNAKE_CASE					:			List[str]    	=       image_mean if image_mean is not None else self.image_mean
         SCREAMING_SNAKE_CASE					:			int    	=       image_std if image_std is not None else self.image_std

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       size if size is not None else self.size
         SCREAMING_SNAKE_CASE					:			Dict    	=       get_size_dict(lowercase__    ,							default_to_square=lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       crop_size if crop_size is not None else self.crop_size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       get_size_dict(lowercase__    ,							param_name='crop_size' )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       make_list_of_images(lowercase__ )

         if not valid_images(lowercase__ ):
              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_center_crop and crop_size is None:
              raise ValueError('Crop size must be specified if do_center_crop is True.' )

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

         if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('Image mean and std must be specified if do_normalize is True.' )

         # All transformations expect numpy arrays.
         SCREAMING_SNAKE_CASE					:			Tuple    	=       [to_numpy_array(lowercase__ ) for image in images]

         if do_resize:
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [self.resize(lowercase__    ,							lowercase__    ,							lowercase__ ) for image in images]

         if do_center_crop:
              SCREAMING_SNAKE_CASE					:			Tuple    	=       [self.center_crop(lowercase__    ,							lowercase__ ) for image in images]

         if do_rescale:
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [self.rescale(lowercase__    ,							lowercase__ ) for image in images]

         if do_normalize:
              SCREAMING_SNAKE_CASE					:			Any    	=       [self.normalize(lowercase__    ,							lowercase__    ,							lowercase__ ) for image in images]

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       [to_channel_dimension_format(lowercase__    ,							lowercase__ ) for image in images]

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       {'pixel_values': images}
         return BatchFeature(data=lowercase__    ,							tensor_type=lowercase__ )




'''simple docstring'''





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 (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







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






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> Tuple:
         # 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=lowercase__ ).to_kwargs()    ,							{'a': 2, 'b': True} )
         self.assertDictEqual(MockClass(a=2    ,							c=2.2_5 ).to_kwargs()    ,							{'a': 2, 'c': 2.2_5} )






    @require_cuda
    def       _UpperCamelCase    (       self )							-> Dict:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       GradScalerKwargs(init_scale=1_024    ,							growth_factor=2 )
         AcceleratorState._reset_state()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       Accelerator(mixed_precision='fp16'    ,							kwargs_handlers=[scaler_handler] )
         print(accelerator.use_fpaa )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale    ,							1_0_2_4.0 )
         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    ,							2_000 )
         self.assertEqual(scaler._enabled    ,							lowercase__ )






    @require_multi_gpu
    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
         execute_subprocess_async(lowercase__    ,							env=os.environ.copy() )


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

 # Check the values changed in kwargs
 _lowerCAmelCase  :Dict  				=    """"""
 _lowerCAmelCase  :Optional[int]  				=    model.bucket_bytes_cap // (1_024 * 1_024)
 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)



