
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 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   int				       =0
	A__    :   bool				       =False
	A__    :   float				       =3.0





class 						lowercase__  (						unittest.TestCase  ):
	def      A_				(			self							:   int							):
						# 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=UpperCAmelCase_							).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							:   Optional[int]							):
						# If no defaults are changed, `to_kwargs` returns an empty dict.
						SCREAMING_SNAKE_CASE__													= GradScalerKwargs(init_scale=1024       ,   growth_factor=2							)
						AcceleratorState._reset_state()
						SCREAMING_SNAKE_CASE__													= Accelerator(mixed_precision='fp16'       ,   kwargs_handlers=[scaler_handler]							)
						print(accelerator.use_fpaa							)
						SCREAMING_SNAKE_CASE__													= accelerator.scaler

						# Check the kwargs have been applied
						self.assertEqual(scaler._init_scale       ,   1_024.0							)
						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       ,   UpperCAmelCase_							)





	@require_multi_gpu
	def      A_				(			self							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= ['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__							)]
						execute_subprocess_async(UpperCAmelCase_       ,   env=os.environ.copy()							)


if __name__ == "__main__":
				__snake_case								=  DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
				__snake_case								=  Accelerator(kwargs_handlers=[ddp_scaler])
				__snake_case								=  torch.nn.Linear(1_00, 2_00)
				__snake_case								=  accelerator.prepare(model)

				# Check the values changed in kwargs
				__snake_case								=  """"""
				__snake_case								=  model.bucket_bytes_cap // (10_24 * 10_24)
				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)







from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__snake_case								=  logging.get_logger(__name__)




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   List[str]				       =["""pixel_values"""]
	def __init__(			self							:   Dict       ,   UpperCAmelCase_							:   bool = True       ,   UpperCAmelCase_							:   int = 32       ,   UpperCAmelCase_							:   int=PILImageResampling.BILINEAR       ,   UpperCAmelCase_							:   bool = True       ,   **UpperCAmelCase_							:   Any       ,   ):
						SCREAMING_SNAKE_CASE__													= do_resize
						SCREAMING_SNAKE_CASE__													= do_rescale
						SCREAMING_SNAKE_CASE__													= size_divisor
						SCREAMING_SNAKE_CASE__													= resample
						super().__init__(**UpperCAmelCase_							)
	def      A_				(			self							:   Optional[int]       ,   UpperCAmelCase_							:   np.ndarray       ,   UpperCAmelCase_							:   int       ,   UpperCAmelCase_							:   Dict       ,   UpperCAmelCase_							:   Optional[ChannelDimension] = None       ,   **UpperCAmelCase_							:   Dict							):
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= get_image_size(UpperCAmelCase_							)
						# Rounds the height and width down to the closest multiple of size_divisor
						SCREAMING_SNAKE_CASE__													= height // size_divisor * size_divisor
						SCREAMING_SNAKE_CASE__													= width // size_divisor * size_divisor
						SCREAMING_SNAKE_CASE__													= resize(UpperCAmelCase_       ,   (new_h, new_w)       ,   resample=UpperCAmelCase_       ,   data_format=UpperCAmelCase_       ,   **UpperCAmelCase_							)
						return image
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   np.ndarray       ,   UpperCAmelCase_							:   float       ,   UpperCAmelCase_							:   Optional[ChannelDimension] = None       ,   **UpperCAmelCase_							:   List[str]							):
						return rescale(image=UpperCAmelCase_       ,   scale=UpperCAmelCase_       ,   data_format=UpperCAmelCase_       ,   **UpperCAmelCase_							)





	def      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,   UpperCAmelCase_							:   Optional[bool] = None       ,   UpperCAmelCase_							:   Optional[int] = None       ,   UpperCAmelCase_							:   List[str]=None       ,   UpperCAmelCase_							:   Optional[bool] = None       ,   UpperCAmelCase_							:   Optional[Union[TensorType, str]] = None       ,   UpperCAmelCase_							:   ChannelDimension = ChannelDimension.FIRST       ,   **UpperCAmelCase_							:   List[Any]       ,   ):
						SCREAMING_SNAKE_CASE__													= do_resize if do_resize is not None else self.do_resize
						SCREAMING_SNAKE_CASE__													= do_rescale if do_rescale is not None else self.do_rescale
						SCREAMING_SNAKE_CASE__													= size_divisor if size_divisor is not None else self.size_divisor
						SCREAMING_SNAKE_CASE__													= resample if resample is not None else self.resample

						if do_resize and size_divisor is None:
											raise ValueError('size_divisor is required for resizing'							)

						SCREAMING_SNAKE_CASE__													= make_list_of_images(UpperCAmelCase_							)

						if not valid_images(UpperCAmelCase_							):
											raise ValueError('Invalid image(s)'							)

						# All transformations expect numpy arrays.
						SCREAMING_SNAKE_CASE__													= [to_numpy_array(UpperCAmelCase_							) for img in images]

						if do_resize:
											SCREAMING_SNAKE_CASE__													= [self.resize(UpperCAmelCase_       ,   size_divisor=UpperCAmelCase_       ,   resample=UpperCAmelCase_							) for image in images]

						if do_rescale:
											SCREAMING_SNAKE_CASE__													= [self.rescale(UpperCAmelCase_       ,   scale=1 / 255							) for image in images]

						SCREAMING_SNAKE_CASE__													= [to_channel_dimension_format(UpperCAmelCase_       ,   UpperCAmelCase_							) for image in images]

						SCREAMING_SNAKE_CASE__													= {'pixel_values': images}
						return BatchFeature(data=UpperCAmelCase_       ,   tensor_type=UpperCAmelCase_							)





