





"""simple docstring"""





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


_UpperCamelCase					      =   logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							int									=		["""pixel_values"""]




				def __init__(  self	:Dict    ,     __lowercase	:bool = True    ,     __lowercase	:int = 32    ,     __lowercase	:List[Any]=PILImageResampling.BILINEAR    ,     __lowercase	:bool = True    ,     **__lowercase	:List[str]    ,     ):
											__lowerCamelCase   :   str       =do_resize
											__lowerCamelCase   :   int       =do_rescale
											__lowerCamelCase   :   str       =size_divisor
											__lowerCamelCase   :   Union[str, Any]       =resample
											super().__init__(**__lowercase      )




				def        __lowercase	(  self	:Union[str, Any]    ,     __lowercase	:np.ndarray    ,     __lowercase	:int    ,     __lowercase	:int    ,     __lowercase	:Optional[ChannelDimension] = None    ,     **__lowercase	:Dict      ):
											__lowerCamelCase     ,    __lowerCamelCase   :   Optional[int]       =get_image_size(__lowercase      )
											# Rounds the height and width down to the closest multiple of size_divisor
											__lowerCamelCase   :   Optional[Any]       =height // size_divisor * size_divisor
											__lowerCamelCase   :   Dict       =width // size_divisor * size_divisor
											__lowerCamelCase   :   Any       =resize(__lowercase    ,     (new_h, new_w)    ,     resample=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )
											return image




				def        __lowercase	(  self	:List[str]    ,     __lowercase	:np.ndarray    ,     __lowercase	:float    ,     __lowercase	:Optional[ChannelDimension] = None    ,     **__lowercase	:Tuple      ):
											return rescale(image=__lowercase    ,     scale=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )




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

											__lowerCamelCase   :   Union[str, Any]       =make_list_of_images(__lowercase      )

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

											# All transformations expect numpy arrays.
											__lowerCamelCase   :   Dict       =[to_numpy_array(__lowercase      ) for img in images]

											if do_resize:
																		__lowerCamelCase   :   Optional[int]       =[self.resize(__lowercase    ,     size_divisor=__lowercase    ,     resample=__lowercase      ) for image in images]

											if do_rescale:
																		__lowerCamelCase   :   Optional[Any]       =[self.rescale(__lowercase    ,     scale=1 / 255      ) for image in images]

											__lowerCamelCase   :   int       =[to_channel_dimension_format(__lowercase    ,     __lowercase      ) for image in images]

											__lowerCamelCase   :   Any       ={'''pixel_values''': images}
											return BatchFeature(data=__lowercase    ,     tensor_type=__lowercase      )











"""simple docstring"""





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				def        __lowercase	(  self	:Dict      ):
											__lowerCamelCase   :   Union[str, Any]       =inspect.getfile(accelerate.test_utils      )
											__lowerCamelCase   :   Union[str, Any]       =os.path.sep.join(
											    mod_file.split(os.path.sep      )[:-1] + ['''scripts''', '''external_deps''', '''test_metrics.py''']      )

											from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

											__lowerCamelCase   :   List[str]       =test_metrics




				@require_cpu
				def        __lowercase	(  self	:Optional[Any]      ):
											debug_launcher(self.test_metrics.main    ,     num_processes=1      )




				@require_cpu
				def        __lowercase	(  self	:int      ):
											debug_launcher(self.test_metrics.main      )




				@require_single_gpu
				def        __lowercase	(  self	:int      ):
											self.test_metrics.main()




				@require_multi_gpu
				def        __lowercase	(  self	:Dict      ):
											print(f'Found {torch.cuda.device_count()} devices.'      )
											__lowerCamelCase   :   Union[str, Any]       =['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
											with patch_environment(omp_num_threads=1      ):
																		execute_subprocess_async(__lowercase    ,     env=os.environ.copy()      )




