


'''simple docstring'''







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 center_crop, 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, is_vision_available, logging


if is_vision_available():
   import PIL


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







class A__	(						UpperCAmelCase__				):
      __UpperCamelCase	:  List[str]     	=							["pixel_values"]






      def __init__(			self      :List[str]					,						SCREAMING_SNAKE_CASE      :bool = True					,						SCREAMING_SNAKE_CASE      :Dict[str, int] = None					,						SCREAMING_SNAKE_CASE      :PILImageResampling = PIL.Image.NEAREST					,						SCREAMING_SNAKE_CASE      :bool = False					,						SCREAMING_SNAKE_CASE      :Dict[str, int] = None					,						SCREAMING_SNAKE_CASE      :Union[int, float] = 1 / 2_5_5					,						SCREAMING_SNAKE_CASE      :bool = False					,						SCREAMING_SNAKE_CASE      :bool = True					,						SCREAMING_SNAKE_CASE      :bool = True					,						SCREAMING_SNAKE_CASE      :Optional[Union[float, List[float]]] = None					,						SCREAMING_SNAKE_CASE      :Optional[Union[float, List[float]]] = None					,						SCREAMING_SNAKE_CASE      :bool = True					,						**SCREAMING_SNAKE_CASE      :int					,						)	-> None:




             '''simple docstring'''
             super().__init__(**SCREAMING_SNAKE_CASE     )
             _a   :			Union[str, Any]					 =size if size is not None else {"""height""": 3_4_6, """width""": 3_4_6}
             _a   :			Tuple					 =get_size_dict(SCREAMING_SNAKE_CASE     )
             _a   :			Dict					 =crop_size if crop_size is not None else {"""height""": 2_8_9, """width""": 2_8_9}
             _a   :			List[str]					 =get_size_dict(SCREAMING_SNAKE_CASE					,						param_name="""crop_size"""     )

             _a   :			Optional[Any]					 =do_resize
             _a   :			Tuple					 =size
             _a   :			Optional[int]					 =resample
             _a   :			Optional[Any]					 =do_center_crop
             _a   :			int					 =crop_size
             _a   :			Dict					 =do_rescale
             _a   :			str					 =rescale_factor
             _a   :			Optional[int]					 =rescale_offset
             _a   :			Union[str, Any]					 =do_normalize
             _a   :			Union[str, Any]					 =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             _a   :			str					 =image_std if image_std is not None else IMAGENET_STANDARD_STD
             _a   :			Tuple					 =include_top






      def __UpperCAmelCase	(			self      :Optional[int]					,						SCREAMING_SNAKE_CASE      :np.ndarray					,						SCREAMING_SNAKE_CASE      :Dict[str, int]					,						SCREAMING_SNAKE_CASE      :PILImageResampling = PIL.Image.NEAREST					,						SCREAMING_SNAKE_CASE      :Optional[Union[str, ChannelDimension]] = None					,						**SCREAMING_SNAKE_CASE      :int					,						)	-> np.ndarray:




             '''simple docstring'''
             _a   :			Tuple					 =get_size_dict(SCREAMING_SNAKE_CASE     )
             if "height" not in size or "width" not in size:
                    raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"     )
             return resize(
                 SCREAMING_SNAKE_CASE					,						size=(size["""height"""], size["""width"""])					,						resample=SCREAMING_SNAKE_CASE					,						data_format=SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :List[Any]					,						SCREAMING_SNAKE_CASE      :np.ndarray					,						SCREAMING_SNAKE_CASE      :Dict[str, int]					,						SCREAMING_SNAKE_CASE      :Optional[Union[str, ChannelDimension]] = None					,						**SCREAMING_SNAKE_CASE      :List[Any]					,						)	-> np.ndarray:




             '''simple docstring'''
             _a   :			Dict					 =get_size_dict(SCREAMING_SNAKE_CASE     )
             if "height" not in size or "width" not in size:
                    raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"     )
             return center_crop(SCREAMING_SNAKE_CASE					,						size=(size["""height"""], size["""width"""])					,						data_format=SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :np.ndarray					,						SCREAMING_SNAKE_CASE      :Union[int, float]					,						SCREAMING_SNAKE_CASE      :bool = True					,						SCREAMING_SNAKE_CASE      :Optional[Union[str, ChannelDimension]] = None					,						**SCREAMING_SNAKE_CASE      :Optional[Any]					,						)	-> Dict:




             '''simple docstring'''
             if offset:
                    _a   :			Union[str, Any]					 =(image - 127.5) * scale
                    if data_format is not None:
                           _a   :			Tuple					 =to_channel_dimension_format(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )
                    _a   :			List[str]					 =rescaled_image.astype(np.floataa     )
             else:
                    _a   :			Tuple					 =rescale(SCREAMING_SNAKE_CASE					,						scale=SCREAMING_SNAKE_CASE					,						data_format=SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )
             return rescaled_image






      def __UpperCAmelCase	(			self      :Optional[Any]					,						SCREAMING_SNAKE_CASE      :np.ndarray					,						SCREAMING_SNAKE_CASE      :Union[float, List[float]]					,						SCREAMING_SNAKE_CASE      :Union[float, List[float]]					,						SCREAMING_SNAKE_CASE      :Optional[Union[str, ChannelDimension]] = None					,						**SCREAMING_SNAKE_CASE      :str					,						)	-> np.ndarray:




             '''simple docstring'''
             return normalize(SCREAMING_SNAKE_CASE					,						mean=SCREAMING_SNAKE_CASE					,						std=SCREAMING_SNAKE_CASE					,						data_format=SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :Any					,						SCREAMING_SNAKE_CASE      :ImageInput					,						SCREAMING_SNAKE_CASE      :bool = None					,						SCREAMING_SNAKE_CASE      :Dict[str, int] = None					,						SCREAMING_SNAKE_CASE      :Optional[int]=None					,						SCREAMING_SNAKE_CASE      :bool = None					,						SCREAMING_SNAKE_CASE      :Dict[str, int] = None					,						SCREAMING_SNAKE_CASE      :bool = None					,						SCREAMING_SNAKE_CASE      :float = None					,						SCREAMING_SNAKE_CASE      :bool = None					,						SCREAMING_SNAKE_CASE      :bool = None					,						SCREAMING_SNAKE_CASE      :Optional[Union[float, List[float]]] = None					,						SCREAMING_SNAKE_CASE      :Optional[Union[float, List[float]]] = None					,						SCREAMING_SNAKE_CASE      :bool = None					,						SCREAMING_SNAKE_CASE      :Optional[Union[str, TensorType]] = None					,						SCREAMING_SNAKE_CASE      :ChannelDimension = ChannelDimension.FIRST					,						**SCREAMING_SNAKE_CASE      :int					,						)	-> PIL.Image.Image:




             '''simple docstring'''
             _a   :			int					 =do_resize if do_resize is not None else self.do_resize
             _a   :			int					 =resample if resample is not None else self.resample
             _a   :			Optional[Any]					 =do_center_crop if do_center_crop is not None else self.do_center_crop
             _a   :			Optional[Any]					 =do_rescale if do_rescale is not None else self.do_rescale
             _a   :			Union[str, Any]					 =rescale_factor if rescale_factor is not None else self.rescale_factor
             _a   :			List[str]					 =rescale_offset if rescale_offset is not None else self.rescale_offset
             _a   :			Union[str, Any]					 =do_normalize if do_normalize is not None else self.do_normalize
             _a   :			str					 =image_mean if image_mean is not None else self.image_mean
             _a   :			Dict					 =image_std if image_std is not None else self.image_std
             _a   :			List[str]					 =include_top if include_top is not None else self.include_top

             _a   :			Optional[int]					 =size if size is not None else self.size
             _a   :			Optional[Any]					 =get_size_dict(SCREAMING_SNAKE_CASE     )
             _a   :			Union[str, Any]					 =crop_size if crop_size is not None else self.crop_size
             _a   :			Tuple					 =get_size_dict(SCREAMING_SNAKE_CASE					,						param_name="""crop_size"""     )

             _a   :			str					 =make_list_of_images(SCREAMING_SNAKE_CASE     )

             if not valid_images(SCREAMING_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_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.
             _a   :			str					 =[to_numpy_array(SCREAMING_SNAKE_CASE     ) for image in images]

             if do_resize:
                    _a   :			List[str]					 =[self.resize(image=SCREAMING_SNAKE_CASE					,						size=SCREAMING_SNAKE_CASE					,						resample=SCREAMING_SNAKE_CASE     ) for image in images]

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

             if do_rescale:
                    _a   :			List[Any]					 =[self.rescale(image=SCREAMING_SNAKE_CASE					,						scale=SCREAMING_SNAKE_CASE					,						offset=SCREAMING_SNAKE_CASE     ) for image in images]

             if do_normalize:
                    _a   :			Optional[Any]					 =[self.normalize(image=SCREAMING_SNAKE_CASE					,						mean=SCREAMING_SNAKE_CASE					,						std=SCREAMING_SNAKE_CASE     ) for image in images]

             if include_top:
                    _a   :			Optional[int]					 =[self.normalize(image=SCREAMING_SNAKE_CASE					,						mean=[0, 0, 0]					,						std=SCREAMING_SNAKE_CASE     ) for image in images]

             _a   :			str					 =[to_channel_dimension_format(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     ) for image in images]

             _a   :			List[str]					 ={"""pixel_values""": images}
             return BatchFeature(data=SCREAMING_SNAKE_CASE					,						tensor_type=SCREAMING_SNAKE_CASE     )






'''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_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment







class A__	(						unittest.TestCase				):






      def __UpperCAmelCase	(			self      :List[Any]     )	-> List[str]:




             '''simple docstring'''
             _a   :			Union[str, Any]					 =inspect.getfile(accelerate.test_utils     )
             _a   :			Any					 =os.path.sep.join(mod_file.split(os.path.sep     )[:-1] + ["""scripts""", """test_sync.py"""]     )






      @require_cpu
      def __UpperCAmelCase	(			self      :List[str]     )	-> Optional[Any]:




             '''simple docstring'''
             debug_launcher(test_sync.main					,						num_processes=1     )






      @require_cpu
      def __UpperCAmelCase	(			self      :int     )	-> Union[str, Any]:




             '''simple docstring'''
             debug_launcher(test_sync.main     )






      @require_single_gpu
      def __UpperCAmelCase	(			self      :Any     )	-> Optional[int]:




             '''simple docstring'''
             test_sync.main()






      @require_multi_gpu
      def __UpperCAmelCase	(			self      :Optional[Any]     )	-> int:




             '''simple docstring'''
             print(f"Found {torch.cuda.device_count()} devices."     )
             _a   :			int					 =["""torchrun""", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
             with patch_environment(omp_num_threads=1     ):
                    execute_subprocess_async(SCREAMING_SNAKE_CASE					,						env=os.environ.copy()     )


