
"""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


SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)



class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					List[str]   =			['''pixel_values''']






       def __init__(  self :							Tuple       ,      UpperCAmelCase_ :							bool = True       ,      UpperCAmelCase_ :							Dict[str, int] = None       ,      UpperCAmelCase_ :							PILImageResampling = PIL.Image.BICUBIC       ,      UpperCAmelCase_ :							bool = True       ,      UpperCAmelCase_ :							Dict[str, int] = None       ,      UpperCAmelCase_ :							Union[int, float] = 1 / 255       ,      UpperCAmelCase_ :							bool = True       ,      UpperCAmelCase_ :							bool = True       ,      UpperCAmelCase_ :							Optional[Union[float, List[float]]] = None       ,      UpperCAmelCase_ :							Optional[Union[float, List[float]]] = None       ,      **UpperCAmelCase_ :							List[str]       ,      )->  None:







              """simple docstring"""
              super().__init__(**UpperCAmelCase_			)
              UpperCamelCase					 =		size if size is not None else {"height": 256, "width": 256}
              UpperCamelCase					 =		get_size_dict(UpperCAmelCase_			)
              UpperCamelCase					 =		crop_size if crop_size is not None else {"height": 224, "width": 224}
              UpperCamelCase					 =		get_size_dict(UpperCAmelCase_       ,      param_name="crop_size"			)

              UpperCamelCase					 =		do_resize
              UpperCamelCase					 =		size
              UpperCamelCase					 =		resample
              UpperCamelCase					 =		do_center_crop
              UpperCamelCase					 =		crop_size
              UpperCamelCase					 =		do_rescale
              UpperCamelCase					 =		rescale_factor
              UpperCamelCase					 =		do_normalize
              UpperCamelCase					 =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
              UpperCamelCase					 =		image_std if image_std is not None else IMAGENET_STANDARD_STD






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple       ,      UpperCAmelCase_ :							np.ndarray       ,      UpperCAmelCase_ :							Dict[str, int]       ,      UpperCAmelCase_ :							PILImageResampling = PIL.Image.BICUBIC       ,      UpperCAmelCase_ :							Optional[Union[str, ChannelDimension]] = None       ,      **UpperCAmelCase_ :							str       ,      )->  np.ndarray:







              """simple docstring"""
              UpperCamelCase					 =		get_size_dict(UpperCAmelCase_			)
              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(
                  UpperCAmelCase_       ,      size=(size["height"], size["width"])       ,      resample=UpperCAmelCase_       ,      data_format=UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple       ,      UpperCAmelCase_ :							np.ndarray       ,      UpperCAmelCase_ :							Dict[str, int]       ,      UpperCAmelCase_ :							Optional[Union[str, ChannelDimension]] = None       ,      **UpperCAmelCase_ :							Tuple       ,      )->  np.ndarray:







              """simple docstring"""
              UpperCamelCase					 =		get_size_dict(UpperCAmelCase_			)
              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(UpperCAmelCase_       ,      size=(size["height"], size["width"])       ,      data_format=UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]       ,      UpperCAmelCase_ :							np.ndarray       ,      UpperCAmelCase_ :							Union[int, float]       ,      UpperCAmelCase_ :							Optional[Union[str, ChannelDimension]] = None       ,      **UpperCAmelCase_ :							List[str]       ,      )->  str:







              """simple docstring"""
              return rescale(UpperCAmelCase_       ,      scale=UpperCAmelCase_       ,      data_format=UpperCAmelCase_       ,      **UpperCAmelCase_			)






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







              """simple docstring"""
              return normalize(UpperCAmelCase_       ,      mean=UpperCAmelCase_       ,      std=UpperCAmelCase_       ,      data_format=UpperCAmelCase_       ,      **UpperCAmelCase_			)






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







              """simple docstring"""
              UpperCamelCase					 =		do_resize if do_resize is not None else self.do_resize
              UpperCamelCase					 =		resample if resample is not None else self.resample
              UpperCamelCase					 =		do_center_crop if do_center_crop is not None else self.do_center_crop
              UpperCamelCase					 =		do_rescale if do_rescale is not None else self.do_rescale
              UpperCamelCase					 =		rescale_factor if rescale_factor is not None else self.rescale_factor
              UpperCamelCase					 =		do_normalize if do_normalize is not None else self.do_normalize
              UpperCamelCase					 =		image_mean if image_mean is not None else self.image_mean
              UpperCamelCase					 =		image_std if image_std is not None else self.image_std

              UpperCamelCase					 =		size if size is not None else self.size
              UpperCamelCase					 =		get_size_dict(UpperCAmelCase_			)
              UpperCamelCase					 =		crop_size if crop_size is not None else self.crop_size
              UpperCamelCase					 =		get_size_dict(UpperCAmelCase_       ,      param_name="crop_size"			)

              UpperCamelCase					 =		make_list_of_images(UpperCAmelCase_			)

              if not valid_images(UpperCAmelCase_			):
                     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.
              UpperCamelCase					 =		[to_numpy_array(UpperCAmelCase_			) for image in images]

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

              if do_center_crop:
                     UpperCamelCase					 =		[self.center_crop(image=UpperCAmelCase_       ,      size=UpperCAmelCase_			) for image in images]

              if do_rescale:
                     UpperCamelCase					 =		[self.rescale(image=UpperCAmelCase_       ,      scale=UpperCAmelCase_			) for image in images]

              if do_normalize:
                     UpperCamelCase					 =		[self.normalize(image=UpperCAmelCase_       ,      mean=UpperCAmelCase_       ,      std=UpperCAmelCase_			) for image in images]

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

              UpperCamelCase					 =		{"pixel_values": images}
              return BatchFeature(data=UpperCAmelCase_       ,      tensor_type=UpperCAmelCase_			)







"""simple docstring"""


import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


SCREAMING_SNAKE_CASE      =     4
SCREAMING_SNAKE_CASE      =     3



class   __a  (      _lowerCAmelCase	):
       pass





def 	lowerCamelCase__			(							UpperCAmelCase_				)->							List[str]:







       """simple docstring"""
       for shard in shards:
              for i in range(UpperCAmelCase_				):
                     yield {"i": i, "shard": shard}



def 	lowerCamelCase__			(							)->							Union[str, Any]:







       """simple docstring"""
       UpperCamelCase					 =		int(os.environ["RANK"]				)
       UpperCamelCase					 =		int(os.environ["WORLD_SIZE"]				)

       UpperCamelCase					 =		ArgumentParser()
       parser.add_argument("--streaming"		,      type=UpperCAmelCase_				)
       parser.add_argument("--local_rank"		,      type=UpperCAmelCase_				)
       parser.add_argument("--num_workers"		,      type=UpperCAmelCase_		,      default=0				)
       UpperCamelCase					 =		parser.parse_args()
       UpperCamelCase					 =		args.streaming
       UpperCamelCase					 =		args.num_workers

       UpperCamelCase					 =		{"shards": [F"shard_{shard_idx}" for shard_idx in range(UpperCAmelCase_				)]}
       UpperCamelCase					 =		IterableDataset.from_generator(UpperCAmelCase_		,      gen_kwargs=UpperCAmelCase_				)
       if not streaming:
              UpperCamelCase					 =		Dataset.from_list(list(UpperCAmelCase_				)				)

       UpperCamelCase					 =		split_dataset_by_node(UpperCAmelCase_		,      rank=UpperCAmelCase_		,      world_size=UpperCAmelCase_				)
       UpperCamelCase					 =		torch.utils.data.DataLoader(UpperCAmelCase_		,      num_workers=UpperCAmelCase_				)

       UpperCamelCase					 =		NUM_SHARDS * NUM_ITEMS_PER_SHARD
       UpperCamelCase					 =		full_size // world_size
       expected_local_size += int(rank < (full_size % world_size)				)

       UpperCamelCase					 =		sum(1 for _ in dataloader				)
       if local_size != expected_local_size:
              raise FailedTestError(F"local_size {local_size} != expected_local_size {expected_local_size}"				)


if __name__ == "__main__":
      main()





