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,
    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, is_vision_available, logging


if is_vision_available():
   import PIL


__lowerCamelCase      :			int							       =		logging.get_logger(__name__)







class __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_														=							["pixel_values"]




   def __init__(   self      :    int	,     _lowercase      :    bool = True	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    int = 0.9	,     _lowercase      :    PILImageResampling = PILImageResampling.BICUBIC	,     _lowercase      :    bool = True	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    Union[int, float] = 1 / 2_55	,     _lowercase      :    bool = True	,     _lowercase      :    bool = True	,     _lowercase      :    Optional[Union[float, List[float]]] = None	,     _lowercase      :    Optional[Union[float, List[float]]] = None	,     **_lowercase      :    List[Any]	,     ):



     """simple docstring"""






     super().__init__(**_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				size if size is not None else {"""shortest_edge""": 2_24}
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     default_to_square=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     param_name="""crop_size"""				)

     SCREAMING_SNAKE_CASE__      							=				do_resize
     SCREAMING_SNAKE_CASE__      							=				size
     SCREAMING_SNAKE_CASE__      							=				crop_pct
     SCREAMING_SNAKE_CASE__      							=				resample
     SCREAMING_SNAKE_CASE__      							=				do_center_crop
     SCREAMING_SNAKE_CASE__      							=				crop_size
     SCREAMING_SNAKE_CASE__      							=				do_rescale
     SCREAMING_SNAKE_CASE__      							=				rescale_factor
     SCREAMING_SNAKE_CASE__      							=				do_normalize
     SCREAMING_SNAKE_CASE__      							=				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
     SCREAMING_SNAKE_CASE__      							=				image_std if image_std is not None else IMAGENET_DEFAULT_STD




   def      __a						(   self      :    List[Any]	,     _lowercase      :    np.ndarray	,     _lowercase      :    Dict[str, int]	,     _lowercase      :    Optional[float] = None	,     _lowercase      :    PILImageResampling = PILImageResampling.BICUBIC	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    Dict	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     default_to_square=_lowercase				)
     if "shortest_edge" not in size and ("height" not in size or "width" not in size):
       raise ValueError(f"""size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""				)
     if crop_pct is not None:
       if "shortest_edge" in size:
         SCREAMING_SNAKE_CASE__      							=				int(size["""shortest_edge"""] / crop_pct				)
       elif "height" in size and "width" in size:
         if size["height"] == size["width"]:
           SCREAMING_SNAKE_CASE__      							=				int(size["""height"""] / crop_pct				)
         else:
           SCREAMING_SNAKE_CASE__      							=				(int(size["""height"""] / crop_pct				), int(size["""width"""] / crop_pct				))
       else:
         raise ValueError("""Invalid size for resize: {}""".format(_lowercase				)				)

       SCREAMING_SNAKE_CASE__      							=				get_resize_output_image_size(_lowercase	,     size=_lowercase	,     default_to_square=_lowercase				)
     else:
       if "shortest_edge" in size:
         SCREAMING_SNAKE_CASE__      							=				get_resize_output_image_size(_lowercase	,     size=size["""shortest_edge"""]	,     default_to_square=_lowercase				)
       elif "height" in size and "width" in size:
         SCREAMING_SNAKE_CASE__      							=				(size["""height"""], size["""width"""])
       else:
         raise ValueError("""Invalid size for resize: {}""".format(_lowercase				)				)

     return resize(_lowercase	,     size=_lowercase	,     resample=_lowercase	,     data_format=_lowercase	,     **_lowercase				)




   def      __a						(   self      :    Tuple	,     _lowercase      :    np.ndarray	,     _lowercase      :    Dict[str, int]	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    Optional[Any]	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase				)
     if "height" not in size or "width" not in size:
       raise ValueError(f"""size must contain 'height' and 'width' as keys. Got {size.keys()}"""				)
     return center_crop(_lowercase	,     size=(size["""height"""], size["""width"""])	,     data_format=_lowercase	,     **_lowercase				)




   def      __a						(   self      :    Tuple	,     _lowercase      :    np.ndarray	,     _lowercase      :    Union[int, float]	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    int	,     ):



     """simple docstring"""






     return rescale(_lowercase	,     scale=_lowercase	,     data_format=_lowercase	,     **_lowercase				)




   def      __a						(   self      :    Dict	,     _lowercase      :    np.ndarray	,     _lowercase      :    Union[float, List[float]]	,     _lowercase      :    Union[float, List[float]]	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    Union[str, Any]	,     ):



     """simple docstring"""






     return normalize(_lowercase	,     mean=_lowercase	,     std=_lowercase	,     data_format=_lowercase	,     **_lowercase				)




   def      __a						(   self      :    Optional[int]	,     _lowercase      :    ImageInput	,     _lowercase      :    bool = None	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    int = None	,     _lowercase      :    PILImageResampling = None	,     _lowercase      :    bool = None	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    bool = None	,     _lowercase      :    float = None	,     _lowercase      :    bool = None	,     _lowercase      :    Optional[Union[float, List[float]]] = None	,     _lowercase      :    Optional[Union[float, List[float]]] = None	,     _lowercase      :    Optional[Union[str, TensorType]] = None	,     _lowercase      :    ChannelDimension = ChannelDimension.FIRST	,     **_lowercase      :    Optional[Any]	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__      							=				crop_pct if crop_pct is not None else self.crop_pct
     SCREAMING_SNAKE_CASE__      							=				resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE__      							=				do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE__      							=				do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__      							=				rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE__      							=				do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE__      							=				image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE__      							=				image_std if image_std is not None else self.image_std

     SCREAMING_SNAKE_CASE__      							=				size if size is not None else self.size
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     default_to_square=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     param_name="""crop_size"""				)

     SCREAMING_SNAKE_CASE__      							=				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 or resample is None:
       raise ValueError("""Size and resample must be specified if do_resize is True."""				)

     if do_center_crop and crop_pct is None:
       raise ValueError("""Crop_pct 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__      							=				[to_numpy_array(_lowercase				) for image in images]

     if do_resize:
       SCREAMING_SNAKE_CASE__      							=				[self.resize(image=_lowercase	,     size=_lowercase	,     crop_pct=_lowercase	,     resample=_lowercase				) for image in images]

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

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

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

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

     SCREAMING_SNAKE_CASE__      							=				{"""pixel_values""": images}
     return BatchFeature(data=_lowercase	,     tensor_type=_lowercase				)

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


__lowerCamelCase      :			int							       =		4
__lowerCamelCase      :			Dict							       =		3







class __snake_case    (						lowerCamelCase_		):
   pass





def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]     )							-> List[str]:







  """simple docstring"""





  for shard in shards:
    for i in range(__UpperCamelCase     ):
      yield {"i": i, "shard": shard}



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







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				int(os.environ["""RANK"""]     )
  SCREAMING_SNAKE_CASE__      							=				int(os.environ["""WORLD_SIZE"""]     )

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

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

  SCREAMING_SNAKE_CASE__      							=				split_dataset_by_node(__UpperCamelCase			,					rank=__UpperCamelCase			,					world_size=__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				torch.utils.data.DataLoader(__UpperCamelCase			,					num_workers=__UpperCamelCase     )

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

  SCREAMING_SNAKE_CASE__      							=				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()
