




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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


UpperCamelCase__						=		logging.get_logger(__name__)


class 							__SCREAMING_SNAKE_CASE				(						_lowerCamelCase    ):
					_lowerCamelCase						    =					['pixel_values']

					def __init__(							self					,			__lowercase = True					,			__lowercase = None					,			__lowercase = True					,			__lowercase = None					,			__lowercase = PILImageResampling.BICUBIC					,			__lowercase = True					,			__lowercase = 1 / 255					,			__lowercase = True					,			__lowercase = None					,			__lowercase = None					,			**__lowercase					,			):







						'''simple docstring'''




						super().__init__(**_SCREAMING_SNAKE_CASE   )
						lowerCamelCase_				:						List[str]				  =				crop_size if crop_size is not None else {'height': 256, 'width': 256}
						lowerCamelCase_				:						int				  =				get_size_dict(_SCREAMING_SNAKE_CASE					,			param_name='crop_size'   )
						lowerCamelCase_				:						List[str]				  =				size if size is not None else {'height': 224, 'width': 224}
						lowerCamelCase_				:						Optional[int]				  =				get_size_dict(_SCREAMING_SNAKE_CASE   )

						lowerCamelCase_				:						str				  =				do_center_crop
						lowerCamelCase_				:						Optional[int]				  =				crop_size
						lowerCamelCase_				:						Optional[Any]				  =				do_resize
						lowerCamelCase_				:						Optional[int]				  =				size
						lowerCamelCase_				:						List[Any]				  =				resample
						lowerCamelCase_				:						str				  =				do_rescale
						lowerCamelCase_				:						str				  =				rescale_factor
						lowerCamelCase_				:						Union[str, Any]				  =				do_normalize
						lowerCamelCase_				:						str				  =				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
						lowerCamelCase_				:						List[Any]				  =				image_std if image_std is not None else IMAGENET_DEFAULT_STD

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase = None					,			__lowercase = None					,			**__lowercase					,			):







						'''simple docstring'''




						lowerCamelCase_				:						List[str]				  =				self.size if size is None else size
						lowerCamelCase_				:						List[str]				  =				get_size_dict(_SCREAMING_SNAKE_CASE   )
						lowerCamelCase_				:						Optional[Any]				  =				get_size_dict(_SCREAMING_SNAKE_CASE					,			param_name='crop_size'   )

						lowerCamelCase_				:						Optional[int]				  =				get_image_size(_SCREAMING_SNAKE_CASE   )
						lowerCamelCase_				:						Tuple				  =				min(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE   )
						lowerCamelCase_				:						Optional[Any]				  =				(size['height'] / crop_size['height']) * min_dim
						lowerCamelCase_				:						Tuple				  =				(size['width'] / crop_size['width']) * min_dim
						return center_crop(_SCREAMING_SNAKE_CASE					,			size=(cropped_height, cropped_width)					,			data_format=_SCREAMING_SNAKE_CASE					,			**_SCREAMING_SNAKE_CASE   )

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase = PIL.Image.BICUBIC					,			__lowercase = None					,			**__lowercase					,			):







						'''simple docstring'''




						lowerCamelCase_				:						Dict				  =				get_size_dict(_SCREAMING_SNAKE_CASE   )
						if "height" not in size or "width" not in size:
							raise ValueError(f"The size dictionary must contain the 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       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase = None					,			**__lowercase					,			):







						'''simple docstring'''




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

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase = None					,			**__lowercase					,			):







						'''simple docstring'''




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





					def       lowercase_				(							self					,			__lowercase					,			__lowercase = None					,			__lowercase = None					,			__lowercase = None					,			__lowercase = None					,			__lowercase = None					,			__lowercase = None					,			__lowercase = None					,			__lowercase = None					,			__lowercase = None					,			__lowercase = None					,			__lowercase = None					,			__lowercase = ChannelDimension.FIRST					,			**__lowercase					,			):







						'''simple docstring'''




						lowerCamelCase_				:						Tuple				  =				do_center_crop if do_center_crop is not None else self.do_center_crop
						lowerCamelCase_				:						Optional[int]				  =				crop_size if crop_size is not None else self.crop_size
						lowerCamelCase_				:						int				  =				get_size_dict(_SCREAMING_SNAKE_CASE					,			param_name='crop_size'   )
						lowerCamelCase_				:						List[str]				  =				do_resize if do_resize is not None else self.do_resize
						lowerCamelCase_				:						Any				  =				size if size is not None else self.size
						lowerCamelCase_				:						Any				  =				get_size_dict(_SCREAMING_SNAKE_CASE   )
						lowerCamelCase_				:						Any				  =				resample if resample is not None else self.resample
						lowerCamelCase_				:						Union[str, Any]				  =				do_rescale if do_rescale is not None else self.do_rescale
						lowerCamelCase_				:						int				  =				rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCamelCase_				:						List[Any]				  =				do_normalize if do_normalize is not None else self.do_normalize
						lowerCamelCase_				:						Tuple				  =				image_mean if image_mean is not None else self.image_mean
						lowerCamelCase_				:						Union[str, Any]				  =				image_std if image_std is not None else self.image_std

						lowerCamelCase_				:						Tuple				  =				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_center_crop and crop_size is None:
							raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'   )

						if do_resize and size is None:
							raise ValueError('Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True.'   )

						# All transformations expect numpy arrays.
						lowerCamelCase_				:						int				  =				[to_numpy_array(_SCREAMING_SNAKE_CASE   ) for image in images]

						if do_center_crop:
							lowerCamelCase_				:						int				  =				[self.center_crop(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			size=_SCREAMING_SNAKE_CASE   ) for image in images]

						if do_resize:
							lowerCamelCase_				:						Optional[Any]				  =				[self.resize(image=_SCREAMING_SNAKE_CASE					,			size=_SCREAMING_SNAKE_CASE					,			resample=_SCREAMING_SNAKE_CASE   ) for image in images]

						if do_rescale:
							lowerCamelCase_				:						str				  =				[self.rescale(image=_SCREAMING_SNAKE_CASE					,			scale=_SCREAMING_SNAKE_CASE   ) for image in images]

						if do_normalize:
							lowerCamelCase_				:						Optional[int]				  =				[self.normalize(image=_SCREAMING_SNAKE_CASE					,			mean=_SCREAMING_SNAKE_CASE					,			std=_SCREAMING_SNAKE_CASE   ) for image in images]

						lowerCamelCase_				:						Tuple				  =				[to_channel_dimension_format(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE   ) for image in images]

						lowerCamelCase_				:						str				  =				{'pixel_values': images}
						return BatchFeature(data=_SCREAMING_SNAKE_CASE					,			tensor_type=_SCREAMING_SNAKE_CASE   )


"""simple docstring"""







import os
import sys
from pathlib import Path

import pytest

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

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch
def        __lowerCAmelCase							():
	__lowerCAmelCase     :		Optional[int]		     = Dataset.from_dict({'i': range(17						)}						)
	__lowerCAmelCase     :		List[Any]		     = len(_UpperCamelCase						)
	__lowerCAmelCase     :		Tuple		     = 3
	__lowerCAmelCase     :		List[Any]		     = [
	    split_dataset_by_node(_UpperCamelCase			,    rank=_UpperCamelCase			,    world_size=_UpperCamelCase						) for rank in range(_UpperCamelCase						)
	]
	assert sum(len(_UpperCamelCase						) for ds in datasets_per_rank						) == full_size
	assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
def        __lowerCAmelCase							():
	def gen():
		return ({"i": i} for i in range(17						))

	__lowerCAmelCase     :		List[Any]		     = 3
	__lowerCAmelCase     :		Optional[Any]		     = IterableDataset.from_generator(_UpperCamelCase						)
	__lowerCAmelCase     :		List[Any]		     = len(list(_UpperCamelCase						)						)
	__lowerCAmelCase     :		str		     = [
	    split_dataset_by_node(_UpperCamelCase			,    rank=_UpperCamelCase			,    world_size=_UpperCamelCase						) for rank in range(_UpperCamelCase						)
	]
	assert sum(len(list(_UpperCamelCase						)						) for ds in datasets_per_rank						) == full_size
	assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
@pytest.mark.parametrize('shards_per_node'			,    [1, 2, 3]						)
def        __lowerCAmelCase							(_UpperCamelCase						):
	def gen(_UpperCamelCase						):
		for shard in shards:
			yield from ({"i": i, "shard": shard} for i in range(17						))

	__lowerCAmelCase     :		List[Any]		     = 3
	__lowerCAmelCase     :		Optional[int]		     = shards_per_node * world_size
	__lowerCAmelCase     :		Dict		     = {'shards': [F"shard_{shard_idx}.txt" for shard_idx in range(_UpperCamelCase						)]}
	__lowerCAmelCase     :		Dict		     = IterableDataset.from_generator(_UpperCamelCase			,    gen_kwargs=_UpperCamelCase						)
	__lowerCAmelCase     :		str		     = len(list(_UpperCamelCase						)						)
	assert full_ds.n_shards == world_size * shards_per_node
	__lowerCAmelCase     :		Union[str, Any]		     = [
	    split_dataset_by_node(_UpperCamelCase			,    rank=_UpperCamelCase			,    world_size=_UpperCamelCase						) for rank in range(_UpperCamelCase						)
	]
	assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
	assert sum(len(list(_UpperCamelCase						)						) for ds in datasets_per_rank						) == full_size
	assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
def        __lowerCAmelCase							():
	def gen():
		return ({"i": i} for i in range(17						))

	__lowerCAmelCase     :		str		     = 2
	__lowerCAmelCase     :		Any		     = IterableDataset.from_generator(_UpperCamelCase						)
	__lowerCAmelCase     :		Optional[int]		     = len(list(_UpperCamelCase						)						)

	__lowerCAmelCase     :		Dict		     = split_dataset_by_node(_UpperCamelCase			,    rank=0			,    world_size=_UpperCamelCase						).shuffle(seed=42						)
	assert len(list(_UpperCamelCase						)						) == 1 + full_size // world_size
	with pytest.raises(_UpperCamelCase						):
		split_dataset_by_node(_UpperCamelCase			,    rank=0			,    world_size=_UpperCamelCase						).shuffle()

	__lowerCAmelCase     :		List[str]		     = split_dataset_by_node(full_ds.shuffle(seed=42						)			,    rank=0			,    world_size=_UpperCamelCase						)
	assert len(list(_UpperCamelCase						)						) == 1 + full_size // world_size
	with pytest.raises(_UpperCamelCase						):
		split_dataset_by_node(full_ds.shuffle()			,    rank=0			,    world_size=_UpperCamelCase						)
@pytest.mark.parametrize('streaming'			,    [False, True]						)
@require_torch
@pytest.mark.skipif(os.name == 'nt'			,    reason='execute_subprocess_async doesn\'t support windows'						)
@pytest.mark.integration
def        __lowerCAmelCase							(_UpperCamelCase						):
	__lowerCAmelCase     :		Tuple		     = 2
	__lowerCAmelCase     :		Optional[Any]		     = get_torch_dist_unique_port()
	__lowerCAmelCase     :		List[Any]		     = Path(_UpperCamelCase						).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
	__lowerCAmelCase     :		int		     = F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
	__lowerCAmelCase     :		Tuple		     = F"\n        --streaming={streaming}\n    ".split()
	__lowerCAmelCase     :		Optional[Any]		     = [sys.executable] + distributed_args + args
	execute_subprocess_async(_UpperCamelCase			,    env=os.environ.copy()						)
@pytest.mark.parametrize(
    'nproc_per_node, num_workers'			,    [
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]			,    )
@require_torch
@pytest.mark.skipif(os.name == 'nt'			,    reason='execute_subprocess_async doesn\'t support windows'						)
@pytest.mark.integration
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase						):
	__lowerCAmelCase     :		Optional[int]		     = True
	__lowerCAmelCase     :		Optional[Any]		     = get_torch_dist_unique_port()
	__lowerCAmelCase     :		Tuple		     = Path(_UpperCamelCase						).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
	__lowerCAmelCase     :		Optional[Any]		     = F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
	__lowerCAmelCase     :		List[Any]		     = F"\n        --streaming={streaming}\n        --num_workers={num_workers}\n    ".split()
	__lowerCAmelCase     :		List[Any]		     = [sys.executable] + distributed_args + args
	execute_subprocess_async(_UpperCamelCase			,    env=os.environ.copy()						)