

'''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      __magic_name__							(			)      ->		int:
	snake_case							=					Dataset.from_dict({'i': range(1_7				)}				)
	snake_case							=					len(A				)
	snake_case							=					3
	snake_case							=					[
	    split_dataset_by_node(A       , rank=A       , world_size=A				) for rank in range(A				)
	]
	assert sum(len(A				) 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      __magic_name__							(			)      ->		List[str]:
	def gen():
		return ({"i": i} for i in range(1_7				))

	snake_case							=					3
	snake_case							=					IterableDataset.from_generator(A				)
	snake_case							=					len(list(A				)				)
	snake_case							=					[
	    split_dataset_by_node(A       , rank=A       , world_size=A				) for rank in range(A				)
	]
	assert sum(len(list(A				)				) 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      __magic_name__							(			A				)      ->		Any:
	def gen(A				):
		for shard in shards:
			yield from ({"i": i, "shard": shard} for i in range(1_7				))

	snake_case							=					3
	snake_case							=					shards_per_node * world_size
	snake_case							=					{'shards': [F'''shard_{shard_idx}.txt''' for shard_idx in range(A				)]}
	snake_case							=					IterableDataset.from_generator(A       , gen_kwargs=A				)
	snake_case							=					len(list(A				)				)
	assert full_ds.n_shards == world_size * shards_per_node
	snake_case							=					[
	    split_dataset_by_node(A       , rank=A       , world_size=A				) for rank in range(A				)
	]
	assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
	assert sum(len(list(A				)				) 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      __magic_name__							(			)      ->		Any:
	def gen():
		return ({"i": i} for i in range(1_7				))

	snake_case							=					2
	snake_case							=					IterableDataset.from_generator(A				)
	snake_case							=					len(list(A				)				)

	snake_case							=					split_dataset_by_node(A       , rank=0       , world_size=A				).shuffle(seed=4_2				)
	assert len(list(A				)				) == 1 + full_size // world_size
	with pytest.raises(A				):
		split_dataset_by_node(A       , rank=0       , world_size=A				).shuffle()

	snake_case							=					split_dataset_by_node(full_ds.shuffle(seed=4_2				)       , rank=0       , world_size=A				)
	assert len(list(A				)				) == 1 + full_size // world_size
	with pytest.raises(A				):
		split_dataset_by_node(full_ds.shuffle()       , rank=0       , world_size=A				)

@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      __magic_name__							(			A				)      ->		Tuple:
	snake_case							=					2
	snake_case							=					get_torch_dist_unique_port()
	snake_case							=					Path(A				).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
	snake_case							=					F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
	snake_case							=					F'''
        --streaming={streaming}
    '''.split()
	snake_case							=					[sys.executable] + distributed_args + args
	execute_subprocess_async(A       , 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      __magic_name__							(			A       , A				)      ->		Union[str, Any]:
	snake_case							=					True
	snake_case							=					get_torch_dist_unique_port()
	snake_case							=					Path(A				).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
	snake_case							=					F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
	snake_case							=					F'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
	snake_case							=					[sys.executable] + distributed_args + args
	execute_subprocess_async(A       , env=os.environ.copy()				)







"""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_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


lowerCamelCase__     =						logging.get_logger(__name__)




class 					A__			(      _lowerCamelCase):
		A_		:			List[str]							  =      ['pixel_values']

		def __init__( self			,						_SCREAMING_SNAKE_CASE = True			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = True			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC			,						_SCREAMING_SNAKE_CASE = True			,						_SCREAMING_SNAKE_CASE = 1 / 2_55			,						_SCREAMING_SNAKE_CASE = True			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						**_SCREAMING_SNAKE_CASE			,						):
			super().__init__(**_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		List[str]		     = crop_size if crop_size is not None else {'height': 2_56, 'width': 2_56}
			__lowerCAmelCase     :		int		     = get_size_dict(_SCREAMING_SNAKE_CASE			,						param_name='crop_size'					)
			__lowerCAmelCase     :		List[str]		     = size if size is not None else {'height': 2_24, 'width': 2_24}
			__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 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						**_SCREAMING_SNAKE_CASE			,						):
			__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     ,  __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 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC			,						_SCREAMING_SNAKE_CASE = None			,						**_SCREAMING_SNAKE_CASE			,						):
			__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 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						**_SCREAMING_SNAKE_CASE			,						):
			return rescale(_SCREAMING_SNAKE_CASE			,						scale=_SCREAMING_SNAKE_CASE			,						data_format=_SCREAMING_SNAKE_CASE			,						**_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						**_SCREAMING_SNAKE_CASE			,						):
			return normalize(_SCREAMING_SNAKE_CASE			,						mean=_SCREAMING_SNAKE_CASE			,						std=_SCREAMING_SNAKE_CASE			,						data_format=_SCREAMING_SNAKE_CASE			,						**_SCREAMING_SNAKE_CASE					)







		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST			,						**_SCREAMING_SNAKE_CASE			,						):
			__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					)