





import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class 		_lowerCAmelCase(		unittest.TestCase):

  """simple docstring"""







  @property
  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Tuple:
     __A    =  1
     __A    =  3
     __A    =  8
     __A    =  8

     __A    =  torch.rand((batch_size, num_channels, height, width)      )

     return sample

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   int:






     if isinstance(image[0]    ,							PIL.Image.Image      ):
        return np.stack([np.array(lowercase_      ) for i in image]    ,							axis=0      )
     elif isinstance(lowercase_    ,							torch.Tensor      ):
        return image.cpu().numpy().transpose(0    ,							2    ,							3    ,							1      )
     return image

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   int:
     __A    =  VaeImageProcessor(do_resize=lowercase_    ,							do_normalize=lowercase_      )

     __A    =  self.dummy_sample
     __A    =  self.to_np(lowercase_      )

     for output_type in ["pt", "np", "pil"]:
        __A    =  image_processor.postprocess(
            image_processor.preprocess(lowercase_      )    ,							output_type=lowercase_    ,							)
        __A    =  self.to_np(lowercase_      )
        __A    =  (input_np * 2_55).round() if output_type == '''pil''' else input_np
        assert (
            np.abs(in_np - out_np      ).max() < 1e-6
        ), f"decoded output does not match input for output_type {output_type}"

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   List[Any]:
     __A    =  VaeImageProcessor(do_resize=lowercase_    ,							do_normalize=lowercase_      )
     __A    =  self.dummy_sample.cpu().numpy().transpose(0    ,							2    ,							3    ,							1      )

     for output_type in ["pt", "np", "pil"]:
        __A    =  image_processor.postprocess(image_processor.preprocess(lowercase_      )    ,							output_type=lowercase_      )

        __A    =  self.to_np(lowercase_      )
        __A    =  (input_np * 2_55).round() if output_type == '''pil''' else input_np
        assert (
            np.abs(in_np - out_np      ).max() < 1e-6
        ), f"decoded output does not match input for output_type {output_type}"

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Optional[Any]:
     __A    =  VaeImageProcessor(do_resize=lowercase_    ,							do_normalize=lowercase_      )

     __A    =  self.dummy_sample.cpu().numpy().transpose(0    ,							2    ,							3    ,							1      )
     __A    =  image_processor.numpy_to_pil(lowercase_      )

     for output_type in ["pt", "np", "pil"]:
        __A    =  image_processor.postprocess(image_processor.preprocess(lowercase_      )    ,							output_type=lowercase_      )
        for i, o in zip(lowercase_    ,							lowercase_      ):
           __A    =  np.array(lowercase_      )
           __A    =  self.to_np(lowercase_      ) if output_type == '''pil''' else (self.to_np(lowercase_      ) * 2_55).round()
           assert (
               np.abs(in_np - out_np      ).max() < 1e-6
           ), f"decoded output does not match input for output_type {output_type}"

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Union[str, Any]:
     __A    =  VaeImageProcessor(do_resize=lowercase_    ,							do_normalize=lowercase_      )

     __A    =  self.dummy_sample
     __A    =  input_pt_ad.squeeze(0      )

     __A    =  image_processor.postprocess(
         image_processor.preprocess(lowercase_      )    ,							output_type='''np'''    ,							)
     __A    =  image_processor.postprocess(
         image_processor.preprocess(lowercase_      )    ,							output_type='''np'''    ,							)

     __A    =  self.to_np(self.dummy_sample      )
     __A    =  input_np_ad.squeeze(0      )

     __A    =  image_processor.postprocess(
         image_processor.preprocess(lowercase_      )    ,							output_type='''np'''    ,							)
     __A    =  image_processor.postprocess(
         image_processor.preprocess(lowercase_      )    ,							output_type='''np'''    ,							)

     assert np.abs(out_pt_ad - out_pt_ad      ).max() < 1e-6
     assert np.abs(out_np_ad - out_np_ad      ).max() < 1e-6





  def 						SCREAMING_SNAKE_CASE__			(   self      )->   List[Any]:
     __A    =  VaeImageProcessor(do_resize=lowercase_    ,							do_normalize=lowercase_      )

     __A    =  self.dummy_sample
     __A    =  list(lowercase_      )

     __A    =  image_processor.postprocess(
         image_processor.preprocess(lowercase_      )    ,							output_type='''np'''    ,							)

     __A    =  image_processor.postprocess(
         image_processor.preprocess(lowercase_      )    ,							output_type='''np'''    ,							)

     __A    =  self.to_np(self.dummy_sample      )
     list(lowercase_      )

     __A    =  image_processor.postprocess(
         image_processor.preprocess(lowercase_      )    ,							output_type='''np'''    ,							)

     __A    =  image_processor.postprocess(
         image_processor.preprocess(lowercase_      )    ,							output_type='''np'''    ,							)

     assert np.abs(out_pt_ad - out_pt_list      ).max() < 1e-6
     assert np.abs(out_np_ad - out_np_list      ).max() < 1e-6




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      UpperCAmelCase__							(     ):





  '''simple docstring'''







  a__    =Dataset.from_dict({'''i''': range(17				)}				)
  a__    =len(_A				)
  a__    =3
  a__    =[
      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      UpperCAmelCase__							(     ):





  '''simple docstring'''







  def gen():
    return ({"i": i} for i in range(17				))

  a__    =3
  a__    =IterableDataset.from_generator(_A				)
  a__    =len(list(_A				)				)
  a__    =[
      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      UpperCAmelCase__							(     _A :					Dict				):





  '''simple docstring'''







  def gen(_A :					List[str]				):
    for shard in shards:
      yield from ({"i": i, "shard": shard} for i in range(17				))

  a__    =3
  a__    =shards_per_node * world_size
  a__    ={'''shards''': [F"""shard_{shard_idx}.txt""" for shard_idx in range(_A				)]}
  a__    =IterableDataset.from_generator(_A      , gen_kwargs=_A				)
  a__    =len(list(_A				)				)
  assert full_ds.n_shards == world_size * shards_per_node
  a__    =[
      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      UpperCAmelCase__							(     ):





  '''simple docstring'''







  def gen():
    return ({"i": i} for i in range(17				))

  a__    =2
  a__    =IterableDataset.from_generator(_A				)
  a__    =len(list(_A				)				)

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

  a__    =split_dataset_by_node(full_ds.shuffle(seed=42				)      , 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      UpperCAmelCase__							(     _A :					Dict				):





  '''simple docstring'''







  a__    =2
  a__    =get_torch_dist_unique_port()
  a__    =Path(_A				).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
  a__    =F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
  a__    =F"""
        --streaming={streaming}
    """.split()
  a__    =[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      UpperCAmelCase__							(     _A :					List[str]      , _A :					str				):





  '''simple docstring'''







  a__    =True
  a__    =get_torch_dist_unique_port()
  a__    =Path(_A				).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
  a__    =F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
  a__    =F"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
  a__    =[sys.executable] + distributed_args + args
  execute_subprocess_async(_A      , env=os.environ.copy()				)
