



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(UpperCAmelCase__       )
 a							=		3
 a							=		[
     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 						UpperCAmelCase__   (   ):






 '''simple docstring'''

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

 a							=		3
 a							=		IterableDataset.from_generator(UpperCAmelCase__       )
 a							=		len(list(UpperCAmelCase__       )       )
 a							=		[
     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 						UpperCAmelCase__   (   UpperCAmelCase__			:Tuple       ):






 '''simple docstring'''

 def gen(UpperCAmelCase__			: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(UpperCAmelCase__       )]}
 a							=		IterableDataset.from_generator(UpperCAmelCase__			,       gen_kwargs=UpperCAmelCase__       )
 a							=		len(list(UpperCAmelCase__       )       )
 assert full_ds.n_shards == world_size * shards_per_node
 a							=		[
     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 						UpperCAmelCase__   (   ):






 '''simple docstring'''

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

 a							=		2
 a							=		IterableDataset.from_generator(UpperCAmelCase__       )
 a							=		len(list(UpperCAmelCase__       )       )

 a							=		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()

 a							=		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 						UpperCAmelCase__   (   UpperCAmelCase__			:int       ):






 '''simple docstring'''

 a							=		2
 a							=		get_torch_dist_unique_port()
 a							=		Path(UpperCAmelCase__       ).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(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 						UpperCAmelCase__   (   UpperCAmelCase__			:List[str]			,       UpperCAmelCase__			:Any       ):






 '''simple docstring'''

 a							=		True
 a							=		get_torch_dist_unique_port()
 a							=		Path(UpperCAmelCase__       ).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(UpperCAmelCase__			,       env=os.environ.copy()       )









import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import SwinaSRImageProcessor
   from transformers.image_transforms import get_image_size


class 		lowerCAmelCase_				(  unittest.TestCase ):





     def __init__(	self							, _UpperCAmelCase							, _UpperCAmelCase=7							, _UpperCAmelCase=3							, _UpperCAmelCase=18							, _UpperCAmelCase=30							, _UpperCAmelCase=400							, _UpperCAmelCase=True							, _UpperCAmelCase=1 / 255							, _UpperCAmelCase=True							, _UpperCAmelCase=8							, )		->				Tuple:
        lowerCamelCase									=parent
        lowerCamelCase									=batch_size
        lowerCamelCase									=num_channels
        lowerCamelCase									=image_size
        lowerCamelCase									=min_resolution
        lowerCamelCase									=max_resolution
        lowerCamelCase									=do_rescale
        lowerCamelCase									=rescale_factor
        lowerCamelCase									=do_pad
        lowerCamelCase									=pad_size





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        return {
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_pad": self.do_pad,
            "pad_size": self.pad_size,
        }





     def 						lowerCamelCase							(	self							, _UpperCAmelCase=False							, _UpperCAmelCase=False							, _UpperCAmelCase=False  )		->				int:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
           lowerCamelCase									=[]
           for i in range(self.batch_size  ):
              image_inputs.append(
                  np.random.randint(
                      255							, size=(self.num_channels, self.max_resolution, self.max_resolution)							, dtype=np.uinta  )  )
        else:
           lowerCamelCase									=[]
           for i in range(self.batch_size  ):
              lowerCamelCase				,       lowerCamelCase									=np.random.choice(np.arange(self.min_resolution							, self.max_resolution  )							, 2  )
              image_inputs.append(np.random.randint(255							, size=(self.num_channels, width, height)							, dtype=np.uinta  )  )

        if not numpify and not torchify:
           # PIL expects the channel dimension as last dimension
           lowerCamelCase									=[Image.fromarray(np.moveaxis(_UpperCAmelCase							, 0							, -1  )  ) for x in image_inputs]

        if torchify:
           lowerCamelCase									=[torch.from_numpy(_UpperCAmelCase  ) for x in image_inputs]

        return image_inputs




@require_torch
@require_vision
class 		lowerCAmelCase_				(  UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    Optional[Any] 	=     SwinaSRImageProcessor if is_vision_available() else None





     def 						lowerCamelCase							(	self  )		->				Dict:
        lowerCamelCase									=SwinaSRImageProcessingTester(self  )





     @property
     def 						lowerCamelCase							(	self  )		->				Any:
        return self.image_processor_tester.prepare_image_processor_dict()





     def 						lowerCamelCase							(	self  )		->				Any:
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_rescale"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """rescale_factor"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_pad"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """pad_size"""  )  )





     def 						lowerCamelCase							(	self  )		->				Any:
        pass





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				int:
        lowerCamelCase				,       lowerCamelCase									=get_image_size(_UpperCAmelCase  )
        lowerCamelCase									=self.image_processor_tester.pad_size

        lowerCamelCase									=(old_height // size + 1) * size - old_height
        lowerCamelCase									=(old_width // size + 1) * size - old_width
        return old_height + pad_height, old_width + pad_width





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, Image.Image  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        lowerCamelCase				,       lowerCamelCase									=self.calculate_expected_size(np.array(image_inputs[0]  )  )
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							, )





     def 						lowerCamelCase							(	self  )		->				Any:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase							, numpify=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, np.ndarray  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        lowerCamelCase				,       lowerCamelCase									=self.calculate_expected_size(image_inputs[0]  )
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							, )





     def 						lowerCamelCase							(	self  )		->				Dict:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase							, torchify=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, torch.Tensor  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        lowerCamelCase				,       lowerCamelCase									=self.calculate_expected_size(image_inputs[0]  )
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							, )