


import unittest

import numpy as np
from datasets import load_dataset

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, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import BeitImageProcessor

class 							lowercase  (						unittest.TestCase			):







     def __init__(						self :						Union[str, Any]   ,   _lowercase :						Tuple   ,   _lowercase :						Dict=7   ,   _lowercase :						List[Any]=3   ,   _lowercase :						str=18   ,   _lowercase :						Optional[int]=30   ,   _lowercase :						List[Any]=4_00   ,   _lowercase :						Any=True   ,   _lowercase :						Optional[int]=None   ,   _lowercase :						int=True   ,   _lowercase :						List[str]=None   ,   _lowercase :						int=True   ,   _lowercase :						List[str]=[0.5, 0.5, 0.5]   ,   _lowercase :						Dict=[0.5, 0.5, 0.5]   ,   _lowercase :						Union[str, Any]=False   ,   ):
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		size if size is not None else {'''height''': 20, '''width''': 20}
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
           SCREAMING_SNAKE_CASE__       :  str						     =		parent
           SCREAMING_SNAKE_CASE__       :  Dict						     =		batch_size
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		num_channels
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		image_size
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		min_resolution
           SCREAMING_SNAKE_CASE__       :  Dict						     =		max_resolution
           SCREAMING_SNAKE_CASE__       :  int						     =		do_resize
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		size
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		do_center_crop
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		crop_size
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		do_normalize
           SCREAMING_SNAKE_CASE__       :  str						     =		image_mean
           SCREAMING_SNAKE_CASE__       :  int						     =		image_std
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		do_reduce_labels







     def 							lowercase__       (						self :						Tuple      ):
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_reduce_labels": self.do_reduce_labels,
           }




def    a   (							)	-> Any:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Any						     =		load_dataset('''hf-internal-testing/fixtures_ade20k'''      ,    split='''test'''							)

      SCREAMING_SNAKE_CASE__       :  str						     =		Image.open(dataset[0]['''file''']							)
      SCREAMING_SNAKE_CASE__       :  Dict						     =		Image.open(dataset[1]['''file''']							)

      return image, map




def    a   (							)	-> str:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  str						     =		load_dataset('''hf-internal-testing/fixtures_ade20k'''      ,    split='''test'''							)

      SCREAMING_SNAKE_CASE__       :  Any						     =		Image.open(ds[0]['''file''']							)
      SCREAMING_SNAKE_CASE__       :  Dict						     =		Image.open(ds[1]['''file''']							)
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		Image.open(ds[2]['''file''']							)
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		Image.open(ds[3]['''file''']							)

      return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class 							lowercase  (						_UpperCAmelCase    ,       unittest.TestCase			):
     lowerCamelCase			:			Tuple      = BeitImageProcessor if is_vision_available() else None







     def 							lowercase__       (						self :						List[Any]      ):
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		BeitImageProcessingTester(self      )







     @property
     def 							lowercase__       (						self :						Optional[Any]      ):
           return self.image_processor_tester.prepare_image_processor_dict()







     def 							lowercase__       (						self :						Tuple      ):
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		self.image_processing_class(**self.image_processor_dict      )
           self.assertTrue(hasattr(_lowercase   ,   '''do_resize'''      )      )
           self.assertTrue(hasattr(_lowercase   ,   '''size'''      )      )
           self.assertTrue(hasattr(_lowercase   ,   '''do_center_crop'''      )      )
           self.assertTrue(hasattr(_lowercase   ,   '''center_crop'''      )      )
           self.assertTrue(hasattr(_lowercase   ,   '''do_normalize'''      )      )
           self.assertTrue(hasattr(_lowercase   ,   '''image_mean'''      )      )
           self.assertTrue(hasattr(_lowercase   ,   '''image_std'''      )      )







     def 							lowercase__       (						self :						Optional[Any]      ):
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		self.image_processing_class.from_dict(self.image_processor_dict      )
           self.assertEqual(image_processor.size   ,   {'''height''': 20, '''width''': 20}      )
           self.assertEqual(image_processor.crop_size   ,   {'''height''': 18, '''width''': 18}      )
           self.assertEqual(image_processor.do_reduce_labels   ,   _lowercase      )

           SCREAMING_SNAKE_CASE__       :  Tuple						     =		self.image_processing_class.from_dict(
               self.image_processor_dict   ,   size=42   ,   crop_size=84   ,   reduce_labels=_lowercase      )
           self.assertEqual(image_processor.size   ,   {'''height''': 42, '''width''': 42}      )
           self.assertEqual(image_processor.crop_size   ,   {'''height''': 84, '''width''': 84}      )
           self.assertEqual(image_processor.do_reduce_labels   ,   _lowercase      )







     def 							lowercase__       (						self :						str      ):
           pass







     def 							lowercase__       (						self :						int      ):
           # Initialize image_processing
           SCREAMING_SNAKE_CASE__       :  Any						     =		self.image_processing_class(**self.image_processor_dict      )
           # create random PIL images
           SCREAMING_SNAKE_CASE__       :  str						     =		prepare_image_inputs(self.image_processor_tester   ,   equal_resolution=_lowercase      )
           for image in image_inputs:
                 self.assertIsInstance(_lowercase   ,   Image.Image      )

           # Test not batched input
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		image_processing(image_inputs[0]   ,   return_tensors='''pt'''      ).pixel_values
           self.assertEqual(
               encoded_images.shape   ,   (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )   ,   )

           # Test batched
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		image_processing(_lowercase   ,   return_tensors='''pt'''      ).pixel_values
           self.assertEqual(
               encoded_images.shape   ,   (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )   ,   )







     def 							lowercase__       (						self :						Dict      ):
           # Initialize image_processing
           SCREAMING_SNAKE_CASE__       :  Dict						     =		self.image_processing_class(**self.image_processor_dict      )
           # create random numpy tensors
           SCREAMING_SNAKE_CASE__       :  Any						     =		prepare_image_inputs(self.image_processor_tester   ,   equal_resolution=_lowercase   ,   numpify=_lowercase      )
           for image in image_inputs:
                 self.assertIsInstance(_lowercase   ,   np.ndarray      )

           # Test not batched input
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		image_processing(image_inputs[0]   ,   return_tensors='''pt'''      ).pixel_values
           self.assertEqual(
               encoded_images.shape   ,   (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )   ,   )

           # Test batched
           SCREAMING_SNAKE_CASE__       :  int						     =		image_processing(_lowercase   ,   return_tensors='''pt'''      ).pixel_values
           self.assertEqual(
               encoded_images.shape   ,   (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )   ,   )







     def 							lowercase__       (						self :						Optional[Any]      ):
           # Initialize image_processing
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		self.image_processing_class(**self.image_processor_dict      )
           # create random PyTorch tensors
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		prepare_image_inputs(self.image_processor_tester   ,   equal_resolution=_lowercase   ,   torchify=_lowercase      )
           for image in image_inputs:
                 self.assertIsInstance(_lowercase   ,   torch.Tensor      )

           # Test not batched input
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		image_processing(image_inputs[0]   ,   return_tensors='''pt'''      ).pixel_values
           self.assertEqual(
               encoded_images.shape   ,   (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )   ,   )

           # Test batched
           SCREAMING_SNAKE_CASE__       :  Any						     =		image_processing(_lowercase   ,   return_tensors='''pt'''      ).pixel_values
           self.assertEqual(
               encoded_images.shape   ,   (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )   ,   )







     def 							lowercase__       (						self :						Optional[int]      ):
           # Initialize image_processing
           SCREAMING_SNAKE_CASE__       :  Any						     =		self.image_processing_class(**self.image_processor_dict      )
           # create random PyTorch tensors
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		prepare_image_inputs(self.image_processor_tester   ,   equal_resolution=_lowercase   ,   torchify=_lowercase      )
           SCREAMING_SNAKE_CASE__       :  Any						     =		[]
           for image in image_inputs:
                 self.assertIsInstance(_lowercase   ,   torch.Tensor      )
                 maps.append(torch.zeros(image.shape[-2:]      ).long()      )

           # Test not batched input
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		image_processing(image_inputs[0]   ,   maps[0]   ,   return_tensors='''pt'''      )
           self.assertEqual(
               encoding['''pixel_values'''].shape   ,   (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )   ,   )
           self.assertEqual(
               encoding['''labels'''].shape   ,   (
                   1,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )   ,   )
           self.assertEqual(encoding['''labels'''].dtype   ,   torch.long      )
           self.assertTrue(encoding['''labels'''].min().item() >= 0      )
           self.assertTrue(encoding['''labels'''].max().item() <= 2_55      )

           # Test batched
           SCREAMING_SNAKE_CASE__       :  str						     =		image_processing(_lowercase   ,   _lowercase   ,   return_tensors='''pt'''      )
           self.assertEqual(
               encoding['''pixel_values'''].shape   ,   (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )   ,   )
           self.assertEqual(
               encoding['''labels'''].shape   ,   (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )   ,   )
           self.assertEqual(encoding['''labels'''].dtype   ,   torch.long      )
           self.assertTrue(encoding['''labels'''].min().item() >= 0      )
           self.assertTrue(encoding['''labels'''].max().item() <= 2_55      )

           # Test not batched input (PIL images)
           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Tuple						     =		prepare_semantic_single_inputs()

           SCREAMING_SNAKE_CASE__       :  int						     =		image_processing(_lowercase   ,   _lowercase   ,   return_tensors='''pt'''      )
           self.assertEqual(
               encoding['''pixel_values'''].shape   ,   (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )   ,   )
           self.assertEqual(
               encoding['''labels'''].shape   ,   (
                   1,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )   ,   )
           self.assertEqual(encoding['''labels'''].dtype   ,   torch.long      )
           self.assertTrue(encoding['''labels'''].min().item() >= 0      )
           self.assertTrue(encoding['''labels'''].max().item() <= 2_55      )

           # Test batched input (PIL images)
           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Dict						     =		prepare_semantic_batch_inputs()

           SCREAMING_SNAKE_CASE__       :  Any						     =		image_processing(_lowercase   ,   _lowercase   ,   return_tensors='''pt'''      )
           self.assertEqual(
               encoding['''pixel_values'''].shape   ,   (
                   2,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )   ,   )
           self.assertEqual(
               encoding['''labels'''].shape   ,   (
                   2,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )   ,   )
           self.assertEqual(encoding['''labels'''].dtype   ,   torch.long      )
           self.assertTrue(encoding['''labels'''].min().item() >= 0      )
           self.assertTrue(encoding['''labels'''].max().item() <= 2_55      )







     def 							lowercase__       (						self :						int      ):
           # Initialize image_processing
           SCREAMING_SNAKE_CASE__       :  str						     =		self.image_processing_class(**self.image_processor_dict      )

           # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  List[str]						     =		prepare_semantic_single_inputs()
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		image_processing(_lowercase   ,   _lowercase   ,   return_tensors='''pt'''      )
           self.assertTrue(encoding['''labels'''].min().item() >= 0      )
           self.assertTrue(encoding['''labels'''].max().item() <= 1_50      )

           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		True
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		image_processing(_lowercase   ,   _lowercase   ,   return_tensors='''pt'''      )
           self.assertTrue(encoding['''labels'''].min().item() >= 0      )
           self.assertTrue(encoding['''labels'''].max().item() <= 2_55      )









"""simple docstring"""





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


__UpperCamelCase    : str				 =						4
__UpperCamelCase    : List[str]				 =						3







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					pass

def     __SCREAMING_SNAKE_CASE    ( A_					):
	for shard in shards:
		for i in range(A_					):
			yield {"i": i, "shard": shard}

def     __SCREAMING_SNAKE_CASE    ( ):
	lowerCAmelCase__      :		List[str]    							=      int(os.environ['''RANK''']					)
	lowerCAmelCase__      :		Union[str, Any]    							=      int(os.environ['''WORLD_SIZE''']					)

	lowerCAmelCase__      :		Tuple    							=      ArgumentParser()
	parser.add_argument('''--streaming'''		,	type=A_					)
	parser.add_argument('''--local_rank'''		,	type=A_					)
	parser.add_argument('''--num_workers'''		,	type=A_		,	default=0					)
	lowerCAmelCase__      :		Tuple    							=      parser.parse_args()
	lowerCAmelCase__      :		Tuple    							=      args.streaming
	lowerCAmelCase__      :		Any    							=      args.num_workers

	lowerCAmelCase__      :		Dict    							=      {'''shards''': [f'shard_{shard_idx}' for shard_idx in range(A_					)]}
	lowerCAmelCase__      :		Optional[int]    							=      IterableDataset.from_generator(A_		,	gen_kwargs=A_					)
	if not streaming:
		lowerCAmelCase__      :		Dict    							=      Dataset.from_list(list(A_					)					)

	lowerCAmelCase__      :		Optional[Any]    							=      split_dataset_by_node(A_		,	rank=A_		,	world_size=A_					)
	lowerCAmelCase__      :		List[Any]    							=      torch.utils.data.DataLoader(A_		,	num_workers=A_					)

	lowerCAmelCase__      :		Optional[int]    							=      NUM_SHARDS * NUM_ITEMS_PER_SHARD
	lowerCAmelCase__      :		List[str]    							=      full_size // world_size
	expected_local_size += int(rank < (full_size % world_size)					)

	lowerCAmelCase__      :		int    							=      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()




