

'''simple docstring'''




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, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor



class     lowercase_		(						unittest.TestCase	):

  '''simple docstring'''





  def __init__(       self				,	a_				,	a_=7				,	a_=3				,	a_=1_8				,	a_=3_0				,	a_=4_0_0				,	a_=True				,	a_=None				,	a_=True				,	a_=[0.5, 0.5, 0.5]				,	a_=[0.5, 0.5, 0.5]				,	)							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		size if size is not None else {'height': 1_8, 'width': 1_8}
    UpperCAmelCase  							=		parent
    UpperCAmelCase  							=		batch_size
    UpperCAmelCase  							=		num_channels
    UpperCAmelCase  							=		image_size
    UpperCAmelCase  							=		min_resolution
    UpperCAmelCase  							=		max_resolution
    UpperCAmelCase  							=		do_resize
    UpperCAmelCase  							=		size
    UpperCAmelCase  							=		do_normalize
    UpperCAmelCase  							=		image_mean
    UpperCAmelCase  							=		image_std





  def   snake_case_     (       self       )							->			Optional[int]:


    """simple docstring"""
    return {
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_normalize": self.do_normalize,
        "do_resize": self.do_resize,
        "size": self.size,
    }




@require_torch
@require_vision
class     lowercase_		(						a	,    unittest.TestCase	):

  '''simple docstring'''





  __lowerCAmelCase   :	Optional[Any]      					=				ViTImageProcessor if is_vision_available() else None
  def   snake_case_     (       self       )							->			Tuple:


    """simple docstring"""
    UpperCAmelCase  							=		ViTImageProcessingTester(self       )
  @property
  def   snake_case_     (       self       )							->			int:


    """simple docstring"""
    return self.image_processor_tester.prepare_image_processor_dict()
  def   snake_case_     (       self       )							->			List[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		self.image_processing_class(**self.image_processor_dict       )
    self.assertTrue(hasattr(a_				,	'image_mean'       )       )
    self.assertTrue(hasattr(a_				,	'image_std'       )       )
    self.assertTrue(hasattr(a_				,	'do_normalize'       )       )
    self.assertTrue(hasattr(a_				,	'do_resize'       )       )
    self.assertTrue(hasattr(a_				,	'size'       )       )
  def   snake_case_     (       self       )							->			Tuple:


    """simple docstring"""
    UpperCAmelCase  							=		self.image_processing_class.from_dict(self.image_processor_dict       )
    self.assertEqual(image_processor.size				,	{'height': 1_8, 'width': 1_8}       )

    UpperCAmelCase  							=		self.image_processing_class.from_dict(self.image_processor_dict				,	size=4_2       )
    self.assertEqual(image_processor.size				,	{'height': 4_2, 'width': 4_2}       )
  def   snake_case_     (       self       )							->			Optional[Any]:


    """simple docstring"""
    pass
  def   snake_case_     (       self       )							->			Optional[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		self.image_processing_class(**self.image_processor_dict       )
    # create random PIL images
    UpperCAmelCase  							=		prepare_image_inputs(self.image_processor_tester				,	equal_resolution=a_       )
    for image in image_inputs:
      self.assertIsInstance(a_				,	Image.Image       )

    # Test not batched input
    UpperCAmelCase  							=		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.size['height'],
            self.image_processor_tester.size['width'],
        )				,	)

    # Test batched
    UpperCAmelCase  							=		image_processing(a_				,	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.size['height'],
            self.image_processor_tester.size['width'],
        )				,	)
  def   snake_case_     (       self       )							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		self.image_processing_class(**self.image_processor_dict       )
    # create random numpy tensors
    UpperCAmelCase  							=		prepare_image_inputs(self.image_processor_tester				,	equal_resolution=a_				,	numpify=a_       )
    for image in image_inputs:
      self.assertIsInstance(a_				,	np.ndarray       )

    # Test not batched input
    UpperCAmelCase  							=		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.size['height'],
            self.image_processor_tester.size['width'],
        )				,	)

    # Test batched
    UpperCAmelCase  							=		image_processing(a_				,	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.size['height'],
            self.image_processor_tester.size['width'],
        )				,	)





  def   snake_case_     (       self       )							->			Tuple:


    """simple docstring"""
    UpperCAmelCase  							=		self.image_processing_class(**self.image_processor_dict       )
    # create random PyTorch tensors
    UpperCAmelCase  							=		prepare_image_inputs(self.image_processor_tester				,	equal_resolution=a_				,	torchify=a_       )
    for image in image_inputs:
      self.assertIsInstance(a_				,	torch.Tensor       )

    # Test not batched input
    UpperCAmelCase  							=		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.size['height'],
            self.image_processor_tester.size['width'],
        )				,	)

    # Test batched
    UpperCAmelCase  							=		image_processing(a_				,	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.size['height'],
            self.image_processor_tester.size['width'],
        )				,	)





'''simple docstring'''




from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


_a			:	Optional[int]								=  TypeVar('DatasetType', Dataset, IterableDataset)


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				DatasetType    ,				SCREAMING_SNAKE_CASE :				int    ,				SCREAMING_SNAKE_CASE :				int  ):
  if isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  ):
    return _split_by_node_map_style_dataset(SCREAMING_SNAKE_CASE    ,				rank=SCREAMING_SNAKE_CASE    ,				world_size=SCREAMING_SNAKE_CASE  )
  else:
    return _split_by_node_iterable_dataset(SCREAMING_SNAKE_CASE    ,				rank=SCREAMING_SNAKE_CASE    ,				world_size=SCREAMING_SNAKE_CASE  )


