
'''simple docstring'''


import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict



@pytest.mark.parametrize(
    '''split_dict''',      [
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train''',      num_bytes=1337,      num_examples=42,      dataset_name='''my_dataset''')}),
        SplitDict({'''train''': SplitInfo(name='''train''',      num_bytes=1337,      num_examples=42)}),
        SplitDict({'''train''': SplitInfo()}),
    ],      )
def  __snake_case     (    _UpperCAmelCase					:       SplitDict):
   UpperCamelCase       =  split_dict._to_yaml_list()
   assert len(_UpperCAmelCase) == len(_UpperCAmelCase)
   UpperCamelCase       =  SplitDict._from_yaml_list(_UpperCAmelCase)
   for split_name, split_info in split_dict.items():
      # dataset_name field is deprecated, and is therefore not part of the YAML dump
      UpperCamelCase       =  None
      # the split name of split_dict takes over the name of the split info object
      UpperCamelCase       =  split_name
   assert split_dict == reloaded



@pytest.mark.parametrize(
    '''split_info''',      [SplitInfo(), SplitInfo(dataset_name=_UpperCAmelCase), SplitInfo(dataset_name='''my_dataset''')])
def  __snake_case     (    _UpperCAmelCase					:       Optional[Any]):
   # For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
   # field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
   UpperCamelCase       =  asdict(SplitDict({'''train''': split_info}))
   assert "dataset_name" in split_dict_asdict["train"]
   assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name







'''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 LevitImageProcessor


class 						lowercase__	(  unittest.TestCase   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__=7 ,   lowerCamelCase__=3 ,   lowerCamelCase__=1_8 ,   lowerCamelCase__=3_0 ,   lowerCamelCase__=4_0_0 ,   lowerCamelCase__=True ,   lowerCamelCase__=None ,   lowerCamelCase__=True ,   lowerCamelCase__=None ,   lowerCamelCase__=True ,   lowerCamelCase__=[0.5, 0.5, 0.5] ,   lowerCamelCase__=[0.5, 0.5, 0.5] ,   ):

      '''simple docstring'''







      UpperCamelCase       =  size if size is not None else {'''shortest_edge''': 1_8}
      UpperCamelCase       =  crop_size if crop_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_center_crop
      UpperCamelCase       =  crop_size
      UpperCamelCase       =  do_normalize
      UpperCamelCase       =  image_mean
      UpperCamelCase       =  image_std







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "do_center_crop": self.do_center_crop,
          "size": self.size,
          "crop_size": self.crop_size,
      }




@require_torch
@require_vision
class 						lowercase__	(  snake_case_,					unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							LevitImageProcessor if is_vision_available() else None







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  LevitImageProcessingTester(self   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return self.image_processor_tester.prepare_image_processor_dict()







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''image_mean'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''image_std'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''do_normalize'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''do_resize'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''do_center_crop'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''size'''   )   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class.from_dict(self.image_processor_dict   )
      self.assertEqual(image_processor.size ,   {'''shortest_edge''': 1_8}   )
      self.assertEqual(image_processor.crop_size ,   {'''height''': 1_8, '''width''': 1_8}   )

      UpperCamelCase       =  self.image_processing_class.from_dict(self.image_processor_dict ,   size=4_2 ,   crop_size=8_4   )
      self.assertEqual(image_processor.size ,   {'''shortest_edge''': 4_2}   )
      self.assertEqual(image_processor.crop_size ,   {'''height''': 8_4, '''width''': 8_4}   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      pass







   def       UpperCAmelCase				(			self   ):

      '''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=lowerCamelCase__   )
      for image in image_inputs:
         self.assertIsInstance(lowerCamelCase__ ,   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.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          ) ,   )

      # Test batched
      UpperCamelCase       =  image_processing(lowerCamelCase__ ,   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       UpperCAmelCase				(			self   ):

      '''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=lowerCamelCase__ ,   numpify=lowerCamelCase__   )
      for image in image_inputs:
         self.assertIsInstance(lowerCamelCase__ ,   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.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          ) ,   )

      # Test batched
      UpperCamelCase       =  image_processing(lowerCamelCase__ ,   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       UpperCAmelCase				(			self   ):

      '''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=lowerCamelCase__ ,   torchify=lowerCamelCase__   )
      for image in image_inputs:
         self.assertIsInstance(lowerCamelCase__ ,   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.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          ) ,   )

      # Test batched
      UpperCamelCase       =  image_processing(lowerCamelCase__ ,   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'''],
          ) ,   )





