
import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows






def 						__magic_name__			(   __a      :	Any						):  # picklable for multiprocessing



     '''simple docstring'''




     return i + 1






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def 						__magic_name__			(   ):



     '''simple docstring'''




     with parallel_backend("""spark"""						):
          assert ParallelBackendConfig.backend_name == "spark"

     UpperCamelCase__				=    [1, 2, 3]
     with pytest.raises(__a						):
          with parallel_backend("""unsupported backend"""						):
               map_nested(__a	,		__a	,		num_proc=2						)

     with pytest.raises(__a						):
          with parallel_backend("""unsupported backend"""						):
               map_nested(__a	,		__a	,		num_proc=-1						)






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc"""	,		[2, -1]						)
def 						__magic_name__			(   __a      :	Optional[int]						):



     '''simple docstring'''




     UpperCamelCase__				=    [1, 2]
     UpperCamelCase__				=    {"""a""": 1, """b""": 2}
     UpperCamelCase__				=    {"""a""": [1, 2], """b""": [3, 4]}
     UpperCamelCase__				=    {"""a""": {"""1""": 1}, """b""": 2}
     UpperCamelCase__				=    {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
     UpperCamelCase__				=    [2, 3]
     UpperCamelCase__				=    {"""a""": 2, """b""": 3}
     UpperCamelCase__				=    {"""a""": [2, 3], """b""": [4, 5]}
     UpperCamelCase__				=    {"""a""": {"""1""": 2}, """b""": 3}
     UpperCamelCase__				=    {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}

     with parallel_backend("""spark"""						):
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa







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 GLPNImageProcessor




class      __A(    unittest.TestCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=7	,      SCREAMING_SNAKE_CASE_=3	,      SCREAMING_SNAKE_CASE_=18	,      SCREAMING_SNAKE_CASE_=30	,      SCREAMING_SNAKE_CASE_=4_00	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=32	,      SCREAMING_SNAKE_CASE_=True	,      ):
       UpperCamelCase__				=    parent
       UpperCamelCase__				=    batch_size
       UpperCamelCase__				=    num_channels
       UpperCamelCase__				=    image_size
       UpperCamelCase__				=    min_resolution
       UpperCamelCase__				=    max_resolution
       UpperCamelCase__				=    do_resize
       UpperCamelCase__				=    size_divisor
       UpperCamelCase__				=    do_rescale







  def 	UpperCAmelCase_ (self						):
       return {
           "do_resize": self.do_resize,
           "size_divisor": self.size_divisor,
           "do_rescale": self.do_rescale,
       }






@require_torch
@require_vision
class      __A(    __lowerCamelCase    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      GLPNImageProcessor if is_vision_available() else None







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    GLPNImageProcessingTester(self						)







  @property
  def 	UpperCAmelCase_ (self						):
       return self.image_processor_tester.prepare_image_processor_dict()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_resize"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """size_divisor"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """resample"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_rescale"""						)						)







  def 	UpperCAmelCase_ (self						):
       pass







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processing
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      Image.Image						)

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       UpperCamelCase__				=    image_processing(image_inputs[0]	,      return_tensors="""pt"""						).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processing
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_	,      numpify=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      np.ndarray						)

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       UpperCamelCase__				=    image_processing(image_inputs[0]	,      return_tensors="""pt"""						).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processing
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_	,      torchify=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      torch.Tensor						)

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       UpperCamelCase__				=    image_processing(image_inputs[0]	,      return_tensors="""pt"""						).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)





