




"""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 MobileNetVaImageProcessor







class 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				def __init__( self	,				_a	,				_a=7	,				_a=3	,				_a=18	,				_a=30	,				_a=400	,				_a=True	,				_a=None	,				_a=True	,				_a=None	,				):





							"""simple docstring"""
							lowerCamelCase       =      size if size is not None else {"""shortest_edge""": 20}
							lowerCamelCase       =      crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
							lowerCamelCase       =      parent
							lowerCamelCase       =      batch_size
							lowerCamelCase       =      num_channels
							lowerCamelCase       =      image_size
							lowerCamelCase       =      min_resolution
							lowerCamelCase       =      max_resolution
							lowerCamelCase       =      do_resize
							lowerCamelCase       =      size
							lowerCamelCase       =      do_center_crop
							lowerCamelCase       =      crop_size





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							}







@require_torch
@require_vision
class 					__magic_name__							( UpperCAmelCase__		,      unittest.TestCase       ):






				'''simple docstring'''
				__UpperCamelCase    						=		MobileNetVaImageProcessor if is_vision_available() else None





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      MobileNetVaImageProcessingTester(self				)





				@property
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return self.image_processor_tester.prepare_image_processor_dict()





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(_a	,				"""do_resize"""				)				)
							self.assertTrue(hasattr(_a	,				"""size"""				)				)
							self.assertTrue(hasattr(_a	,				"""do_center_crop"""				)				)
							self.assertTrue(hasattr(_a	,				"""crop_size"""				)				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.image_processing_class.from_dict(self.image_processor_dict				)
							self.assertEqual(image_processor.size	,				{"""shortest_edge""": 20}				)
							self.assertEqual(image_processor.crop_size	,				{"""height""": 18, """width""": 18}				)

							lowerCamelCase       =      self.image_processing_class.from_dict(self.image_processor_dict	,				size=42	,				crop_size=84				)
							self.assertEqual(image_processor.size	,				{"""shortest_edge""": 42}				)
							self.assertEqual(image_processor.crop_size	,				{"""height""": 84, """width""": 84}				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							pass





				def      _lowerCAmelCase       ( self				):





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

							# Test not batched input
							lowerCamelCase       =      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
							lowerCamelCase       =      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.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )	,				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							lowerCamelCase       =      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
							lowerCamelCase       =      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
							lowerCamelCase       =      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.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )	,				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							lowerCamelCase       =      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
							lowerCamelCase       =      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
							lowerCamelCase       =      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.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )	,				)









"""simple docstring"""




from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap



def 					a__				(       snake_case__       )		->      List[str]:
			return getitem, k



def 					a__				(       snake_case__ ,				snake_case__       )		->      Optional[Any]:
			return setitem, k, v



def 					a__				(       snake_case__       )		->      str:
			return delitem, k



def 					a__				(       snake_case__ ,				snake_case__ ,				*snake_case__       )		->      Union[str, Any]:
			try:
						return fun(snake_case__ ,				*snake_case__       ), None
			except Exception as e:
						return None, e


lowerCAmelCase							:    Optional[int]  							=							(
    _set("""key_a""", """val_a"""),
    _set("""key_b""", """val_b"""),
)

lowerCAmelCase							:    List[Any]  							=							[
    _set("""key_a""", """val_a"""),
    _set("""key_a""", """val_b"""),
]

lowerCAmelCase							:    List[Any]  							=							[
    _set("""key_a""", """val_a"""),
    _set("""key_b""", """val_b"""),
    _del("""key_a"""),
    _del("""key_b"""),
    _set("""key_a""", """val_a"""),
    _del("""key_a"""),
]

lowerCAmelCase							:    List[Any]  							=							[
    _get("""key_a"""),
    _del("""key_a"""),
    _set("""key_a""", """val_a"""),
    _del("""key_a"""),
    _del("""key_a"""),
    _get("""key_a"""),
]

lowerCAmelCase							:    Tuple  							=							[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

lowerCAmelCase							:    Tuple  							=							[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set("""key_a""", """val_b"""),
]



@pytest.mark.parametrize(
    """operations""" ,				(
        pytest.param(_add_items ,				id="""add items"""       ),
        pytest.param(_overwrite_items ,				id="""overwrite items"""       ),
        pytest.param(_delete_items ,				id="""delete items"""       ),
        pytest.param(_access_absent_items ,				id="""access absent items"""       ),
        pytest.param(_add_with_resize_up ,				id="""add with resize up"""       ),
        pytest.param(_add_with_resize_down ,				id="""add with resize down"""       ),
    ) ,				)
def 					a__				(       snake_case__       )		->      Any:
			lowerCamelCase       =      HashMap(initial_block_size=4       )
			lowerCamelCase       =      {}
			for _, (fun, *args) in enumerate(snake_case__       ):
						lowerCamelCase   ,     lowerCamelCase       =      _run_operation(snake_case__ ,				snake_case__ ,				*snake_case__       )
						lowerCamelCase   ,     lowerCamelCase       =      _run_operation(snake_case__ ,				snake_case__ ,				*snake_case__       )
						assert my_res == py_res
						assert str(snake_case__       ) == str(snake_case__       )
						assert set(snake_case__       ) == set(snake_case__       )
						assert len(snake_case__       ) == len(snake_case__       )
						assert set(my.items()       ) == set(py.items()       )



def 					a__				(       )		->      int:
			def is_public(snake_case__       ) -> bool:
						return not name.startswith("""_"""       )

			lowerCamelCase       =      {name for name in dir({}       ) if is_public(snake_case__       )}
			lowerCamelCase       =      {name for name in dir(HashMap()       ) if is_public(snake_case__       )}

			assert dict_public_names > hash_public_names



