


"""simple docstring"""
from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				str:

							'''simple docstring'''





							return getitem, k







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Optional[Any]:

							'''simple docstring'''





							return setitem, k, v







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				Optional[int]:

							'''simple docstring'''





							return delitem, k







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, *_SCREAMING_SNAKE_CASE				)    ->				Any:

							'''simple docstring'''





							try:
														return fun(_SCREAMING_SNAKE_CASE				, *_SCREAMING_SNAKE_CASE				), None
							except Exception as e:
														return None, e


__SCREAMING_SNAKE_CASE							:    str		=    (
    _set('key_a', 'val_a'),
    _set('key_b', 'val_b'),
)

__SCREAMING_SNAKE_CASE							:    int		=    [
    _set('key_a', 'val_a'),
    _set('key_a', 'val_b'),
]

__SCREAMING_SNAKE_CASE							:    int		=    [
    _set('key_a', 'val_a'),
    _set('key_b', 'val_b'),
    _del('key_a'),
    _del('key_b'),
    _set('key_a', 'val_a'),
    _del('key_a'),
]

__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    [
    _get('key_a'),
    _del('key_a'),
    _set('key_a', 'val_a'),
    _del('key_a'),
    _del('key_a'),
    _get('key_a'),
]

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

__SCREAMING_SNAKE_CASE							:    List[str]		=    [
    *[_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				(      _SCREAMING_SNAKE_CASE				)    ->				Optional[int]:

							'''simple docstring'''





							snake_case_           =		HashMap(initial_block_size=4				)
							snake_case_           =		{}
							for _, (fun, *args) in enumerate(_SCREAMING_SNAKE_CASE				):
														snake_case_       ,						snake_case_           =		_run_operation(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, *_SCREAMING_SNAKE_CASE				)
														snake_case_       ,						snake_case_           =		_run_operation(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, *_SCREAMING_SNAKE_CASE				)
														assert my_res == py_res
														assert str(_SCREAMING_SNAKE_CASE				) == str(_SCREAMING_SNAKE_CASE				)
														assert set(_SCREAMING_SNAKE_CASE				) == set(_SCREAMING_SNAKE_CASE				)
														assert len(_SCREAMING_SNAKE_CASE				) == len(_SCREAMING_SNAKE_CASE				)
														assert set(my.items()				) == set(py.items()				)







def 						_a				(      )    ->				Dict:

							'''simple docstring'''





							def is_public(_SCREAMING_SNAKE_CASE				) -> bool:
														return not name.startswith("""_"""				)

							snake_case_           =		{name for name in dir({}				) if is_public(_SCREAMING_SNAKE_CASE				)}
							snake_case_           =		{name for name in dir(HashMap()				) if is_public(_SCREAMING_SNAKE_CASE				)}

							assert dict_public_names > hash_public_names







"""simple docstring"""
import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()





class      __A     (snake_case__						,		snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		str							      =    VQModel
							__lowercase:		Union[str, Any]							      =    """sample"""



							@property
							def 		lowerCAmelCase       (				self							:      List[str]		,       UpperCAmelCase_							:      List[str]=(32, 32)       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		4
														snake_case_           =		3

														snake_case_           =		floats_tensor((batch_size, num_channels) + sizes       ).to(UpperCAmelCase_       )

														return {"sample": image}



							@property
							def 		lowerCAmelCase       (				self							:      Tuple       )					->str:



														"""simple docstring"""



														return (3, 32, 32)



							@property
							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Any:



														"""simple docstring"""



														return (3, 32, 32)



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		{
														    """block_out_channels""": [32, 64],
														    """in_channels""": 3,
														    """out_channels""": 3,
														    """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
														    """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
														    """latent_channels""": 3,
														}
														snake_case_           =		self.dummy_input
														return init_dict, inputs_dict



							def 		lowerCAmelCase       (				self							:      List[str]       )					->Dict:



														"""simple docstring"""



														pass



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->List[str]:



														"""simple docstring"""



														pass



							def 		lowerCAmelCase       (				self							:      Any       )					->Union[str, Any]:



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		VQModel.from_pretrained("""fusing/vqgan-dummy"""		,       output_loading_info=UpperCAmelCase_       )
														self.assertIsNotNone(UpperCAmelCase_       )
														self.assertEqual(len(loading_info["""missing_keys"""]       )		,       0       )

														model.to(UpperCAmelCase_       )
														snake_case_           =		model(**self.dummy_input       )

														assert image is not None, "Make sure output is not None"



							def 		lowerCAmelCase       (				self							:      Tuple       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		VQModel.from_pretrained("""fusing/vqgan-dummy"""       )
														model.to(UpperCAmelCase_       ).eval()

														torch.manual_seed(0       )
														if torch.cuda.is_available():
																					torch.cuda.manual_seed_all(0       )

														snake_case_           =		torch.randn(1		,       model.config.in_channels		,       model.config.sample_size		,       model.config.sample_size       )
														snake_case_           =		image.to(UpperCAmelCase_       )
														with torch.no_grad():
																					snake_case_           =		model(UpperCAmelCase_       ).sample

														snake_case_           =		output[0, -1, -3:, -3:].flatten().cpu()
														# fmt: off
														snake_case_           =		torch.tensor([-0.0_153, -0.4_044, -0.1_880, -0.5_161, -0.2_418, -0.4_072, -0.1_612, -0.0_633, -0.0_143]       )
														# fmt: on
														self.assertTrue(torch.allclose(UpperCAmelCase_		,       UpperCAmelCase_		,       atol=1E-3       )       )



