"""simple docstring"""



from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap





def    SCREAMING_SNAKE_CASE       (     snake_case):
     return getitem, k





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     return setitem, k, v





def    SCREAMING_SNAKE_CASE       (     snake_case):
     return delitem, k





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      *snake_case):
     try:
          return fun(snake_case,      *snake_case), None
     except Exception as e:
          return None, e


__lowercase		:		Union[str, Any]														=							(
    _set("key_a", "val_a"),
    _set("key_b", "val_b"),
)

__lowercase		:		str														=							[
    _set("key_a", "val_a"),
    _set("key_a", "val_b"),
]

__lowercase		:		str														=							[
    _set("key_a", "val_a"),
    _set("key_b", "val_b"),
    _del("key_a"),
    _del("key_b"),
    _set("key_a", "val_a"),
    _del("key_a"),
]

__lowercase		:		int														=							[
    _get("key_a"),
    _del("key_a"),
    _set("key_a", "val_a"),
    _del("key_a"),
    _del("key_a"),
    _get("key_a"),
]

__lowercase		:		Union[str, Any]														=							[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

__lowercase		:		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    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							HashMap(initial_block_size=4)
     __snake_case				=							{}
     for _, (fun, *args) in enumerate(snake_case):
          __snake_case		,	__snake_case				=							_run_operation(snake_case,      snake_case,      *snake_case)
          __snake_case		,	__snake_case				=							_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    SCREAMING_SNAKE_CASE       (     ):
     def is_public(snake_case) -> bool:
          return not name.startswith('''_''')

     __snake_case				=							{name for name in dir({}) if is_public(snake_case)}
     __snake_case				=							{name for name in dir(HashMap()) if is_public(snake_case)}

     assert dict_public_names > hash_public_names
"""simple docstring"""



import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()



@slow
@require_torch_gpu
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def  lowercase					(		self							:     Tuple    )    ->       str:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
    def  lowercase					(		self							:     int    )    ->       Tuple:
         __snake_case				=							StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''    )
         __snake_case				=							sd_pipe.to(A_    )
         sd_pipe.set_progress_bar_config(disable=A_    )

         sd_pipe.set_scheduler('''sample_euler'''    )

         __snake_case				=							'''A painting of a squirrel eating a burger'''
         __snake_case				=							torch.manual_seed(0    )
         __snake_case				=							sd_pipe([prompt]	,      generator=A_	,      guidance_scale=9.0	,      num_inference_steps=20	,      output_type='''np'''    )

         __snake_case				=							output.images

         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         __snake_case				=							np.array([0.04_47, 0.04_92, 0.04_68, 0.04_08, 0.03_83, 0.04_08, 0.03_54, 0.03_80, 0.03_39]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
    def  lowercase					(		self							:     Optional[Any]    )    ->       Tuple:
         __snake_case				=							StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''    )
         __snake_case				=							sd_pipe.to(A_    )
         sd_pipe.set_progress_bar_config(disable=A_    )

         sd_pipe.set_scheduler('''sample_euler'''    )

         __snake_case				=							'''A painting of a squirrel eating a burger'''
         __snake_case				=							torch.manual_seed(0    )
         __snake_case				=							sd_pipe([prompt]	,      generator=A_	,      guidance_scale=9.0	,      num_inference_steps=20	,      output_type='''np'''    )

         __snake_case				=							output.images

         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         __snake_case				=							np.array([0.12_37, 0.13_20, 0.14_38, 0.13_59, 0.13_90, 0.11_32, 0.12_77, 0.11_75, 0.11_12]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5E-1







    def  lowercase					(		self							:     List[str]    )    ->       Optional[Any]:
         __snake_case				=							StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''    )
         __snake_case				=							sd_pipe.to(A_    )
         sd_pipe.set_progress_bar_config(disable=A_    )

         sd_pipe.set_scheduler('''sample_dpmpp_2m'''    )

         __snake_case				=							'''A painting of a squirrel eating a burger'''
         __snake_case				=							torch.manual_seed(0    )
         __snake_case				=							sd_pipe(
             [prompt]	,      generator=A_	,      guidance_scale=7.5	,      num_inference_steps=15	,      output_type='''np'''	,      use_karras_sigmas=A_	,      )

         __snake_case				=							output.images

         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         __snake_case				=							np.array(
             [0.11_38_16_89, 0.12_11_29_21, 0.1_38_94_57, 0.12_54_96_06, 0.1_24_49_64, 0.10_83_15_17, 0.11_56_28_66, 0.10_86_78_16, 0.10_49_90_48]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2