


'''simple docstring'''
from __future__ import annotations

from collections.abc import Iterator
from typing import Any






class        lowerCAmelCase_  :






       def __init__( self			,     _UpperCamelCase   )->      Any:
            _A				=							data
            _A				=							None






class        lowerCAmelCase_  :






       def __init__( self   )->      Tuple:
            _A				=							None
            _A				=							None






       def __iter__( self   )->      Iterator[Any]:
            _A				=							self.head
            while self.head:
                 yield node.data
                 _A				=							node.next
                 if node == self.head:
                      break






       def __len__( self   )->      int:
            return sum(1 for _ in self   )






       def __repr__( self   )->      int:
            return "->".join(str(_UpperCamelCase   ) for item in iter(self   )   )






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      None:
            self.insert_nth(len(self   )			,     _UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      None:
            self.insert_nth(0			,     _UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase   )->      None:
            if index < 0 or index > len(self   ):
                 raise IndexError('list index out of range.'   )
            _A				=							Node(_UpperCamelCase   )
            if self.head is None:
                 _A				=							new_node  # first node points itself
                 _A				=							_A				=							new_node
            elif index == 0:  # insert at head
                 _A				=							self.head
                 _A				=							_A				=							new_node
            else:
                 _A				=							self.head
                 for _ in range(index - 1   ):
                      _A				=							temp.next
                 _A				=							temp.next
                 _A				=							new_node
                 if index == len(self   ) - 1:  # insert at tail
                      _A				=							new_node






       def   UpperCamelCase						( self   )->      Any:
            return self.delete_nth(0   )






       def   UpperCamelCase						( self   )->      Any:
            return self.delete_nth(len(self   ) - 1   )






       def   UpperCamelCase						( self			,     _UpperCamelCase = 0   )->      Any:
            if not 0 <= index < len(self   ):
                 raise IndexError('list index out of range.'   )
            _A				=							self.head
            if self.head == self.tail:  # just one node
                 _A				=							_A				=							None
            elif index == 0:  # delete head node
                 _A				=							self.tail.next.next
                 _A				=							self.head.next
            else:
                 _A				=							self.head
                 for _ in range(index - 1   ):
                      _A				=							temp.next
                 _A				=							temp.next
                 _A				=							temp.next.next
                 if index == len(self   ) - 1:  # delete at tail
                      _A				=							temp
            return delete_node.data






       def   UpperCamelCase						( self   )->      bool:
            return len(self   ) == 0






def 					lowerCamelCase_ ( )					->	None:







     """simple docstring"""
     _A				=							CircularLinkedList()
     assert len(__UpperCamelCase		) == 0
     assert circular_linked_list.is_empty() is True
     assert str(__UpperCamelCase		) == ""

     try:
          circular_linked_list.delete_front()
          raise AssertionError  # This should not happen
     except IndexError:
          assert True  # This should happen

     try:
          circular_linked_list.delete_tail()
          raise AssertionError  # This should not happen
     except IndexError:
          assert True  # This should happen

     try:
          circular_linked_list.delete_nth(-1		)
          raise AssertionError
     except IndexError:
          assert True

     try:
          circular_linked_list.delete_nth(0		)
          raise AssertionError
     except IndexError:
          assert True

     assert circular_linked_list.is_empty() is True
     for i in range(5		):
          assert len(__UpperCamelCase		) == i
          circular_linked_list.insert_nth(__UpperCamelCase							,    i + 1		)
     assert str(__UpperCamelCase		) == "->".join(str(__UpperCamelCase		) for i in range(1							,    6		)		)

     circular_linked_list.insert_tail(6		)
     assert str(__UpperCamelCase		) == "->".join(str(__UpperCamelCase		) for i in range(1							,    7		)		)
     circular_linked_list.insert_head(0		)
     assert str(__UpperCamelCase		) == "->".join(str(__UpperCamelCase		) for i in range(0							,    7		)		)

     assert circular_linked_list.delete_front() == 0
     assert circular_linked_list.delete_tail() == 6
     assert str(__UpperCamelCase		) == "->".join(str(__UpperCamelCase		) for i in range(1							,    6		)		)
     assert circular_linked_list.delete_nth(2		) == 3

     circular_linked_list.insert_nth(2							,    3		)
     assert str(__UpperCamelCase		) == "->".join(str(__UpperCamelCase		) for i in range(1							,    6		)		)

     assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
    import doctest

    doctest.testmod()









'''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        lowerCAmelCase_  (     unittest.TestCase	):






       def   UpperCamelCase						( self   )->      Optional[int]:
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()






       def   UpperCamelCase						( self   )->      Union[str, Any]:
            _A				=							StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'   )
            _A				=							sd_pipe.to(_UpperCamelCase   )
            sd_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            sd_pipe.set_scheduler('sample_euler'   )

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

            _A				=							output.images

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

            assert image.shape == (1, 512, 512, 3)
            _A				=							np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]   )

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






       def   UpperCamelCase						( self   )->      Optional[Any]:
            _A				=							StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'   )
            _A				=							sd_pipe.to(_UpperCamelCase   )
            sd_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            sd_pipe.set_scheduler('sample_euler'   )

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

            _A				=							output.images

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

            assert image.shape == (1, 512, 512, 3)
            _A				=							np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]   )

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






       def   UpperCamelCase						( self   )->      Optional[int]:
            _A				=							StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'   )
            _A				=							sd_pipe.to(_UpperCamelCase   )
            sd_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            sd_pipe.set_scheduler('sample_dpmpp_2m'   )

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

            _A				=							output.images

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

            assert image.shape == (1, 512, 512, 3)
            _A				=							np.array(
                [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]   )

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





