

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_		(       SCREAMING_SNAKE_CASE			,							SCREAMING_SNAKE_CASE			,							unittest.TestCase				):
							_UpperCAmelCase    :  Any						      =      VQModel
							_UpperCAmelCase    :  Tuple						      =      '''sample'''
							@property
							def     lowerCAmelCase     (		self		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			int=(3_2, 3_2)):
								__lowerCamelCase				:				Any					       = 4
								__lowerCamelCase				:				Union[str, Any]					       = 3

								__lowerCamelCase				:				Tuple					       = floats_tensor((batch_size, num_channels) + sizes).to(_snake_case)

								return {"sample": image}
							@property
							def     lowerCAmelCase     (		self		:			int):
								return (3, 3_2, 3_2)
							@property
							def     lowerCAmelCase     (		self		:			List[Any]):
								return (3, 3_2, 3_2)
							def     lowerCAmelCase     (		self		:			int):
								__lowerCamelCase				:				List[Any]					       = {
								    'block_out_channels': [3_2, 6_4],
								    'in_channels': 3,
								    'out_channels': 3,
								    'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
								    'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
								    'latent_channels': 3,
								}
								__lowerCamelCase				:				Optional[int]					       = self.dummy_input
								return init_dict, inputs_dict
							def     lowerCAmelCase     (		self		:			Union[str, Any]):
								pass
							def     lowerCAmelCase     (		self		:			Optional[int]):
								pass
							def     lowerCAmelCase     (		self		:			str):
								__lowerCamelCase       , __lowerCamelCase				:				str					       = VQModel.from_pretrained('fusing/vqgan-dummy'   ,output_loading_info=_snake_case)
								self.assertIsNotNone(_snake_case)
								self.assertEqual(len(loading_info['missing_keys'])   ,0)

								model.to(_snake_case)
								__lowerCamelCase				:				str					       = model(**self.dummy_input)

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


							def     lowerCAmelCase     (		self		:			int):
								__lowerCamelCase				:				Union[str, Any]					       = VQModel.from_pretrained('fusing/vqgan-dummy')
								model.to(_snake_case).eval()

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

								__lowerCamelCase				:				Optional[int]					       = torch.randn(1   ,model.config.in_channels   ,model.config.sample_size   ,model.config.sample_size)
								__lowerCamelCase				:				List[Any]					       = image.to(_snake_case)
								with torch.no_grad():
									__lowerCamelCase				:				Optional[Any]					       = model(_snake_case).sample

								__lowerCamelCase				:				Dict					       = output[0, -1, -3:, -3:].flatten().cpu()
								# fmt: off
								__lowerCamelCase				:				Any					       = torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143])
								# fmt: on
								self.assertTrue(torch.allclose(_snake_case   ,_snake_case   ,atol=1E-3))






from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  tuple[int | None, int | None, float]:
				if not arr:
								return None, None, 0
				if low == high:
								return low, high, arr[low]

				__a										=    (low + high) // 2
				__a							,     __a							,     __a										=    max_subarray(a__						,    a__						,    a__  )
				__a							,     __a							,     __a										=    max_subarray(a__						,    mid + 1						,    a__  )
				__a							,     __a							,     __a										=    max_cross_sum(a__						,    a__						,    a__						,    a__  )
				if left_sum >= right_sum and left_sum >= cross_sum:
								return left_low, left_high, left_sum
				elif right_sum >= left_sum and right_sum >= cross_sum:
								return right_low, right_high, right_sum
				return cross_left, cross_right, cross_sum


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__  )       ->  tuple[int, int, float]:
				__a							,     __a										=    float('''-inf'''  ), -1
				__a							,     __a										=    float('''-inf'''  ), -1

				__a										=    0
				for i in range(a__						,    low - 1						,    -1  ):
								summ += arr[i]
								if summ > left_sum:
												__a										=    summ
												__a										=    i

				__a										=    0
				for i in range(mid + 1						,    high + 1  ):
								summ += arr[i]
								if summ > right_sum:
												__a										=    summ
												__a										=    i

				return max_left, max_right, (left_sum + right_sum)


def     __lowerCAmelCase			(			a__  )       ->  float:
				__a										=    [randint(1						,    a__  ) for _ in range(a__  )]
				__a										=    time.time()
				max_subarray(a__						,    0						,    input_size - 1  )
				__a										=    time.time()
				return end - start


def     __lowerCAmelCase			(			)       ->  None:
				__a										=    [10, 100, 1000, 1_0000, 5_0000, 10_0000, 20_0000, 30_0000, 40_0000, 50_0000]
				__a										=    [time_max_subarray(a__  ) for input_size in input_sizes]
				print('''No of Inputs\t\tTime Taken'''  )
				for input_size, runtime in zip(a__						,    a__  ):
								print(a__						,    '''\t\t'''						,    a__  )
				plt.plot(a__						,    a__  )
				plt.xlabel('''Number of Inputs'''  )
				plt.ylabel('''Time taken in seconds'''  )
				plt.show()


if __name__ == "__main__":
					from doctest import testmod

					testmod()