


import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_snake_case	:						Optional[Any]         =							False

class 							a						(unittest.TestCase ):




							"""simple docstring"""


							def        __snake_case   (				self			:    int       ,      lowerCamelCase			:    Optional[Any]=32							)      ->       Dict:
														set_seed(0							)
														__snake_case						:     int									=  UNetaDModel(sample_size=lowerCamelCase       ,      in_channels=3       ,      out_channels=3							)
														__snake_case						:     List[Any]									=  torch.optim.SGD(model.parameters()       ,      lr=0.00_01							)
														return model, optimizer






							@slow
							def        __snake_case   (				self			:    str							)      ->       List[Any]:
														__snake_case						:     int									=  "cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
														__snake_case						:     Optional[Any]									=  DDPMScheduler(
														    num_train_timesteps=1000       ,      beta_start=0.00_01       ,      beta_end=0.02       ,      beta_schedule="linear"       ,      clip_sample=lowerCamelCase       ,      )
														__snake_case						:     Tuple									=  DDIMScheduler(
														    num_train_timesteps=1000       ,      beta_start=0.00_01       ,      beta_end=0.02       ,      beta_schedule="linear"       ,      clip_sample=lowerCamelCase       ,      )

														assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

														# shared batches for DDPM and DDIM
														set_seed(0							)
														__snake_case						:     Tuple									=  [torch.randn((4, 3, 32, 32)							).clip(-1       ,      1							).to(lowerCamelCase							) for _ in range(4							)]
														__snake_case						:     str									=  [torch.randn((4, 3, 32, 32)							).to(lowerCamelCase							) for _ in range(4							)]
														__snake_case						:     List[Any]									=  [torch.randint(0       ,      1000       ,      (4,)							).long().to(lowerCamelCase							) for _ in range(4							)]

														# train with a DDPM scheduler
														__snake_case					,     __snake_case						:     str									=  self.get_model_optimizer(resolution=32							)
														model.train().to(lowerCamelCase							)
														for i in range(4							):
																					optimizer.zero_grad()
																					__snake_case						:     Optional[Any]									=  ddpm_scheduler.add_noise(clean_images[i]       ,      noise[i]       ,      timesteps[i]							)
																					__snake_case						:     List[str]									=  model(lowerCamelCase       ,      timesteps[i]							).sample
																					__snake_case						:     Tuple									=  torch.nn.functional.mse_loss(lowerCamelCase       ,      noise[i]							)
																					loss.backward()
																					optimizer.step()
														del model, optimizer

														# recreate the model and optimizer, and retry with DDIM
														__snake_case					,     __snake_case						:     Optional[int]									=  self.get_model_optimizer(resolution=32							)
														model.train().to(lowerCamelCase							)
														for i in range(4							):
																					optimizer.zero_grad()
																					__snake_case						:     str									=  ddim_scheduler.add_noise(clean_images[i]       ,      noise[i]       ,      timesteps[i]							)
																					__snake_case						:     Any									=  model(lowerCamelCase       ,      timesteps[i]							).sample
																					__snake_case						:     Tuple									=  torch.nn.functional.mse_loss(lowerCamelCase       ,      noise[i]							)
																					loss.backward()
																					optimizer.step()
														del model, optimizer

														self.assertTrue(torch.allclose(lowerCamelCase       ,      lowerCamelCase       ,      atol=1E-5							)							)
														self.assertTrue(torch.allclose(lowerCamelCase       ,      lowerCamelCase       ,      atol=1E-5							)							)



"""simple docstring"""




from __future__ import annotations

import typing
from collections import Counter




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      typing.Counter[int]   			=  Counter()
  for base in range(1			,					max_perimeter + 1    ):
    for perpendicular in range(_UpperCamelCase			,					max_perimeter + 1    ):
      _lowercase:      str   			=  (base * base + perpendicular * perpendicular) ** 0.5
      if hypotenuse == int(_UpperCamelCase    ):
        _lowercase:      str   			=  int(base + perpendicular + hypotenuse    )
        if perimeter > max_perimeter:
          continue
        triplets[perimeter] += 1
  return triplets




def 			_lowerCAmelCase		(		_UpperCamelCase = 1_000    ):






  """simple docstring"""



  _lowercase:      int   			=  pythagorean_triple(_UpperCamelCase    )
  return triplets.most_common(1    )[0][0]


if __name__ == "__main__":
  print(f"""Perimeter {solution()} has maximum solutions""")
