

'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     @property
     def        UpperCAmelCase_	(		self			:					str	)						->     int:
      torch.manual_seed(0	)
      UpperCAmelCase	:     Tuple						 =			UNetaDModel(
          block_out_channels=(32, 64)					,      layers_per_block=2					,      sample_size=32					,      in_channels=3					,      out_channels=3					,      down_block_types=('DownBlock2D', 'AttnDownBlock2D')					,      up_block_types=('AttnUpBlock2D', 'UpBlock2D')					,      )
      return model




     def        UpperCAmelCase_	(		self			:					Dict	)						->     List[str]:
      UpperCAmelCase	:     str						 =			self.dummy_uncond_unet
      UpperCAmelCase	:     Optional[int]						 =			KarrasVeScheduler()

      UpperCAmelCase	:     str						 =			KarrasVePipeline(unet=__lowercase					,      scheduler=__lowercase	)
      pipe.to(__lowercase	)
      pipe.set_progress_bar_config(disable=__lowercase	)

      UpperCAmelCase	:     List[Any]						 =			torch.manual_seed(0	)
      UpperCAmelCase	:     List[Any]						 =			pipe(num_inference_steps=2					,      generator=__lowercase					,      output_type='numpy'	).images

      UpperCAmelCase	:     List[str]						 =			torch.manual_seed(0	)
      UpperCAmelCase	:     Any						 =			pipe(num_inference_steps=2					,      generator=__lowercase					,      output_type='numpy'					,      return_dict=__lowercase	)[0]

      UpperCAmelCase	:     Dict						 =			image[0, -3:, -3:, -1]
      UpperCAmelCase	:     Optional[Any]						 =			image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      UpperCAmelCase	:     Optional[int]						 =			np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]	)

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




@slow
@require_torch
class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Tuple:
      UpperCAmelCase	:     Any						 =			'''google/ncsnpp-celebahq-256'''
      UpperCAmelCase	:     List[str]						 =			UNetaDModel.from_pretrained(__lowercase	)
      UpperCAmelCase	:     int						 =			KarrasVeScheduler()

      UpperCAmelCase	:     List[str]						 =			KarrasVePipeline(unet=__lowercase					,      scheduler=__lowercase	)
      pipe.to(__lowercase	)
      pipe.set_progress_bar_config(disable=__lowercase	)

      UpperCAmelCase	:     Dict						 =			torch.manual_seed(0	)
      UpperCAmelCase	:     List[str]						 =			pipe(num_inference_steps=20					,      generator=__lowercase					,      output_type='numpy'	).images

      UpperCAmelCase	:     List[Any]						 =			image[0, -3:, -3:, -1]
      assert image.shape == (1, 256, 256, 3)
      UpperCAmelCase	:     Optional[Any]						 =			np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]	)

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







'''simple docstring'''

import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment




class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     def        UpperCAmelCase_	(		self			:					str	)						->     Tuple:
      UpperCAmelCase	:     Any						 =			inspect.getfile(accelerate.test_utils	)
      UpperCAmelCase	:     Dict						 =			os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_script.py']	)
      UpperCAmelCase	:     Dict						 =			os.path.sep.join(
          mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_distributed_data_loop.py']	)
      UpperCAmelCase	:     List[Any]						 =			os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_ops.py']	)


     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Optional[int]:
      print(f"""Found {torch.cuda.device_count()} devices."""	)
      UpperCAmelCase	:     Dict						 =			['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
      with patch_environment(omp_num_threads=1	):
       execute_subprocess_async(lowercase_					,      env=os.environ.copy()	)


     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     List[Any]:
      print(f"""Found {torch.cuda.device_count()} devices."""	)
      UpperCAmelCase	:     int						 =			['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
      print(f"""Command: {cmd}"""	)
      with patch_environment(omp_num_threads=1	):
       execute_subprocess_async(lowercase_					,      env=os.environ.copy()	)


     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					str	)						->     List[Any]:
      UpperCAmelCase	:     Optional[int]						 =			['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__	)]
      with patch_environment(omp_num_threads=1	):
       execute_subprocess_async(lowercase_					,      env=os.environ.copy()	)




     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					List[str]	)						->     Optional[Any]:
      print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only"""	)
      UpperCAmelCase	:     Dict						 =			['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
      with patch_environment(omp_num_threads=1					,      cuda_visible_devices='0,1'	):
       execute_subprocess_async(lowercase_					,      env=os.environ.copy()	)


if __name__ == "__main__":
 lowercase__         =      Accelerator()
 lowercase__         =      (accelerator.state.process_index + 2, 10)
 lowercase__         =      torch.randint(0, 10, shape).to(accelerator.device)

 lowercase__         =      ""

 lowercase__         =      accelerator.pad_across_processes(tensor)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
  error_msg += "Padding was not done with the right value (0)."

 lowercase__         =      accelerator.pad_across_processes(tensor, pad_first=True)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 lowercase__         =      accelerator.state.num_processes - accelerator.state.process_index - 1
 if not torch.equal(tensora[index:], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[:index] == 0):
  error_msg += "Padding was not done with the right value (0)."

 # Raise error at the end to make sure we don't stop at the first failure.
 if len(error_msg) > 0:
  raise ValueError(error_msg)




