


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        lowerCamelCase__	(							unittest.TestCase      ):






			"""simple docstring"""






			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       inspect.getfile(accelerate.test_utils     )
						UpperCamelCase__					=       os.path.sep.join(mod_file.split(os.path.sep     )[:-1] + ["scripts", "test_script.py"]     )
						UpperCamelCase__					=       os.path.sep.join(
						    mod_file.split(os.path.sep     )[:-1] + ["scripts", "test_distributed_data_loop.py"]     )
						UpperCamelCase__					=       os.path.sep.join(mod_file.split(os.path.sep     )[:-1] + ["scripts", "test_ops.py"]     )




			@require_multi_gpu
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						print(F'''Found {torch.cuda.device_count()} devices.'''     )
						UpperCamelCase__					=       ["torchrun", F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
						with patch_environment(omp_num_threads=1     ):
									execute_subprocess_async(snake_case      ,						env=os.environ.copy()     )




			@require_multi_gpu
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						print(F'''Found {torch.cuda.device_count()} devices.'''     )
						UpperCamelCase__					=       ["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(snake_case      ,						env=os.environ.copy()     )




			@require_multi_gpu
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       ["torchrun", F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__     )]
						with patch_environment(omp_num_threads=1     ):
									execute_subprocess_async(snake_case      ,						env=os.environ.copy()     )







			@require_multi_gpu
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						print(F'''Found {torch.cuda.device_count()} devices, using 2 devices only'''     )
						UpperCamelCase__					=       ["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(snake_case      ,						env=os.environ.copy()     )


if __name__ == "__main__":
						__UpperCamelCase    		=       Accelerator()
						__UpperCamelCase    		=       (accelerator.state.process_index + 2, 1_0)
						__UpperCamelCase    		=       torch.randint(0, 1_0, shape).to(accelerator.device)

						__UpperCamelCase    		=       ''

						__UpperCamelCase    		=       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)."

						__UpperCamelCase    		=       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."
						__UpperCamelCase    		=       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)






import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
						import jax







@require_flax
class        lowerCamelCase__	(							UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					List[str]    =		FlaxAutoencoderKL




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       4
						UpperCamelCase__					=       3
						UpperCamelCase__					=       (32, 32)

						UpperCamelCase__					=       jax.random.PRNGKey(0     )
						UpperCamelCase__					=       jax.random.uniform(snake_case      ,						((batch_size, num_channels) + sizes)     )

						return {"sample": image, "prng_key": prng_key}







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       {
						    "block_out_channels": [32, 64],
						    "in_channels": 3,
						    "out_channels": 3,
						    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
						    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
						    "latent_channels": 4,
						}
						UpperCamelCase__					=       self.dummy_input
						return init_dict, inputs_dict


