


'''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      _lowerCAmelCase							(      unittest.TestCase			):


	def    _a     (self							):
				A_     :						List[Any]     =					inspect.getfile(accelerate.test_utils							)
				A_     :						Optional[int]     =					os.path.sep.join(mod_file.split(os.path.sep							)[:-1] + ["""scripts""", """test_script.py"""]							)
				A_     :						List[str]     =					os.path.sep.join(
				    mod_file.split(os.path.sep							)[:-1] + ["""scripts""", """test_distributed_data_loop.py"""]							)
				A_     :						Optional[int]     =					os.path.sep.join(mod_file.split(os.path.sep							)[:-1] + ["""scripts""", """test_ops.py"""]							)


	@require_multi_gpu
	def    _a     (self							):
				print(F'Found {torch.cuda.device_count()} devices.'							)
				A_     :						Union[str, Any]     =					["""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    _a     (self							):
				print(F'Found {torch.cuda.device_count()} devices.'							)
				A_     :						Tuple     =					["""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    _a     (self							):
				A_     :						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    _a     (self							):
				print(F'Found {torch.cuda.device_count()} devices, using 2 devices only'							)
				A_     :						Union[str, Any]     =					["""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__":
						lowerCamelCase							:List[Any]       = Accelerator()
						lowerCamelCase							:int       = (accelerator.state.process_index + 2, 1_0)
						lowerCamelCase							:str       = torch.randint(0, 1_0, shape).to(accelerator.device)

						lowerCamelCase							:str       = ''''''

						lowerCamelCase							:Union[str, Any]       = 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)."

						lowerCamelCase							:Tuple       = 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."
						lowerCamelCase							:Tuple       = 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)




'''simple docstring'''




import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch







@require_torch
class      _lowerCAmelCase							:


	@property
	def    _a     (self							):
				return self.get_dummy_input()


	@property
	def    _a     (self							):
				if self.block_type == "down":
							return (4, 32, 16, 16)
				elif self.block_type == "mid":
							return (4, 32, 32, 32)
				elif self.block_type == "up":
							return (4, 32, 64, 64)

				raise ValueError(F'\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'							)


	def    _a     (self	,				lowercase=True	,				lowercase=False	,				lowercase=False	,				lowercase=False	,				):
				A_     :						List[str]     =					4
				A_     :						int     =					32
				A_     :						Optional[int]     =					(32, 32)

				A_     :						Optional[Any]     =					torch.manual_seed(0							)
				A_     :						int     =					torch.device(lowercase							)
				A_     :						int     =					(batch_size, num_channels) + sizes
				A_     :						Optional[int]     =					randn_tensor(lowercase	,				generator=lowercase	,				device=lowercase							)
				A_     :						Union[str, Any]     =					{"""hidden_states""": hidden_states}

				if include_temb:
							A_     :						str     =					128
							A_     :						List[Any]     =					randn_tensor((batch_size, temb_channels)	,				generator=lowercase	,				device=lowercase							)

				if include_res_hidden_states_tuple:
							A_     :						List[str]     =					torch.manual_seed(1							)
							A_     :						int     =					(randn_tensor(lowercase	,				generator=lowercase	,				device=lowercase							),)

				if include_encoder_hidden_states:
							A_     :						List[str]     =					floats_tensor((batch_size, 32, 32)							).to(lowercase							)

				if include_skip_sample:
							A_     :						Dict     =					randn_tensor(((batch_size, 3) + sizes)	,				generator=lowercase	,				device=lowercase							)

				return dummy_input


	def    _a     (self							):
				A_     :						Tuple     =					{
				    """in_channels""": 32,
				    """out_channels""": 32,
				    """temb_channels""": 128,
				}
				if self.block_type == "up":
							A_     :						Any     =					32

				if self.block_type == "mid":
							init_dict.pop("""out_channels"""							)

				A_     :						Optional[int]     =					self.dummy_input
				return init_dict, inputs_dict


	def    _a     (self	,				lowercase							):
				A_,   A_     :						Optional[Any]     =					self.prepare_init_args_and_inputs_for_common()
				A_     :						int     =					self.block_class(**lowercase							)
				unet_block.to(lowercase							)
				unet_block.eval()

				with torch.no_grad():
							A_     :						List[str]     =					unet_block(**lowercase							)

				if isinstance(lowercase	,				lowercase							):
							A_     :						Union[str, Any]     =					output[0]

				self.assertEqual(output.shape	,				self.output_shape							)

				A_     :						int     =					output[0, -1, -3:, -3:]
				A_     :						List[Any]     =					torch.tensor(lowercase							).to(lowercase							)
				assert torch_all_close(output_slice.flatten()	,				lowercase	,				atol=5E-3							)


	@unittest.skipIf(torch_device == """mps"""	,				"""Training is not supported in mps"""							)
	def    _a     (self							):
				A_,   A_     :						Tuple     =					self.prepare_init_args_and_inputs_for_common()
				A_     :						List[str]     =					self.block_class(**lowercase							)
				model.to(lowercase							)
				model.train()
				A_     :						Any     =					model(**lowercase							)

				if isinstance(lowercase	,				lowercase							):
							A_     :						str     =					output[0]

				A_     :						Union[str, Any]     =					torch.device(lowercase							)
				A_     :						Tuple     =					randn_tensor(output.shape	,				device=lowercase							)
				A_     :						List[str]     =					torch.nn.functional.mse_loss(lowercase	,				lowercase							)
				loss.backward()