

"""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        _a      (    self	)	->   str:


           """simple docstring"""


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

     @require_multi_gpu
     def        _a      (    self	)	->   Dict:


           """simple docstring"""


           print(f'''Found {torch.cuda.device_count()} devices.'''	)
           SCREAMING_SNAKE_CASE__ :   List[str]   		=						['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
           with patch_environment(omp_num_threads=1	):
                 execute_subprocess_async(UpperCamelCase_						,     env=os.environ.copy()	)

     @require_multi_gpu
     def        _a      (    self	)	->   str:


           """simple docstring"""


           print(f'''Found {torch.cuda.device_count()} devices.'''	)
           SCREAMING_SNAKE_CASE__ :   str   		=						['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(UpperCamelCase_						,     env=os.environ.copy()	)

     @require_multi_gpu
     def        _a      (    self	)	->   Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   int   		=						['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__	)]
           with patch_environment(omp_num_threads=1	):
                 execute_subprocess_async(UpperCamelCase_						,     env=os.environ.copy()	)





     @require_multi_gpu
     def        _a      (    self	)	->   Any:


           """simple docstring"""


           print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only'''	)
           SCREAMING_SNAKE_CASE__ :   Optional[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(UpperCamelCase_						,     env=os.environ.copy()	)


if __name__ == "__main__":
  a       :Optional[Any]     =			Accelerator()
  a       :Any     =			(accelerator.state.process_index + 2, 10)
  a       :Any     =			torch.randint(0, 10, shape).to(accelerator.device)

  a       :Dict     =			""

  a       :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)."

  a       :Any     =			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."
  a       :List[Any]     =			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 torch

from diffusers import StableDiffusionPipeline


UpperCamelCase__          =	'path-to-your-trained-model'
UpperCamelCase__          =	StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')

UpperCamelCase__          =	'A photo of sks dog in a bucket'
UpperCamelCase__          =	pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save('dog-bucket.png')

