
"""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       ):



     def  lowerCamelCase_						(							self :Any   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=inspect.getfile(accelerate.test_utils   )
      UpperCamelCase_     :					Union[str, Any]   	=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_     :					Optional[Any]   	=os.path.sep.join(mod_file.split(os.path.sep   )[:-1] + ['scripts', 'test_ops.py']   )



     @require_multi_gpu
     def  lowerCamelCase_						(							self :Any   ):







      '''simple docstring'''



      print(f'''Found {torch.cuda.device_count()} devices.'''   )
      UpperCamelCase_     :					str   	=['''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  lowerCamelCase_						(							self :int   ):







      '''simple docstring'''



      print(f'''Found {torch.cuda.device_count()} devices.'''   )
      UpperCamelCase_     :					Optional[Any]   	=['''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  lowerCamelCase_						(							self :Optional[int]   ):







      '''simple docstring'''



      UpperCamelCase_     :					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  lowerCamelCase_						(							self :Any   ):







      '''simple docstring'''



      print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only'''   )
      UpperCamelCase_     :					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__":
 __SCREAMING_SNAKE_CASE							=     Accelerator()
 __SCREAMING_SNAKE_CASE							=     (accelerator.state.process_index + 2, 10)
 __SCREAMING_SNAKE_CASE							=     torch.randint(0, 10, shape).to(accelerator.device)

 __SCREAMING_SNAKE_CASE							=     ''

 __SCREAMING_SNAKE_CASE							=     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)."

 __SCREAMING_SNAKE_CASE							=     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."
 __SCREAMING_SNAKE_CASE							=     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 importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=False      )		->     Optional[Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							Optional[Any]	    =	OmegaConf.load(lowercase_      )
						if display:
												print(yaml.dump(OmegaConf.to_container(lowercase_      )      )      )
						return config




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=None      ,       lowercase_=None      )		->     Union[str, Any]:




						'''simple docstring'''







						if conf_path is None:
												__UpperCAmelCase			:							Optional[int]	    =	'''./model_checkpoints/vqgan_only.yaml'''
						__UpperCAmelCase			:							Dict	    =	load_config(lowercase_      ,       display=lowercase_      )
						__UpperCAmelCase			:							Tuple	    =	VQModel(**config.model.params      )
						if ckpt_path is None:
												__UpperCAmelCase			:							Dict	    =	'''./model_checkpoints/vqgan_only.pt'''
						__UpperCAmelCase			:							int	    =	torch.load(lowercase_      ,       map_location=lowercase_      )
						if ".ckpt" in ckpt_path:
												__UpperCAmelCase			:							Union[str, Any]	    =	sd['''state_dict''']
						model.load_state_dict(lowercase_      ,       strict=lowercase_      )
						model.to(lowercase_      )
						del sd
						return model




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     List[Any]:




						'''simple docstring'''







						__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[Any]	    =	model.encode(lowercase_      )
						print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"      )
						__UpperCAmelCase			:							List[str]	    =	model.decode(lowercase_      )
						return xrec




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=False      )		->     int:




						'''simple docstring'''







						__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[int]	    =	string.rsplit('''.'''      ,       1      )
						if reload:
												__UpperCAmelCase			:							Optional[int]	    =	importlib.import_module(lowercase_      )
												importlib.reload(lowercase_      )
						return getattr(importlib.import_module(lowercase_      ,       package=lowercase_      )      ,       cls      )




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     Union[str, Any]:




						'''simple docstring'''







						if "target" not in config:
												raise KeyError('''Expected key `target` to instantiate.'''      )
						return get_obj_from_str(config['''target''']      )(**config.get('''params'''      ,       {}      )      )




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_=True      ,       lowercase_=True      )		->     int:




						'''simple docstring'''







						__UpperCAmelCase			:							Dict	    =	instantiate_from_config(lowercase_      )
						if sd is not None:
												model.load_state_dict(lowercase_      )
						if gpu:
												model.cuda()
						if eval_mode:
												model.eval()
						return {"model": model}





def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      )		->     Optional[Any]:




						'''simple docstring'''







						if ckpt:
												__UpperCAmelCase			:							Optional[int]	    =	torch.load(lowercase_      ,       map_location='''cpu'''      )
												__UpperCAmelCase			:							Union[str, Any]	    =	pl_sd['''global_step''']
												print(f"loaded model from global step {global_step}."      )
						else:
												__UpperCAmelCase			:							Union[str, Any]	    =	{'''state_dict''': None}
												__UpperCAmelCase			:							Optional[int]	    =	None
						__UpperCAmelCase			:							str	    =	load_model_from_config(config.model      ,       pl_sd['''state_dict''']      ,       gpu=lowercase_      ,       eval_mode=lowercase_      )['''model''']
						return model, global_step

