





import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler

class 	UpperCamelCase__				:



     '''simple docstring'''

     def __init__(							self		:					List[str]		,lowerCamelCase__		:					Optional[int] )  ->	Optional[int]:




           '''simple docstring'''


           if isinstance(lowerCamelCase__		,lowerCamelCase__ ):
                 # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
                 # modified it, it will not be accepted here again, since `auto` values would have been overridden
                 SCREAMING_SNAKE_CASE	=		deepcopy(lowerCamelCase__ )
           elif os.path.exists(lowerCamelCase__ ):
                 with io.open(lowerCamelCase__		,"""r"""		,encoding="""utf-8""" ) as f:
                       SCREAMING_SNAKE_CASE	=		json.load(lowerCamelCase__ )
           else:
                 try:
                       SCREAMING_SNAKE_CASE	=		baseaa.urlsafe_baadecode(lowerCamelCase__ ).decode("""utf-8""" )
                       SCREAMING_SNAKE_CASE	=		json.loads(lowerCamelCase__ )
                 except (UnicodeDecodeError, AttributeError, ValueError):
                       raise ValueError(
                           F"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}""" )

           SCREAMING_SNAKE_CASE	=		config

           self.set_stage_and_offload()






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.get_value("""zero_optimization.stage"""		,-1 )

           # offload
           SCREAMING_SNAKE_CASE	=		False
           if self.is_zeroa() or self.is_zeroa():
                 SCREAMING_SNAKE_CASE	=		set(["""cpu""", """nvme"""] )
                 SCREAMING_SNAKE_CASE	=		set(
                     [
                         self.get_value("""zero_optimization.offload_optimizer.device""" ),
                         self.get_value("""zero_optimization.offload_param.device""" ),
                     ] )
                 if len(offload_devices & offload_devices_valid ) > 0:
                       SCREAMING_SNAKE_CASE	=		True






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any		,lowerCamelCase__		:					Optional[int] )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.config

           # find the config node of interest if it exists
           SCREAMING_SNAKE_CASE	=		ds_key_long.split(""".""" )
           SCREAMING_SNAKE_CASE	=		nodes.pop()
           for node in nodes:
                 SCREAMING_SNAKE_CASE	=		config.get(lowerCamelCase__ )
                 if config is None:
                       return None, ds_key

           return config, ds_key






     def        SCREAMING_SNAKE_CASE__						(							self		:					str		,lowerCamelCase__		:					int		,lowerCamelCase__		:					Tuple=None )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self.find_config_node(lowerCamelCase__ )
           if config is None:
                 return default
           return config.get(lowerCamelCase__		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					int		,lowerCamelCase__		:					Any		,lowerCamelCase__		:					List[Any]=False )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.config

           # find the config node of interest if it exists
           SCREAMING_SNAKE_CASE	=		ds_key_long.split(""".""" )
           for node in nodes:
                 SCREAMING_SNAKE_CASE	=		config
                 SCREAMING_SNAKE_CASE	=		config.get(lowerCamelCase__ )
                 if config is None:
                       if must_exist:
                             raise ValueError(F"""Can't find {ds_key_long} entry in the config: {self.config}""" )
                       else:
                             return

        # if found remove it
           if parent_config is not None:
                 parent_config.pop(lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any]		,lowerCamelCase__		:					str )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.get_value(lowerCamelCase__ )
           return False if value is None else bool(lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					str		,lowerCamelCase__		:					Optional[Any] )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.get_value(lowerCamelCase__ )
           return False if value is None else not bool(lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	int:




           '''simple docstring'''


           return self._stage == 2






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Optional[int]:




           '''simple docstring'''


           return self._stage == 3






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	str:




           '''simple docstring'''


           return self._offload

class 	UpperCamelCase__				:



     '''simple docstring'''

     def __init__(							self		:					Optional[int]		,lowerCamelCase__		:					Tuple )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		engine






     def        SCREAMING_SNAKE_CASE__						(							self		:					str		,lowerCamelCase__		:					Union[str, Any]		,**lowerCamelCase__		:					Optional[Any] )  ->	List[Any]:




           '''simple docstring'''


           self.engine.backward(lowerCamelCase__		,**lowerCamelCase__ )

           # Deepspeed's `engine.step` performs the following operations:
           # - gradient accumulation check
           # - gradient clipping
           # - optimizer step
           # - zero grad
           # - checking overflow
           # - lr_scheduler step (only if engine.lr_scheduler is not None)
           self.engine.step()
           # and this plugin overrides the above calls with no-ops when Accelerate runs under
           # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
           # training loop that works transparently under many training regimes.

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     def __init__(							self		:					Optional[Any]		,lowerCamelCase__		:					str )  ->	str:




           '''simple docstring'''


           super().__init__(lowerCamelCase__		,device_placement=lowerCamelCase__		,scaler=lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		hasattr(self.optimizer		,"""overflow""" )






     def        SCREAMING_SNAKE_CASE__						(							self		:					int		,lowerCamelCase__		:					Any=None )  ->	Union[str, Any]:




           '''simple docstring'''


           pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Optional[int]:




           '''simple docstring'''


           pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	List[Any]:




           '''simple docstring'''


           if self.__has_overflow__:
                 return self.optimizer.overflow
           return False

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     def __init__(							self		:					Union[str, Any]		,lowerCamelCase__		:					int		,lowerCamelCase__		:					int )  ->	str:




           '''simple docstring'''


           super().__init__(lowerCamelCase__		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	List[Any]:




           '''simple docstring'''


           pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed

class 	UpperCamelCase__				:



     '''simple docstring'''

     def __init__(							self		:					Optional[Any]		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					List[Any]=0.001		,lowerCamelCase__		:					Tuple=0		,**lowerCamelCase__		:					Optional[int] )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		params
           SCREAMING_SNAKE_CASE	=		lr
           SCREAMING_SNAKE_CASE	=		weight_decay
           SCREAMING_SNAKE_CASE	=		kwargs

class 	UpperCamelCase__				:



     '''simple docstring'''

     def __init__(							self		:					int		,lowerCamelCase__		:					Optional[Any]		,lowerCamelCase__		:					Tuple=None		,lowerCamelCase__		:					int=0		,**lowerCamelCase__		:					str )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		optimizer
           SCREAMING_SNAKE_CASE	=		total_num_steps
           SCREAMING_SNAKE_CASE	=		warmup_num_steps
           SCREAMING_SNAKE_CASE	=		kwargs









import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

class 	UpperCamelCase__				(   lowerCAmelCase_							,   lowerCAmelCase_							,   unittest.TestCase     ):



     '''simple docstring'''

     __snake_case					:			str				=    VQModel
     __snake_case					:			Optional[Any]				=    "sample"






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple		,lowerCamelCase__		:					Optional[Any]=(32, 32) )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		4
           SCREAMING_SNAKE_CASE	=		3

           SCREAMING_SNAKE_CASE	=		floats_tensor((batch_size, num_channels) + sizes ).to(lowerCamelCase__ )

           return {"sample": image}






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	List[str]:




           '''simple docstring'''


           return (3, 32, 32)






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	List[str]:




           '''simple docstring'''


           return (3, 32, 32)






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Optional[int]:




           '''simple docstring'''


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






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	str:




           '''simple docstring'''


           pass






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	List[str]:




           '''simple docstring'''


           pass






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		VQModel.from_pretrained("""fusing/vqgan-dummy"""		,output_loading_info=lowerCamelCase__ )
           self.assertIsNotNone(lowerCamelCase__ )
           self.assertEqual(len(loading_info["""missing_keys"""] )		,0 )

           model.to(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		model(**self.dummy_input )

           assert image is not None, "Make sure output is not None"






     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		VQModel.from_pretrained("""fusing/vqgan-dummy""" )
           model.to(lowerCamelCase__ ).eval()

           torch.manual_seed(0 )
           if torch.cuda.is_available():
                 torch.cuda.manual_seed_all(0 )

           SCREAMING_SNAKE_CASE	=		torch.randn(1		,model.config.in_channels		,model.config.sample_size		,model.config.sample_size )
           SCREAMING_SNAKE_CASE	=		image.to(lowerCamelCase__ )
           with torch.no_grad():
                 SCREAMING_SNAKE_CASE	=		model(lowerCamelCase__ ).sample

           SCREAMING_SNAKE_CASE	=		output[0, -1, -3:, -3:].flatten().cpu()
           # fmt: off
           SCREAMING_SNAKE_CASE	=		torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143] )
           # fmt: on
           self.assertTrue(torch.allclose(lowerCamelCase__		,lowerCamelCase__		,atol=1e-3 ) )


