

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

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



class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   Any	,							lowerCAmelCase			:   List[Any]   )							->     str:






     """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
        __lowerCAmelCase  :      Union[str, Any]    =						deepcopy(lowerCAmelCase   )
     elif os.path.exists(lowerCAmelCase   ):
        with io.open(lowerCAmelCase	,							"""r"""	,							encoding="""utf-8"""   ) as f:
           __lowerCAmelCase  :      str    =						json.load(lowerCAmelCase   )
     else:
        try:
           __lowerCAmelCase  :      List[Any]    =						baseaa.urlsafe_baadecode(lowerCAmelCase   ).decode("""utf-8"""   )
           __lowerCAmelCase  :      str    =						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}'''   )

     __lowerCAmelCase  :      Tuple    =						config

     self.set_stage_and_offload()







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						self.get_value("""zero_optimization.stage"""	,							-1   )

     # offload
     __lowerCAmelCase  :      int    =						False
     if self.is_zeroa() or self.is_zeroa():
        __lowerCAmelCase  :      Optional[int]    =						set(["""cpu""", """nvme"""]   )
        __lowerCAmelCase  :      int    =						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:
           __lowerCAmelCase  :      Any    =						True







  def 	SCREAMING_SNAKE_CASE   (      self			:   str	,							lowerCAmelCase			:   str   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						self.config

     # find the config node of interest if it exists
     __lowerCAmelCase  :      int    =						ds_key_long.split("""."""   )
     __lowerCAmelCase  :      Optional[int]    =						nodes.pop()
     for node in nodes:
        __lowerCAmelCase  :      int    =						config.get(lowerCAmelCase   )
        if config is None:
           return None, ds_key

     return config, ds_key







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   Union[str, Any]=None   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						self.find_config_node(lowerCAmelCase   )
     if config is None:
        return default
     return config.get(lowerCAmelCase	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Optional[int]=False   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.config

     # find the config node of interest if it exists
     __lowerCAmelCase  :      Dict    =						ds_key_long.split("""."""   )
     for node in nodes:
        __lowerCAmelCase  :      Tuple    =						config
        __lowerCAmelCase  :      Dict    =						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[str]	,							lowerCAmelCase			:   Union[str, Any]   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.get_value(lowerCAmelCase   )
     return False if value is None else bool(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]	,							lowerCAmelCase			:   str   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.get_value(lowerCAmelCase   )
     return False if value is None else not bool(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Tuple:






     """simple docstring"""







     return self._stage == 2







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     Tuple:






     """simple docstring"""







     return self._stage == 3







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Any:






     """simple docstring"""







     return self._offload






class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   int	,							lowerCAmelCase			:   Optional[int]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						engine







  def 	SCREAMING_SNAKE_CASE   (      self			:   int	,							lowerCAmelCase			:   List[Any]	,							**lowerCAmelCase			:   List[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 SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  def __init__(      self			:   List[str]	,							lowerCAmelCase			:   Tuple   )							->     Tuple:






     """simple docstring"""







     super().__init__(lowerCAmelCase	,							device_placement=lowerCAmelCase	,							scaler=lowerCAmelCase   )
     __lowerCAmelCase  :      int    =						hasattr(self.optimizer	,							"""overflow"""   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   List[Any]=None   )							->     List[str]:






     """simple docstring"""







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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Union[str, Any]:






     """simple docstring"""







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







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






     """simple docstring"""







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






class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






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






     """simple docstring"""







     super().__init__(lowerCAmelCase	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     Any:






     """simple docstring"""







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






class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






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






     """simple docstring"""







     __lowerCAmelCase  :      int    =						params
     __lowerCAmelCase  :      List[Any]    =						lr
     __lowerCAmelCase  :      Any    =						weight_decay
     __lowerCAmelCase  :      List[str]    =						kwargs






class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






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






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						optimizer
     __lowerCAmelCase  :      Optional[Any]    =						total_num_steps
     __lowerCAmelCase  :      Tuple    =						warmup_num_steps
     __lowerCAmelCase  :      Union[str, Any]    =						kwargs





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      Dict										=(EulerDiscreteScheduler,)
  lowerCamelCase   :      Dict										=10







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							**lowerCAmelCase			:   Union[str, Any]   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						{
         """num_train_timesteps""": 11_00,
         """beta_start""": 0.0001,
         """beta_end""": 0.02,
         """beta_schedule""": """linear""",
     }

     config.update(**lowerCAmelCase   )
     return config







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






     """simple docstring"""







     for timesteps in [10, 50, 1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Tuple:






     """simple docstring"""







     for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,							[0.0002, 0.002, 0.02]   ):
        self.check_over_configs(beta_start=lowerCAmelCase	,							beta_end=lowerCAmelCase   )







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






     """simple docstring"""







     for schedule in ["linear", "scaled_linear"]:
        self.check_over_configs(beta_schedule=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     List[Any]:






     """simple docstring"""







     for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      int    =						self.get_scheduler_config()
     __lowerCAmelCase  :      Any    =						scheduler_class(**lowerCAmelCase   )

     scheduler.set_timesteps(self.num_inference_steps   )

     __lowerCAmelCase  :      str    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      List[Any]    =						self.dummy_model()
     __lowerCAmelCase  :      Dict    =						self.dummy_sample_deter * scheduler.init_noise_sigma
     __lowerCAmelCase  :      int    =						sample.to(lowerCAmelCase   )

     for i, t in enumerate(scheduler.timesteps   ):
        __lowerCAmelCase  :      Tuple    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Dict    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      str    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      Optional[int]    =						output.prev_sample

     __lowerCAmelCase  :      str    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      Optional[int]    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 10.0807   ) < 1e-2
     assert abs(result_mean.item() - 0.0131   ) < 1e-3







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






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      List[Any]    =						self.get_scheduler_config(prediction_type="""v_prediction"""   )
     __lowerCAmelCase  :      List[str]    =						scheduler_class(**lowerCAmelCase   )

     scheduler.set_timesteps(self.num_inference_steps   )

     __lowerCAmelCase  :      Any    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      int    =						self.dummy_model()
     __lowerCAmelCase  :      List[str]    =						self.dummy_sample_deter * scheduler.init_noise_sigma
     __lowerCAmelCase  :      List[Any]    =						sample.to(lowerCAmelCase   )

     for i, t in enumerate(scheduler.timesteps   ):
        __lowerCAmelCase  :      Any    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Any    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Dict    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      Dict    =						output.prev_sample

     __lowerCAmelCase  :      List[str]    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      Optional[int]    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 0.0002   ) < 1e-2
     assert abs(result_mean.item() - 2.2676e-06   ) < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      Dict    =						self.get_scheduler_config()
     __lowerCAmelCase  :      Optional[int]    =						scheduler_class(**lowerCAmelCase   )

     scheduler.set_timesteps(self.num_inference_steps	,							device=lowerCAmelCase   )

     __lowerCAmelCase  :      Union[str, Any]    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      Dict    =						self.dummy_model()
     __lowerCAmelCase  :      Dict    =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
     __lowerCAmelCase  :      Dict    =						sample.to(lowerCAmelCase   )

     for t in scheduler.timesteps:
        __lowerCAmelCase  :      Union[str, Any]    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      List[str]    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      int    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      Any    =						output.prev_sample

     __lowerCAmelCase  :      int    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      str    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 10.0807   ) < 1e-2
     assert abs(result_mean.item() - 0.0131   ) < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      Optional[int]    =						self.get_scheduler_config()
     __lowerCAmelCase  :      List[Any]    =						scheduler_class(**lowerCAmelCase	,							use_karras_sigmas=lowerCAmelCase   )

     scheduler.set_timesteps(self.num_inference_steps	,							device=lowerCAmelCase   )

     __lowerCAmelCase  :      str    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      str    =						self.dummy_model()
     __lowerCAmelCase  :      str    =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
     __lowerCAmelCase  :      int    =						sample.to(lowerCAmelCase   )

     for t in scheduler.timesteps:
        __lowerCAmelCase  :      int    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Dict    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Tuple    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      List[Any]    =						output.prev_sample

     __lowerCAmelCase  :      Tuple    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      Any    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 124.52_2994_9951_1719   ) < 1e-2
     assert abs(result_mean.item() - 0.1_6213_9326_3339_9963   ) < 1e-3


