





"""simple docstring"""






import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=None				)		->							Tuple:






       """simple docstring"""




       _UpperCAmelCase			     =  None
       # Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
       if str(weight.dtype				) == "torch.bfloat16":
              # Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
              _UpperCAmelCase			     =  weight.view(torch.intaa				)
              _UpperCAmelCase			     =  'bfloat16'
       _UpperCAmelCase			     =  weight.cpu().numpy()
       _UpperCAmelCase			     =  os.path.join(SCREAMING_SNAKE_CASE,F"""{weight_name}.dat"""				)
       if index is not None:
              if dtype is None:
                     _UpperCAmelCase			     =  str(array.dtype				)
              _UpperCAmelCase			     =  {'dtype': dtype, 'shape': list(array.shape				)}
       if array.ndim == 0:
              _UpperCAmelCase			     =  array[None]
       _UpperCAmelCase			     =  np.memmap(SCREAMING_SNAKE_CASE,dtype=array.dtype,mode='w+',shape=array.shape				)
       _UpperCAmelCase			     =  array[:]
       file_array.flush()
       return index

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							List[Any]:






       """simple docstring"""




       _UpperCAmelCase			     =  tuple(weight_info['shape']				)
       if shape == ():
              # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
              _UpperCAmelCase			     =  (1,)

       _UpperCAmelCase			     =  weight_info['dtype']
       if dtype == "bfloat16":
              # NumPy does not support bfloat16 so this was saved as a int16
              _UpperCAmelCase			     =  'int16'

       _UpperCAmelCase			     =  np.memmap(SCREAMING_SNAKE_CASE,dtype=SCREAMING_SNAKE_CASE,shape=SCREAMING_SNAKE_CASE,mode='r'				)

       if len(weight_info['shape']				) == 0:
              _UpperCAmelCase			     =  weight[0]
       _UpperCAmelCase			     =  torch.tensor(SCREAMING_SNAKE_CASE				)
       if weight_info["dtype"] == "bfloat16":
              _UpperCAmelCase			     =  weight.view(torch.bfloataa				)

       return weight

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							Tuple:






       """simple docstring"""




       if index is None or len(SCREAMING_SNAKE_CASE				) == 0:
              # Nothing to save
              return

       _UpperCAmelCase			     =  os.path.join(SCREAMING_SNAKE_CASE,'index.json'				)
       if os.path.isfile(SCREAMING_SNAKE_CASE				):
              with open(SCREAMING_SNAKE_CASE,'r',encoding='utf-8'				) as f:
                     _UpperCAmelCase			     =  json.load(SCREAMING_SNAKE_CASE				)
       else:
              _UpperCAmelCase			     =  {}
       current_index.update(SCREAMING_SNAKE_CASE				)

       with open(SCREAMING_SNAKE_CASE,'w',encoding='utf-8'				) as f:
              json.dump(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,indent=2				)

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							int:






       """simple docstring"""




       os.makedirs(SCREAMING_SNAKE_CASE,exist_ok=SCREAMING_SNAKE_CASE				)
       _UpperCAmelCase			     =  {}
       for name, parameter in state_dict.items():
              _UpperCAmelCase			     =  offload_weight(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,index=SCREAMING_SNAKE_CASE				)

       # Update index
       save_offload_index(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)



class 						lowerCAmelCase       (   snake_case				):






       def __init__( self     ,	a__     ,	a__  ):
              _UpperCAmelCase			     =  dataset
              _UpperCAmelCase			     =  prefix






       def __getitem__( self     ,	a__  ):
              return self.dataset[f"""{self.prefix}{key}"""]






       def __iter__( self  ):
              return iter([key for key in self.dataset if key.startswith(self.prefix  )]  )






       def __len__( self  ):
              return len(self.dataset  )



class 						lowerCAmelCase       (   snake_case				):






       def __init__( self     ,	a__ = None     ,	a__ = None     ,	a__ = None     ,	a__=None     ,	):
              if state_dict is None and save_folder is None:
                     raise ValueError('Need either a `state_dict` or a `save_folder` containing offloaded weights.'  )

              _UpperCAmelCase			     =  {} if state_dict is None else state_dict
              _UpperCAmelCase			     =  save_folder
              if index is None and save_folder is not None:
                     with open(os.path.join(a__     ,	'index.json'  )  ) as f:
                            _UpperCAmelCase			     =  json.load(a__  )
              _UpperCAmelCase			     =  {} if index is None else index
              _UpperCAmelCase			     =  list(self.state_dict.keys()  )
              self.all_keys.extend([key for key in self.index if key not in self.all_keys]  )
              _UpperCAmelCase			     =  device






       def __getitem__( self     ,	a__  ):
              # State dict gets priority
              if key in self.state_dict:
                     return self.state_dict[key]
              _UpperCAmelCase			     =  self.index[key]
              if weight_info.get('safetensors_file'  ) is not None:
                     if not is_safetensors_available():
                            raise ImportError('These offloaded weights require the use of safetensors: `pip install safetensors`.'  )

                     from safetensors import safe_open

                     _UpperCAmelCase			     =  'cpu' if self.device is None else self.device
                     with safe_open(weight_info['safetensors_file']     ,	framework='pt'     ,	device=a__  ) as f:
                            _UpperCAmelCase			     =  f.get_tensor(weight_info.get('weight_name'     ,	a__  )  )

                     if "dtype" in weight_info:
                            return tensor.to(getattr(a__     ,	weight_info['dtype']  )  )
                     else:
                            return tensor

              _UpperCAmelCase			     =  os.path.join(self.save_folder     ,	f"""{key}.dat"""  )
              return load_offloaded_weight(a__     ,	a__  )






       def __iter__( self  ):
              return iter(self.all_keys  )






       def __len__( self  ):
              return len(self.all_keys  )


def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							Optional[int]:






       """simple docstring"""




       _UpperCAmelCase			     =  {}
       for module_name in submodule_names:
              # We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
              # submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
              result.update(
                  {
                      key: param
                      for key, param in state_dict.items()
                      if key == module_name or key.startswith(module_name + '.'				)
                  }				)
       return result













"""simple docstring"""






import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 						lowerCAmelCase       (   snake_case				):
       lowerCAmelCase__    			=	(LMSDiscreteScheduler,)
       lowerCAmelCase__    			=	10






       def 				__A			( self     ,	**a__  ):
              _UpperCAmelCase			     =  {
                  'num_train_timesteps': 11_00,
                  'beta_start': 0.0_001,
                  'beta_end': 0.02,
                  'beta_schedule': 'linear',
              }

              config.update(**a__  )
              return config






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






       def 				__A			( self  ):
              for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]     ,	[0.0_002, 0.002, 0.02]  ):
                     self.check_over_configs(beta_start=a__     ,	beta_end=a__  )






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






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






       def 				__A			( self  ):
              for t in [0, 5_00, 8_00]:
                     self.check_over_forward(time_step=a__  )






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 1_006.388  ) < 1E-2
              assert abs(result_mean.item() - 1.31  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config(prediction_type='v_prediction'  )
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 0.0_017  ) < 1E-2
              assert abs(result_mean.item() - 2.2676E-06  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__  )

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

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
              _UpperCAmelCase			     =  sample.to(a__  )

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 1_006.388  ) < 1E-2
              assert abs(result_mean.item() - 1.31  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__     ,	use_karras_sigmas=a__  )

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

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter.to(a__  ) * scheduler.init_noise_sigma
              _UpperCAmelCase			     =  sample.to(a__  )

              for t in scheduler.timesteps:
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 3_812.9_927  ) < 2E-2
              assert abs(result_mean.item() - 4.9_648  ) < 1E-3






