
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		(				UpperCAmelCase_		:	Optional[Any]				,						UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	Dict				,						UpperCAmelCase_		:	Dict=None		):




      """simple docstring"""





      a		:int											= 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.
            a		:Optional[int]											= weight.view(torch.intaa		)
            a		:str											= '''bfloat16'''
      a		:Tuple											= weight.cpu().numpy()
      a		:Union[str, Any]											= os.path.join(UpperCAmelCase_				,						F'''{weight_name}.dat'''		)
      if index is not None:
            if dtype is None:
                  a		:List[str]											= str(array.dtype		)
            a		:Dict											= {'''dtype''': dtype, '''shape''': list(array.shape		)}
      if array.ndim == 0:
            a		:Dict											= array[None]
      a		:List[Any]											= np.memmap(UpperCAmelCase_				,						dtype=array.dtype				,						mode='''w+'''				,						shape=array.shape		)
      a		:List[str]											= array[:]
      file_array.flush()
      return index
def        __lowerCamelCase		(				UpperCAmelCase_		:	Tuple				,						UpperCAmelCase_		:	Tuple		):




      """simple docstring"""





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

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

      a		:str											= np.memmap(UpperCAmelCase_				,						dtype=UpperCAmelCase_				,						shape=UpperCAmelCase_				,						mode='''r'''		)

      if len(weight_info['''shape''']		) == 0:
            a		:str											= weight[0]
      a		:List[Any]											= torch.tensor(UpperCAmelCase_		)
      if weight_info["dtype"] == "bfloat16":
            a		:Dict											= weight.view(torch.bfloataa		)

      return weight
def        __lowerCamelCase		(				UpperCAmelCase_		:	int				,						UpperCAmelCase_		:	Optional[Any]		):




      """simple docstring"""





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

      a		:Union[str, Any]											= os.path.join(UpperCAmelCase_				,						'''index.json'''		)
      if os.path.isfile(UpperCAmelCase_		):
            with open(UpperCAmelCase_				,						'''r'''				,						encoding='''utf-8'''		) as f:
                  a		:List[Any]											= json.load(UpperCAmelCase_		)
      else:
            a		:int											= {}
      current_index.update(UpperCAmelCase_		)

      with open(UpperCAmelCase_				,						'''w'''				,						encoding='''utf-8'''		) as f:
            json.dump(UpperCAmelCase_				,						UpperCAmelCase_				,						indent=2		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	Union[str, os.PathLike]				,						UpperCAmelCase_		:	Dict[str, torch.Tensor]		):




      """simple docstring"""





      os.makedirs(UpperCAmelCase_				,						exist_ok=UpperCAmelCase_		)
      a		:Tuple											= {}
      for name, parameter in state_dict.items():
            a		:List[str]											= offload_weight(UpperCAmelCase_				,						UpperCAmelCase_				,						UpperCAmelCase_				,						index=UpperCAmelCase_		)

      # Update index
      save_offload_index(UpperCAmelCase_				,						UpperCAmelCase_		)







class 	_snake_case						(   _snake_case							):




 def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase					):
       a		:Union[str, Any]											= dataset
       a		:Dict											= prefix




 def __getitem__(	self      ,						_lowerCamelCase					):
       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 	_snake_case						(   _snake_case							):




 def __init__(	self      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase=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.'''					)

       a		:Tuple											= {} if state_dict is None else state_dict
       a		:str											= save_folder
       if index is None and save_folder is not None:
             with open(os.path.join(_lowerCamelCase      ,						'''index.json'''					)					) as f:
                   a		:int											= json.load(_lowerCamelCase					)
       a		:Tuple											= {} if index is None else index
       a		:List[Any]											= list(self.state_dict.keys()					)
       self.all_keys.extend([key for key in self.index if key not in self.all_keys]					)
       a		:Optional[int]											= device




 def __getitem__(	self      ,						_lowerCamelCase					):
       # State dict gets priority
       if key in self.state_dict:
             return self.state_dict[key]
       a		:List[Any]											= 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

             a		:List[Any]											= '''cpu''' if self.device is None else self.device
             with safe_open(weight_info['''safetensors_file''']      ,						framework='''pt'''      ,						device=_lowerCamelCase					) as f:
                   a		:Optional[Any]											= f.get_tensor(weight_info.get('''weight_name'''      ,						_lowerCamelCase					)					)

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

       a		:str											= os.path.join(self.save_folder      ,						F'''{key}.dat'''					)
       return load_offloaded_weight(_lowerCamelCase      ,						_lowerCamelCase					)




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




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







def        __lowerCamelCase		(				UpperCAmelCase_		:	Dict[str, torch.Tensor]				,						UpperCAmelCase_		:	List[str]		):




      """simple docstring"""





      a		:Any											= {}
      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









import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler







class 	_snake_case						(   unittest.TestCase							):
 # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
 SCREAMING_SNAKE_CASE__   = (ScoreSdeVeScheduler,)
 SCREAMING_SNAKE_CASE__   = ()




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Tuple											= 4
       a		:List[str]											= 3
       a		:Any											= 8
       a		:Any											= 8

       a		:List[str]											= torch.rand((batch_size, num_channels, height, width)					)

       return sample




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:int											= 4
       a		:Union[str, Any]											= 3
       a		:Optional[Any]											= 8
       a		:Tuple											= 8

       a		:Dict											= batch_size * num_channels * height * width
       a		:Optional[int]											= torch.arange(_lowerCamelCase					)
       a		:List[Any]											= sample.reshape(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					)
       a		:Union[str, Any]											= sample / num_elems
       a		:Dict											= sample.permute(3      ,						0      ,						1      ,						2					)

       return sample




 def  SCREAMING_SNAKE_CASE__       (	self					):
       def model(_lowerCamelCase      ,						_lowerCamelCase      ,						*_lowerCamelCase					):
             return sample * t / (t + 1)

       return model




 def  SCREAMING_SNAKE_CASE__       (	self      ,						**_lowerCamelCase					):
       a		:str											= {
           '''num_train_timesteps''': 2000,
           '''snr''': 0.15,
           '''sigma_min''': 0.01,
           '''sigma_max''': 1348,
           '''sampling_eps''': 1e-5,
       }

       config.update(**_lowerCamelCase					)
       return config




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase=0      ,						**_lowerCamelCase					):
       a		:Optional[int]											= dict(self.forward_default_kwargs					)

       for scheduler_class in self.scheduler_classes:
             a		:Union[str, Any]											= self.dummy_sample
             a		:Tuple											= 0.1 * sample

             a		:Union[str, Any]											= self.get_scheduler_config(**_lowerCamelCase					)
             a		:Union[str, Any]											= scheduler_class(**_lowerCamelCase					)

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_lowerCamelCase					)
                   a		:Optional[int]											= scheduler_class.from_pretrained(_lowerCamelCase					)

             a		:List[str]											= scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Dict											= new_scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

             a		:int											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Optional[Any]											= new_scheduler.step_correct(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler correction are not identical"




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase=0      ,						**_lowerCamelCase					):
       a		:Dict											= dict(self.forward_default_kwargs					)
       kwargs.update(_lowerCamelCase					)

       for scheduler_class in self.scheduler_classes:
             a		:int											= self.dummy_sample
             a		:str											= 0.1 * sample

             a		:Optional[Any]											= self.get_scheduler_config()
             a		:Union[str, Any]											= scheduler_class(**_lowerCamelCase					)

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_lowerCamelCase					)
                   a		:Union[str, Any]											= scheduler_class.from_pretrained(_lowerCamelCase					)

             a		:Union[str, Any]											= scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Optional[int]											= new_scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

             a		:List[str]											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Tuple											= new_scheduler.step_correct(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler correction are not identical"




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for timesteps in [10, 100, 1000]:
             self.check_over_configs(num_train_timesteps=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]      ,						[1, 100, 1000]					):
             self.check_over_configs(sigma_min=_lowerCamelCase      ,						sigma_max=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for t in [0.1, 0.5, 0.75]:
             self.check_over_forward(time_step=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Dict											= dict(self.forward_default_kwargs					)

       a		:Optional[Any]											= self.scheduler_classes[0]
       a		:Union[str, Any]											= self.get_scheduler_config()
       a		:List[Any]											= scheduler_class(**_lowerCamelCase					)

       a		:Union[str, Any]											= 3

       a		:List[Any]											= self.dummy_model()
       a		:Dict											= self.dummy_sample_deter

       scheduler.set_sigmas(_lowerCamelCase					)
       scheduler.set_timesteps(_lowerCamelCase					)
       a		:str											= torch.manual_seed(0					)

       for i, t in enumerate(scheduler.timesteps					):
             a		:Optional[int]											= scheduler.sigmas[i]

             for _ in range(scheduler.config.correct_steps					):
                   with torch.no_grad():
                         a		:Dict											= model(_lowerCamelCase      ,						_lowerCamelCase					)
                   a		:int											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=_lowerCamelCase      ,						**_lowerCamelCase					).prev_sample

             with torch.no_grad():
                   a		:Any											= model(_lowerCamelCase      ,						_lowerCamelCase					)

             a		:Optional[Any]											= scheduler.step_pred(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=_lowerCamelCase      ,						**_lowerCamelCase					)
             a							,       a		:Any											= output.prev_sample, output.prev_sample_mean

       a		:Dict											= torch.sum(torch.abs(_lowerCamelCase					)					)
       a		:int											= torch.mean(torch.abs(_lowerCamelCase					)					)

       assert np.isclose(result_sum.item()      ,						143_7275_8528.0					)
       assert np.isclose(result_mean.item()      ,						1871_4530.0					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:str											= dict(self.forward_default_kwargs					)

       a		:Union[str, Any]											= kwargs.pop('''num_inference_steps'''      ,						_lowerCamelCase					)

       for scheduler_class in self.scheduler_classes:
             a		:Optional[int]											= self.get_scheduler_config()
             a		:str											= scheduler_class(**_lowerCamelCase					)

             a		:List[str]											= self.dummy_sample
             a		:List[Any]											= 0.1 * sample

             if num_inference_steps is not None and hasattr(_lowerCamelCase      ,						'''set_timesteps'''					):
                   scheduler.set_timesteps(_lowerCamelCase					)
             elif num_inference_steps is not None and not hasattr(_lowerCamelCase      ,						'''set_timesteps'''					):
                   a		:Dict											= num_inference_steps

             a		:Tuple											= scheduler.step_pred(_lowerCamelCase      ,						0      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Tuple											= scheduler.step_pred(_lowerCamelCase      ,						1      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             self.assertEqual(output_a.shape      ,						sample.shape					)
             self.assertEqual(output_a.shape      ,						output_a.shape					)





