





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput




@dataclass
class 			__UpperCamelCase    (							A__    ):
     __A	:  torch.FloatTensor
     __A	:  torch.FloatTensor




class 			__UpperCamelCase    (							A__	, A__    ):
     __A	:  Dict							  =   1





     @register_to_config
     def __init__(     self				,							_UpperCamelCase = 2000				,							_UpperCamelCase = 0.15				,							_UpperCamelCase = 0.01				,							_UpperCamelCase = 1348.0				,							_UpperCamelCase = 1e-5				,							_UpperCamelCase = 1				,							):
        # standard deviation of the initial noise distribution
        _UpperCAmelCase           =   sigma_max

        # setable values
        _UpperCAmelCase           =   None

        self.set_sigmas(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None					):
        return sample





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = None					):
        _UpperCAmelCase           =   sampling_eps if sampling_eps is not None else self.config.sampling_eps

        _UpperCAmelCase           =   torch.linspace(1				,							_UpperCamelCase				,							_UpperCamelCase				,							device=_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None					):
        _UpperCAmelCase           =   sigma_min if sigma_min is not None else self.config.sigma_min
        _UpperCAmelCase           =   sigma_max if sigma_max is not None else self.config.sigma_max
        _UpperCAmelCase           =   sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if self.timesteps is None:
           self.set_timesteps(_UpperCamelCase				,							_UpperCamelCase					)

        _UpperCAmelCase           =   sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
        _UpperCAmelCase           =   torch.exp(torch.linspace(math.log(_UpperCamelCase					)				,							math.log(_UpperCamelCase					)				,							_UpperCamelCase					)					)
        _UpperCAmelCase           =   torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase					):
        return torch.where(
            timesteps == 0				,							torch.zeros_like(t.to(timesteps.device					)					)				,							self.discrete_sigmas[timesteps - 1].to(timesteps.device					)				,							)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = True				,							):
        if self.timesteps is None:
           raise ValueError(
               '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''					)

        _UpperCAmelCase           =   timestep * torch.ones(
            sample.shape[0]				,							device=sample.device					)  # torch.repeat_interleave(timestep, sample.shape[0])
        _UpperCAmelCase           =   (timestep * (len(self.timesteps					) - 1)).long()

        # mps requires indices to be in the same device, so we use cpu as is the default with cuda
        _UpperCAmelCase           =   timesteps.to(self.discrete_sigmas.device					)

        _UpperCAmelCase           =   self.discrete_sigmas[timesteps].to(sample.device					)
        _UpperCAmelCase           =   self.get_adjacent_sigma(_UpperCamelCase				,							_UpperCamelCase					).to(sample.device					)
        _UpperCAmelCase           =   torch.zeros_like(_UpperCamelCase					)
        _UpperCAmelCase           =   (sigma**2 - adjacent_sigma**2) ** 0.5

        # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
        # also equation 47 shows the analog from SDE models to ancestral sampling methods
        _UpperCAmelCase           =   diffusion.flatten()
        while len(diffusion.shape					) < len(sample.shape					):
           _UpperCAmelCase           =   diffusion.unsqueeze(-1					)
        _UpperCAmelCase           =   drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        _UpperCAmelCase           =   randn_tensor(
            sample.shape				,							layout=sample.layout				,							generator=_UpperCamelCase				,							device=sample.device				,							dtype=sample.dtype					)
        _UpperCAmelCase           =   sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        _UpperCAmelCase           =   prev_sample_mean + diffusion * noise  # add impact of diffusion field g

        if not return_dict:
           return (prev_sample, prev_sample_mean)

        return SdeVeOutput(prev_sample=_UpperCamelCase				,							prev_sample_mean=_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = True				,							):
        if self.timesteps is None:
           raise ValueError(
               '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''					)

        # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
        # sample noise for correction
        _UpperCAmelCase           =   randn_tensor(sample.shape				,							layout=sample.layout				,							generator=_UpperCamelCase					).to(sample.device					)

        # compute step size from the model_output, the noise, and the snr
        _UpperCAmelCase           =   torch.norm(model_output.reshape(model_output.shape[0]				,							-1					)				,							dim=-1					).mean()
        _UpperCAmelCase           =   torch.norm(noise.reshape(noise.shape[0]				,							-1					)				,							dim=-1					).mean()
        _UpperCAmelCase           =   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
        _UpperCAmelCase           =   step_size * torch.ones(sample.shape[0]					).to(sample.device					)
        # self.repeat_scalar(step_size, sample.shape[0])

        # compute corrected sample: model_output term and noise term
        _UpperCAmelCase           =   step_size.flatten()
        while len(step_size.shape					) < len(sample.shape					):
           _UpperCAmelCase           =   step_size.unsqueeze(-1					)
        _UpperCAmelCase           =   sample + step_size * model_output
        _UpperCAmelCase           =   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
           return (prev_sample,)

        return SchedulerOutput(prev_sample=_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							):
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        _UpperCAmelCase           =   timesteps.to(original_samples.device					)
        _UpperCAmelCase           =   self.discrete_sigmas.to(original_samples.device					)[timesteps]
        _UpperCAmelCase           =   (
            noise * sigmas[:, None, None, None]
            if noise is not None
            else torch.randn_like(_UpperCamelCase					) * sigmas[:, None, None, None]
        )
        _UpperCAmelCase           =   noise + original_samples
        return noisy_samples





     def __len__(     self					):
        return self.config.num_train_timesteps



'''simple docstring'''
import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')


def      a (     __a  )    ->      List[str]:



					'''simple docstring'''






					UpperCamelCase__  :int				   =					test_file.split(os.path.sep  )
					if components[0:2] != ["tests", "models"]:
										raise ValueError(
										    '''`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '''
										    f'''{test_file} instead.'''  )
					UpperCamelCase__  :List[Any]				   =					components[-1]
					if not test_fn.endswith('''py'''  ):
										raise ValueError(f'''`test_file` should be a python file. Got {test_fn} instead.'''  )
					if not test_fn.startswith('''test_modeling_'''  ):
										raise ValueError(
										    f'''`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.'''  )

					UpperCamelCase__  :Optional[Any]				   =					components[:-1] + [test_fn.replace('''.py'''					,   ''''''  )]
					UpperCamelCase__  :str				   =					'''.'''.join(__a  )

					return test_module_path


def      a (     __a  )    ->      Tuple:



					'''simple docstring'''






					UpperCamelCase__  :Dict				   =					get_module_path(__a  )
					UpperCamelCase__  :List[str]				   =					importlib.import_module(__a  )

					return test_module


def      a (     __a  )    ->      int:



					'''simple docstring'''






					UpperCamelCase__  :Dict				   =					[]
					UpperCamelCase__  :Optional[Any]				   =					get_test_module(__a  )
					for attr in dir(__a  ):
										if attr.endswith('''ModelTester'''  ):
															tester_classes.append(getattr(__a					,   __a  )  )

    # sort with class names
					return sorted(__a					,   key=lambda __a  : x.__name__  )


def      a (     __a  )    ->      List[Any]:



					'''simple docstring'''






					UpperCamelCase__  :List[Any]				   =					[]
					UpperCamelCase__  :Dict				   =					get_test_module(__a  )
					for attr in dir(__a  ):
										UpperCamelCase__  :Dict				   =					getattr(__a					,   __a  )
										# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
										# `all_model_classes` is not empty (which also excludes other special classes).
										UpperCamelCase__  :Optional[int]				   =					getattr(__a					,   '''all_model_classes'''					,   []  )
										if len(__a  ) > 0:
															test_classes.append(__a  )

    # sort with class names
					return sorted(__a					,   key=lambda __a  : x.__name__  )


def      a (     __a  )    ->      Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__  :int				   =					get_test_classes(__a  )
					UpperCamelCase__  :Any				   =					set()
					for test_class in test_classes:
										model_classes.update(test_class.all_model_classes  )

					# sort with class names
					return sorted(__a					,   key=lambda __a  : x.__name__  )


def      a (     __a  )    ->      Dict:



					'''simple docstring'''






					UpperCamelCase__  :Optional[Any]				   =					test_class()
					if hasattr(__a					,   '''setUp'''  ):
										test.setUp()

					UpperCamelCase__  :Union[str, Any]				   =					None
					if hasattr(__a					,   '''model_tester'''  ):
										# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
										if test.model_tester is not None:
															UpperCamelCase__  :List[str]				   =					test.model_tester.__class__

					return model_tester


def      a (     __a					,   __a  )    ->      Any:



					'''simple docstring'''






					UpperCamelCase__  :str				   =					get_test_classes(__a  )

					UpperCamelCase__  :List[str]				   =					[]
					for test_class in test_classes:
										if model_class in test_class.all_model_classes:
															target_test_classes.append(__a  )

    # sort with class names
					return sorted(__a					,   key=lambda __a  : x.__name__  )


def      a (     __a					,   __a  )    ->      Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__  :Optional[int]				   =					get_test_classes_for_model(__a					,   __a  )

					UpperCamelCase__  :List[str]				   =					[]
					for test_class in test_classes:
										UpperCamelCase__  :int				   =					get_model_tester_from_test_class(__a  )
										if tester_class is not None:
															tester_classes.append(__a  )

    # sort with class names
					return sorted(__a					,   key=lambda __a  : x.__name__  )


def      a (     __a  )    ->      int:



					'''simple docstring'''






					UpperCamelCase__  :Any				   =					get_test_classes(__a  )
					UpperCamelCase__  :Dict				   =					{test_class: get_model_tester_from_test_class(__a  ) for test_class in test_classes}
					return test_tester_mapping


def      a (     __a  )    ->      List[str]:



					'''simple docstring'''






					UpperCamelCase__  :Any				   =					get_model_classes(__a  )
					UpperCamelCase__  :Tuple				   =					{
					    model_class: get_test_classes_for_model(__a					,   __a  ) for model_class in model_classes
					}
					return model_test_mapping


def      a (     __a  )    ->      Tuple:



					'''simple docstring'''






					UpperCamelCase__  :List[str]				   =					get_model_classes(__a  )
					UpperCamelCase__  :Union[str, Any]				   =					{
					    model_class: get_tester_classes_for_model(__a					,   __a  ) for model_class in model_classes
					}
					return model_to_tester_mapping







def      a (     __a  )    ->      List[Any]:



					'''simple docstring'''






					if isinstance(__a					,   __a  ):
										return o
					elif isinstance(__a					,   __a  ):
										return o.__name__
					elif isinstance(__a					,   (list, tuple)  ):
										return [to_json(__a  ) for x in o]
					elif isinstance(__a					,   __a  ):
										return {to_json(__a  ): to_json(__a  ) for k, v in o.items()}
					else:
										return o