"""simple docstring"""







import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class  UpperCamelCase	(    _A						):
   lowercase							=						(DDPMParallelScheduler,)
   def     _UpperCAmelCase     (    self  ,**__UpperCamelCase    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_			:       int									=			{
            "num_train_timesteps": 1000,
            "beta_start": 0.0001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
            "variance_type": "fixed_small",
            "clip_sample": True,
        }

        config.update(**__lowerCamelCase    )
        return config
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        for timesteps in [1, 5, 100, 1000]:
             self.check_over_configs(num_train_timesteps=__lowerCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   List[str]:


        '''simple docstring'''


        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]  ,[0.002, 0.02, 0.2, 2]    ):
             self.check_over_configs(beta_start=__lowerCamelCase  ,beta_end=__lowerCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=__lowerCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        for variance in ["fixed_small", "fixed_large", "other"]:
             self.check_over_configs(variance_type=__lowerCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=__lowerCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   List[str]:


        '''simple docstring'''


        self.check_over_configs(thresholding=__lowerCamelCase    )
        for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "sample", "v_prediction"]:
                  self.check_over_configs(
                      thresholding=__lowerCamelCase  ,prediction_type=__lowerCamelCase  ,sample_max_value=__lowerCamelCase  ,)
   def     _UpperCAmelCase     (    self    )			->   Any:


        '''simple docstring'''


        for prediction_type in ["epsilon", "sample", "v_prediction"]:
             self.check_over_configs(prediction_type=__lowerCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   List[str]:


        '''simple docstring'''


        for t in [0, 500, 999]:
             self.check_over_forward(time_step=__lowerCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        lowercase_			:       int									=			self.scheduler_classes[0]
        lowercase_			:       str									=			self.get_scheduler_config()
        lowercase_			:       int									=			scheduler_class(**__lowerCamelCase    )

        assert torch.sum(torch.abs(scheduler._get_variance(0    ) - 0.0    )    ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487    ) - 0.0_0979    )    ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999    ) - 0.02    )    ) < 1e-5
   def     _UpperCAmelCase     (    self    )			->   Tuple:


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			self.scheduler_classes[0]
        lowercase_			:       Union[str, Any]									=			self.get_scheduler_config()
        lowercase_			:       Any									=			scheduler_class(**__lowerCamelCase    )

        lowercase_			:       Dict									=			len(__lowerCamelCase    )

        lowercase_			:       List[Any]									=			self.dummy_model()
        lowercase_			:       Optional[int]									=			self.dummy_sample_deter
        lowercase_			:       List[Any]									=			self.dummy_sample_deter + 0.1
        lowercase_			:       Dict									=			self.dummy_sample_deter - 0.1

        lowercase_			:       Optional[int]									=			samplea.shape[0]
        lowercase_			:       List[str]									=			torch.stack([samplea, samplea, samplea]  ,dim=0    )
        lowercase_			:       int									=			torch.arange(__lowerCamelCase    )[0:3, None].repeat(1  ,__lowerCamelCase    )

        lowercase_			:       Optional[int]									=			model(samples.flatten(0  ,1    )  ,timesteps.flatten(0  ,1    )    )
        lowercase_			:       List[str]									=			scheduler.batch_step_no_noise(__lowerCamelCase  ,timesteps.flatten(0  ,1    )  ,samples.flatten(0  ,1    )    )

        lowercase_			:       Optional[Any]									=			torch.sum(torch.abs(__lowerCamelCase    )    )
        lowercase_			:       Optional[int]									=			torch.mean(torch.abs(__lowerCamelCase    )    )

        assert abs(result_sum.item() - 1153.1833    ) < 1e-2
        assert abs(result_mean.item() - 0.5005    ) < 1e-3
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			self.scheduler_classes[0]
        lowercase_			:       Union[str, Any]									=			self.get_scheduler_config()
        lowercase_			:       Tuple									=			scheduler_class(**__lowerCamelCase    )

        lowercase_			:       Any									=			len(__lowerCamelCase    )

        lowercase_			:       Dict									=			self.dummy_model()
        lowercase_			:       Optional[int]									=			self.dummy_sample_deter
        lowercase_			:       Dict									=			torch.manual_seed(0    )

        for t in reversed(range(__lowerCamelCase    )    ):
             # 1. predict noise residual
             lowercase_			:       List[str]									=			model(__lowerCamelCase  ,__lowerCamelCase    )

             # 2. predict previous mean of sample x_t-1
             lowercase_			:       Tuple									=			scheduler.step(__lowerCamelCase  ,__lowerCamelCase  ,__lowerCamelCase  ,generator=__lowerCamelCase    ).prev_sample

             lowercase_			:       Union[str, Any]									=			pred_prev_sample

        lowercase_			:       int									=			torch.sum(torch.abs(__lowerCamelCase    )    )
        lowercase_			:       Union[str, Any]									=			torch.mean(torch.abs(__lowerCamelCase    )    )

        assert abs(result_sum.item() - 258.9606    ) < 1e-2
        assert abs(result_mean.item() - 0.3372    ) < 1e-3
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        lowercase_			:       Tuple									=			self.scheduler_classes[0]
        lowercase_			:       Tuple									=			self.get_scheduler_config(prediction_type='v_prediction'    )
        lowercase_			:       Optional[int]									=			scheduler_class(**__lowerCamelCase    )

        lowercase_			:       Union[str, Any]									=			len(__lowerCamelCase    )

        lowercase_			:       str									=			self.dummy_model()
        lowercase_			:       Tuple									=			self.dummy_sample_deter
        lowercase_			:       int									=			torch.manual_seed(0    )

        for t in reversed(range(__lowerCamelCase    )    ):
             # 1. predict noise residual
             lowercase_			:       int									=			model(__lowerCamelCase  ,__lowerCamelCase    )

             # 2. predict previous mean of sample x_t-1
             lowercase_			:       List[Any]									=			scheduler.step(__lowerCamelCase  ,__lowerCamelCase  ,__lowerCamelCase  ,generator=__lowerCamelCase    ).prev_sample

             lowercase_			:       List[str]									=			pred_prev_sample

        lowercase_			:       Optional[Any]									=			torch.sum(torch.abs(__lowerCamelCase    )    )
        lowercase_			:       Optional[int]									=			torch.mean(torch.abs(__lowerCamelCase    )    )

        assert abs(result_sum.item() - 202.0296    ) < 1e-2
        assert abs(result_mean.item() - 0.2631    ) < 1e-3
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       Any									=			self.scheduler_classes[0]
        lowercase_			:       Optional[int]									=			self.get_scheduler_config()
        lowercase_			:       Tuple									=			scheduler_class(**__lowerCamelCase    )

        lowercase_			:       Dict									=			[100, 87, 50, 1, 0]

        scheduler.set_timesteps(timesteps=__lowerCamelCase    )

        lowercase_			:       Optional[Any]									=			scheduler.timesteps

        for i, timestep in enumerate(__lowerCamelCase    ):
             if i == len(__lowerCamelCase    ) - 1:
                  lowercase_			:       List[Any]									=			-1
             else:
                  lowercase_			:       Optional[int]									=			timesteps[i + 1]

             lowercase_			:       Optional[Any]									=			scheduler.previous_timestep(__lowerCamelCase    )
             lowercase_			:       Optional[int]									=			prev_t.item()

             self.assertEqual(__lowerCamelCase  ,__lowerCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			self.scheduler_classes[0]
        lowercase_			:       Dict									=			self.get_scheduler_config()
        lowercase_			:       str									=			scheduler_class(**__lowerCamelCase    )

        lowercase_			:       Dict									=			[100, 87, 50, 51, 0]

        with self.assertRaises(__lowerCamelCase  ,msg='`custom_timesteps` must be in descending order.'    ):
             scheduler.set_timesteps(timesteps=__lowerCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   List[str]:


        '''simple docstring'''


        lowercase_			:       List[str]									=			self.scheduler_classes[0]
        lowercase_			:       Tuple									=			self.get_scheduler_config()
        lowercase_			:       Tuple									=			scheduler_class(**__lowerCamelCase    )

        lowercase_			:       Any									=			[100, 87, 50, 1, 0]
        lowercase_			:       Optional[Any]									=			len(__lowerCamelCase    )

        with self.assertRaises(__lowerCamelCase  ,msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'    ):
             scheduler.set_timesteps(num_inference_steps=__lowerCamelCase  ,timesteps=__lowerCamelCase    )







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


        '''simple docstring'''


        lowercase_			:       List[Any]									=			self.scheduler_classes[0]
        lowercase_			:       Dict									=			self.get_scheduler_config()
        lowercase_			:       int									=			scheduler_class(**__lowerCamelCase    )

        lowercase_			:       List[Any]									=			[scheduler.config.num_train_timesteps]

        with self.assertRaises(
            __lowerCamelCase  ,msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'  ,):
             scheduler.set_timesteps(timesteps=__lowerCamelCase    )


"""simple docstring"""







import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)







class  UpperCamelCase	(    nn.Module						):
   def __init__(    self    )			->   Any:


        '''simple docstring'''


        super().__init__()
        lowercase_			:       Dict									=			nn.Linear(3  ,4    )
        lowercase_			:       Union[str, Any]									=			nn.BatchNormad(4    )
        lowercase_			:       Union[str, Any]									=			nn.Linear(4  ,5    )







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   int:


        '''simple docstring'''


        return self.lineara(self.batchnorm(self.lineara(__UpperCamelCase    )    )    )







class  UpperCamelCase	(    unittest.TestCase						):
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       int									=			ModelForTest()
        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase  ,model.state_dict()    )
             lowercase_			:       str									=			os.path.join(__UpperCamelCase  ,'index.json'    )
             self.assertTrue(os.path.isfile(__UpperCamelCase    )    )
             # TODO: add tests on what is inside the index

             for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
                  lowercase_			:       int									=			os.path.join(__UpperCamelCase  ,f'''{key}.dat'''    )
                  self.assertTrue(os.path.isfile(__UpperCamelCase    )    )
                  # TODO: add tests on the fact weights are properly loaded
   def     _UpperCAmelCase     (    self    )			->   Tuple:


        '''simple docstring'''


        lowercase_			:       List[str]									=			[torch.floataa, torch.floataa, torch.bfloataa]

        for dtype in dtypes:
             lowercase_			:       Tuple									=			torch.randn(2  ,3  ,dtype=__UpperCamelCase    )
             with TemporaryDirectory() as tmp_dir:
                  lowercase_			:       List[str]									=			offload_weight(__UpperCamelCase  ,'weight'  ,__UpperCamelCase  ,{}    )
                  lowercase_			:       str									=			os.path.join(__UpperCamelCase  ,'weight.dat'    )
                  self.assertTrue(os.path.isfile(__UpperCamelCase    )    )
                  self.assertDictEqual(__UpperCamelCase  ,{'weight': {'shape': [2, 3], 'dtype': str(__UpperCamelCase    ).split('.'    )[1]}}    )

                  lowercase_			:       Optional[Any]									=			load_offloaded_weight(__UpperCamelCase  ,index['weight']    )
                  self.assertTrue(torch.equal(__UpperCamelCase  ,__UpperCamelCase    )    )
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       List[str]									=			ModelForTest()
        lowercase_			:       Optional[int]									=			model.state_dict()
        lowercase_			:       int									=			{k: v for k, v in state_dict.items() if 'linear2' not in k}
        lowercase_			:       Optional[Any]									=			{k: v for k, v in state_dict.items() if 'linear2' in k}

        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase  ,__UpperCamelCase    )
             lowercase_			:       Tuple									=			OffloadedWeightsLoader(state_dict=__UpperCamelCase  ,save_folder=__UpperCamelCase    )

             # Every key is there with the right value
             self.assertEqual(sorted(__UpperCamelCase    )  ,sorted(state_dict.keys()    )    )
             for key, param in state_dict.items():
                  self.assertTrue(torch.allclose(__UpperCamelCase  ,weight_map[key]    )    )

        lowercase_			:       int									=			{k: v for k, v in state_dict.items() if 'weight' in k}
        lowercase_			:       int									=			{k: v for k, v in state_dict.items() if 'weight' not in k}

        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase  ,__UpperCamelCase    )
             lowercase_			:       int									=			OffloadedWeightsLoader(state_dict=__UpperCamelCase  ,save_folder=__UpperCamelCase    )

             # Every key is there with the right value
             self.assertEqual(sorted(__UpperCamelCase    )  ,sorted(state_dict.keys()    )    )
             for key, param in state_dict.items():
                  self.assertTrue(torch.allclose(__UpperCamelCase  ,weight_map[key]    )    )

        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase  ,__UpperCamelCase    )
             # Duplicates are removed
             lowercase_			:       Tuple									=			OffloadedWeightsLoader(state_dict=__UpperCamelCase  ,save_folder=__UpperCamelCase    )

             # Every key is there with the right value
             self.assertEqual(sorted(__UpperCamelCase    )  ,sorted(state_dict.keys()    )    )
             for key, param in state_dict.items():
                  self.assertTrue(torch.allclose(__UpperCamelCase  ,weight_map[key]    )    )







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


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			{'a.1': 0, 'a.10': 1, 'a.2': 2}
        lowercase_			:       int									=			extract_submodules_state_dict(__UpperCamelCase  ,['a.1', 'a.2']    )
        self.assertDictEqual(__UpperCamelCase  ,{'a.1': 0, 'a.2': 2}    )

        lowercase_			:       Union[str, Any]									=			{'a.1.a': 0, 'a.10.a': 1, 'a.2.a': 2}
        lowercase_			:       Optional[int]									=			extract_submodules_state_dict(__UpperCamelCase  ,['a.1', 'a.2']    )
        self.assertDictEqual(__UpperCamelCase  ,{'a.1.a': 0, 'a.2.a': 2}    )

