import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Dict		=(DDPMScheduler,)



     def 			a__  (					self				,      **lowerCAmelCase__       ):
            _A=	{
                '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 			a__  (					self       ):
            for timesteps in [1, 5, 100, 1000]:
                   self.check_over_configs(num_train_timesteps=lowerCAmelCase__       )



     def 			a__  (					self       ):
            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 			a__  (					self       ):
            for schedule in ["linear", "squaredcos_cap_v2"]:
                   self.check_over_configs(beta_schedule=lowerCAmelCase__       )



     def 			a__  (					self       ):
            for variance in ["fixed_small", "fixed_large", "other"]:
                   self.check_over_configs(variance_type=lowerCAmelCase__       )



     def 			a__  (					self       ):
            for clip_sample in [True, False]:
                   self.check_over_configs(clip_sample=lowerCAmelCase__       )



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



     def 			a__  (					self       ):
            for t in [0, 500, 999]:
                   self.check_over_forward(time_step=lowerCAmelCase__       )



     def 			a__  (					self       ):
            _A=	self.scheduler_classes[0]
            _A=	self.get_scheduler_config()
            _A=	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       ) - 0.00979       )       ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.02       )       ) < 1E-5



     def 			a__  (					self       ):
            _A=	self.scheduler_classes[0]
            _A=	self.get_scheduler_config()
            _A=	scheduler_class(**lowerCAmelCase__       )

            _A=	len(lowerCAmelCase__       )

            _A=	self.dummy_model()
            _A=	self.dummy_sample_deter
            _A=	torch.manual_seed(0       )

            for t in reversed(range(lowerCAmelCase__       )       ):
                   # 1. predict noise residual
                   _A=	model(lowerCAmelCase__				,      lowerCAmelCase__       )

                   # 2. predict previous mean of sample x_t-1
                   _A=	scheduler.step(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      generator=lowerCAmelCase__       ).prev_sample

                   # if t > 0:
                   #     noise = self.dummy_sample_deter
                   #     variance = scheduler.get_variance(t) ** (0.5) * noise
                   #
                   # sample = pred_prev_sample + variance
                   _A=	pred_prev_sample

            _A=	torch.sum(torch.abs(lowerCAmelCase__       )       )
            _A=	torch.mean(torch.abs(lowerCAmelCase__       )       )

            assert abs(result_sum.item() - 258.9606       ) < 1E-2
            assert abs(result_mean.item() - 0.3372       ) < 1E-3



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

            _A=	len(lowerCAmelCase__       )

            _A=	self.dummy_model()
            _A=	self.dummy_sample_deter
            _A=	torch.manual_seed(0       )

            for t in reversed(range(lowerCAmelCase__       )       ):
                   # 1. predict noise residual
                   _A=	model(lowerCAmelCase__				,      lowerCAmelCase__       )

                   # 2. predict previous mean of sample x_t-1
                   _A=	scheduler.step(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      generator=lowerCAmelCase__       ).prev_sample

                   # if t > 0:
                   #     noise = self.dummy_sample_deter
                   #     variance = scheduler.get_variance(t) ** (0.5) * noise
                   #
                   # sample = pred_prev_sample + variance
                   _A=	pred_prev_sample

            _A=	torch.sum(torch.abs(lowerCAmelCase__       )       )
            _A=	torch.mean(torch.abs(lowerCAmelCase__       )       )

            assert abs(result_sum.item() - 202.0296       ) < 1E-2
            assert abs(result_mean.item() - 0.2631       ) < 1E-3



     def 			a__  (					self       ):
            _A=	self.scheduler_classes[0]
            _A=	self.get_scheduler_config()
            _A=	scheduler_class(**lowerCAmelCase__       )

            _A=	[100, 87, 50, 1, 0]

            scheduler.set_timesteps(timesteps=lowerCAmelCase__       )

            _A=	scheduler.timesteps

            for i, timestep in enumerate(lowerCAmelCase__       ):
                   if i == len(lowerCAmelCase__       ) - 1:
                          _A=	-1
                   else:
                          _A=	timesteps[i + 1]

                   _A=	scheduler.previous_timestep(lowerCAmelCase__       )
                   _A=	prev_t.item()

                   self.assertEqual(lowerCAmelCase__				,      lowerCAmelCase__       )



     def 			a__  (					self       ):
            _A=	self.scheduler_classes[0]
            _A=	self.get_scheduler_config()
            _A=	scheduler_class(**lowerCAmelCase__       )

            _A=	[100, 87, 50, 51, 0]

            with self.assertRaises(lowerCAmelCase__				,      msg='`custom_timesteps` must be in descending order.'       ):
                   scheduler.set_timesteps(timesteps=lowerCAmelCase__       )



     def 			a__  (					self       ):
            _A=	self.scheduler_classes[0]
            _A=	self.get_scheduler_config()
            _A=	scheduler_class(**lowerCAmelCase__       )

            _A=	[100, 87, 50, 1, 0]
            _A=	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 			a__  (					self       ):
            _A=	self.scheduler_classes[0]
            _A=	self.get_scheduler_config()
            _A=	scheduler_class(**lowerCAmelCase__       )

            _A=	[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__       )
UpperCAmelCase_								=							{
    '''Pillow''': '''Pillow<10.0.0''',
    '''accelerate''': '''accelerate>=0.20.3''',
    '''av''': '''av==9.2.0''',
    '''beautifulsoup4''': '''beautifulsoup4''',
    '''black''': '''black~=23.1''',
    '''codecarbon''': '''codecarbon==1.2.0''',
    '''cookiecutter''': '''cookiecutter==1.7.3''',
    '''dataclasses''': '''dataclasses''',
    '''datasets''': '''datasets!=2.5.0''',
    '''decord''': '''decord==0.6.0''',
    '''deepspeed''': '''deepspeed>=0.9.3''',
    '''diffusers''': '''diffusers''',
    '''dill''': '''dill<0.3.5''',
    '''evaluate''': '''evaluate>=0.2.0''',
    '''fairscale''': '''fairscale>0.3''',
    '''faiss-cpu''': '''faiss-cpu''',
    '''fastapi''': '''fastapi''',
    '''filelock''': '''filelock''',
    '''flax''': '''flax>=0.4.1,<=0.7.0''',
    '''ftfy''': '''ftfy''',
    '''fugashi''': '''fugashi>=1.0''',
    '''GitPython''': '''GitPython<3.1.19''',
    '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
    '''huggingface-hub''': '''huggingface-hub>=0.14.1,<1.0''',
    '''importlib_metadata''': '''importlib_metadata''',
    '''ipadic''': '''ipadic>=1.0.0,<2.0''',
    '''isort''': '''isort>=5.5.4''',
    '''jax''': '''jax>=0.2.8,!=0.3.2,<=0.4.13''',
    '''jaxlib''': '''jaxlib>=0.1.65,<=0.4.13''',
    '''jieba''': '''jieba''',
    '''kenlm''': '''kenlm''',
    '''keras-nlp''': '''keras-nlp>=0.3.1''',
    '''librosa''': '''librosa''',
    '''nltk''': '''nltk''',
    '''natten''': '''natten>=0.14.6''',
    '''numpy''': '''numpy>=1.17''',
    '''onnxconverter-common''': '''onnxconverter-common''',
    '''onnxruntime-tools''': '''onnxruntime-tools>=1.4.2''',
    '''onnxruntime''': '''onnxruntime>=1.4.0''',
    '''opencv-python''': '''opencv-python''',
    '''optuna''': '''optuna''',
    '''optax''': '''optax>=0.0.8,<=0.1.4''',
    '''packaging''': '''packaging>=20.0''',
    '''parameterized''': '''parameterized''',
    '''phonemizer''': '''phonemizer''',
    '''protobuf''': '''protobuf''',
    '''psutil''': '''psutil''',
    '''pyyaml''': '''pyyaml>=5.1''',
    '''pydantic''': '''pydantic<2''',
    '''pytest''': '''pytest>=7.2.0''',
    '''pytest-timeout''': '''pytest-timeout''',
    '''pytest-xdist''': '''pytest-xdist''',
    '''python''': '''python>=3.8.0''',
    '''ray[tune]''': '''ray[tune]''',
    '''regex''': '''regex!=2019.12.17''',
    '''requests''': '''requests''',
    '''rhoknp''': '''rhoknp>=1.1.0,<1.3.1''',
    '''rjieba''': '''rjieba''',
    '''rouge-score''': '''rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1''',
    '''ruff''': '''ruff>=0.0.241,<=0.0.259''',
    '''sacrebleu''': '''sacrebleu>=1.4.12,<2.0.0''',
    '''sacremoses''': '''sacremoses''',
    '''safetensors''': '''safetensors>=0.3.1''',
    '''sagemaker''': '''sagemaker>=2.31.0''',
    '''scikit-learn''': '''scikit-learn''',
    '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
    '''sigopt''': '''sigopt''',
    '''starlette''': '''starlette''',
    '''sudachipy''': '''sudachipy>=0.6.6''',
    '''sudachidict_core''': '''sudachidict_core>=20220729''',
    '''tensorflow-cpu''': '''tensorflow-cpu>=2.6,<2.14''',
    '''tensorflow''': '''tensorflow>=2.6,<2.14''',
    '''tensorflow-text''': '''tensorflow-text<2.14''',
    '''tf2onnx''': '''tf2onnx''',
    '''timeout-decorator''': '''timeout-decorator''',
    '''timm''': '''timm''',
    '''tokenizers''': '''tokenizers>=0.11.1,!=0.11.3,<0.14''',
    '''torch''': '''torch>=1.9,!=1.12.0''',
    '''torchaudio''': '''torchaudio''',
    '''torchvision''': '''torchvision''',
    '''pyctcdecode''': '''pyctcdecode>=0.4.0''',
    '''tqdm''': '''tqdm>=4.27''',
    '''unidic''': '''unidic>=1.0.2''',
    '''unidic_lite''': '''unidic_lite>=1.0.7''',
    '''urllib3''': '''urllib3<2.0.0''',
    '''uvicorn''': '''uvicorn''',
}