





'''simple docstring'''

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = (UnCLIPScheduler,)


    def 			snake_case_(		self ,		**A       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			{
            """num_train_timesteps""": 1000,
            """variance_type""": """fixed_small_log""",
            """clip_sample""": True,
            """clip_sample_range""": 1.0,
            """prediction_type""": """epsilon""",
        }

        config.update(**A       )
        return config


    def 			snake_case_(		self       )		->   Union[str, Any]:
        for timesteps in [1, 5, 100, 1000]:
            self.check_over_configs(num_train_timesteps=A       )


    def 			snake_case_(		self       )		->   Union[str, Any]:
        for variance in ["fixed_small_log", "learned_range"]:
            self.check_over_configs(variance_type=A       )


    def 			snake_case_(		self       )		->   str:
        for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=A       )


    def 			snake_case_(		self       )		->   Optional[Any]:
        for clip_sample_range in [1, 5, 10, 20]:
            self.check_over_configs(clip_sample_range=A       )


    def 			snake_case_(		self       )		->   Dict:
        for prediction_type in ["epsilon", "sample"]:
            self.check_over_configs(prediction_type=A       )


    def 			snake_case_(		self       )		->   List[Any]:
        for time_step in [0, 500, 999]:
            for prev_timestep in [None, 5, 100, 250, 500, 750]:
                if prev_timestep is not None and prev_timestep >= time_step:
                    continue

                self.check_over_forward(time_step=A ,		prev_timestep=A       )


    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE									=			self.get_scheduler_config(variance_type="""fixed_small_log"""       )
        _SCREAMING_SNAKE_CASE									=			scheduler_class(**A       )

        assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 1.00_00e-10       )       ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.054_9625       )       ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.999_4987       )       ) < 1e-5


    def 			snake_case_(		self       )		->   int:
        _SCREAMING_SNAKE_CASE									=			self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE									=			self.get_scheduler_config(variance_type="""learned_range"""       )
        _SCREAMING_SNAKE_CASE									=			scheduler_class(**A       )

        _SCREAMING_SNAKE_CASE									=			0.5

        assert scheduler._get_variance(1 ,		predicted_variance=A       ) - -10.171_2790 < 1e-5
        assert scheduler._get_variance(487 ,		predicted_variance=A       ) - -5.799_8052 < 1e-5
        assert scheduler._get_variance(999 ,		predicted_variance=A       ) - -0.001_0011 < 1e-5


    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE									=			self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE									=			scheduler_class(**A       )

        _SCREAMING_SNAKE_CASE									=			scheduler.timesteps

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

        for i, t in enumerate(A       ):
            # 1. predict noise residual
            _SCREAMING_SNAKE_CASE									=			model(A ,		A       )

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

            _SCREAMING_SNAKE_CASE									=			pred_prev_sample

        _SCREAMING_SNAKE_CASE									=			torch.sum(torch.abs(A       )       )
        _SCREAMING_SNAKE_CASE									=			torch.mean(torch.abs(A       )       )

        assert abs(result_sum.item() - 252.268_2495       ) < 1e-2
        assert abs(result_mean.item() - 0.328_4743       ) < 1e-3


    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE									=			self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE									=			scheduler_class(**A       )

        scheduler.set_timesteps(25       )

        _SCREAMING_SNAKE_CASE									=			scheduler.timesteps

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

        for i, t in enumerate(A       ):
            # 1. predict noise residual
            _SCREAMING_SNAKE_CASE									=			model(A ,		A       )

            if i + 1 == timesteps.shape[0]:
                _SCREAMING_SNAKE_CASE									=			None
            else:
                _SCREAMING_SNAKE_CASE									=			timesteps[i + 1]

            # 2. predict previous mean of sample x_t-1
            _SCREAMING_SNAKE_CASE									=			scheduler.step(
                A ,		A ,		A ,		prev_timestep=A ,		generator=A       ).prev_sample

            _SCREAMING_SNAKE_CASE									=			pred_prev_sample

        _SCREAMING_SNAKE_CASE									=			torch.sum(torch.abs(A       )       )
        _SCREAMING_SNAKE_CASE									=			torch.mean(torch.abs(A       )       )

        assert abs(result_sum.item() - 258.204_4983       ) < 1e-2
        assert abs(result_mean.item() - 0.336_2038       ) < 1e-3


    def 			snake_case_(		self       )		->   List[str]:
        pass





    def 			snake_case_(		self       )		->   str:
        pass










'''simple docstring'''

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          ImageTextPipelineOutput,
          UniDiffuserPipeline,
      )
else:
      from .modeling_text_decoder import UniDiffuserTextDecoder
      from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
      from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline



