





'''simple docstring'''

import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = (EulerDiscreteScheduler,)
    UpperCamelCase     = 10


    def 			snake_case_(		self ,		**A       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			{
            """num_train_timesteps""": 1100,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
        }

        config.update(**A       )
        return config


    def 			snake_case_(		self       )		->   Optional[Any]:
        for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=A       )


    def 			snake_case_(		self       )		->   Optional[int]:
        for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] ,		[0.0002, 0.002, 0.02]       ):
            self.check_over_configs(beta_start=A ,		beta_end=A       )


    def 			snake_case_(		self       )		->   Union[str, Any]:
        for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=A       )


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


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

        scheduler.set_timesteps(self.num_inference_steps       )

        _SCREAMING_SNAKE_CASE									=			torch.manual_seed(0       )

        _SCREAMING_SNAKE_CASE									=			self.dummy_model()
        _SCREAMING_SNAKE_CASE									=			self.dummy_sample_deter * scheduler.init_noise_sigma
        _SCREAMING_SNAKE_CASE									=			sample.to(A       )

        for i, t in enumerate(scheduler.timesteps       ):
            _SCREAMING_SNAKE_CASE									=			scheduler.scale_model_input(A ,		A       )

            _SCREAMING_SNAKE_CASE									=			model(A ,		A       )

            _SCREAMING_SNAKE_CASE									=			scheduler.step(A ,		A ,		A ,		generator=A       )
            _SCREAMING_SNAKE_CASE									=			output.prev_sample

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

        assert abs(result_sum.item() - 10.0807       ) < 1e-2
        assert abs(result_mean.item() - 0.0131       ) < 1e-3


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

        scheduler.set_timesteps(self.num_inference_steps       )

        _SCREAMING_SNAKE_CASE									=			torch.manual_seed(0       )

        _SCREAMING_SNAKE_CASE									=			self.dummy_model()
        _SCREAMING_SNAKE_CASE									=			self.dummy_sample_deter * scheduler.init_noise_sigma
        _SCREAMING_SNAKE_CASE									=			sample.to(A       )

        for i, t in enumerate(scheduler.timesteps       ):
            _SCREAMING_SNAKE_CASE									=			scheduler.scale_model_input(A ,		A       )

            _SCREAMING_SNAKE_CASE									=			model(A ,		A       )

            _SCREAMING_SNAKE_CASE									=			scheduler.step(A ,		A ,		A ,		generator=A       )
            _SCREAMING_SNAKE_CASE									=			output.prev_sample

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

        assert abs(result_sum.item() - 0.0002       ) < 1e-2
        assert abs(result_mean.item() - 2.26_76e-06       ) < 1e-3


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

        scheduler.set_timesteps(self.num_inference_steps ,		device=A       )

        _SCREAMING_SNAKE_CASE									=			torch.manual_seed(0       )

        _SCREAMING_SNAKE_CASE									=			self.dummy_model()
        _SCREAMING_SNAKE_CASE									=			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        _SCREAMING_SNAKE_CASE									=			sample.to(A       )

        for t in scheduler.timesteps:
            _SCREAMING_SNAKE_CASE									=			scheduler.scale_model_input(A ,		A       )

            _SCREAMING_SNAKE_CASE									=			model(A ,		A       )

            _SCREAMING_SNAKE_CASE									=			scheduler.step(A ,		A ,		A ,		generator=A       )
            _SCREAMING_SNAKE_CASE									=			output.prev_sample

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

        assert abs(result_sum.item() - 10.0807       ) < 1e-2
        assert abs(result_mean.item() - 0.0131       ) < 1e-3





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

        scheduler.set_timesteps(self.num_inference_steps ,		device=A       )

        _SCREAMING_SNAKE_CASE									=			torch.manual_seed(0       )

        _SCREAMING_SNAKE_CASE									=			self.dummy_model()
        _SCREAMING_SNAKE_CASE									=			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        _SCREAMING_SNAKE_CASE									=			sample.to(A       )

        for t in scheduler.timesteps:
            _SCREAMING_SNAKE_CASE									=			scheduler.scale_model_input(A ,		A       )

            _SCREAMING_SNAKE_CASE									=			model(A ,		A       )

            _SCREAMING_SNAKE_CASE									=			scheduler.step(A ,		A ,		A ,		generator=A       )
            _SCREAMING_SNAKE_CASE									=			output.prev_sample

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

        assert abs(result_sum.item() - 124.52_2994_9951_1719       ) < 1e-2
        assert abs(result_mean.item() - 0.1_6213_9326_3339_9963       ) < 1e-3










'''simple docstring'''

from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset


class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def 			snake_case_(		self       )		->   Tuple:
        return [
            {"col_1": 3, "col_2": "a"},
            {"col_1": 2, "col_2": "b"},
            {"col_1": 1, "col_2": "c"},
            {"col_1": 0, "col_2": "d"},
        ]


    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			{"""col_1""": [3, 2, 1, 0], """col_2""": ["""a""", """b""", """c""", """d"""]}
        return Dataset.from_dict(A       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self._create_example_records()
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        self.assertListEqual(dset.column_names ,		["""col_1""", """col_2"""]       )
        for i, r in enumerate(A       ):
            self.assertDictEqual(A ,		example_records[i]       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self._create_example_records()
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        _SCREAMING_SNAKE_CASE									=			Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}       )
        self.assertEqual(dset.info ,		dset_from_dict.info       )


    def 			snake_case_(		self       )		->   Union[str, Any]:  # checks what happens with missing columns
        _SCREAMING_SNAKE_CASE									=			[{"""col_1""": 1}, {"""col_2""": """x"""}]
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        self.assertDictEqual(dset[0] ,		{"""col_1""": 1}       )
        self.assertDictEqual(dset[1] ,		{"""col_1""": None}       )  # NB: first record is used for columns


    def 			snake_case_(		self       )		->   Optional[Any]:  # checks if the type can be inferred from the second record
        _SCREAMING_SNAKE_CASE									=			[{"""col_1""": []}, {"""col_1""": [1, 2]}]
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        self.assertEqual(dset.info.features["""col_1"""] ,		Sequence(Value("""int64"""       )       )       )





    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list([]       )
        self.assertEqual(len(A       ) ,		0       )
        self.assertListEqual(dset.column_names ,		[]       )



