






import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				(KDPMaDiscreteScheduler,)
     SCREAMING_SNAKE_CASE   						=				10




     def 		lowerCAmelCase_     (				self       :			Dict ,	**UpperCamelCase_       :			Optional[int]				):


         """simple docstring"""



         __A         =	{
             """num_train_timesteps""": 1_100,
             """beta_start""": 0.0001,
             """beta_end""": 0.02,
             """beta_schedule""": """linear""",
         }

         config.update(**UpperCamelCase_				)
         return config




     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         for timesteps in [10, 50, 100, 1_000]:
             self.check_over_configs(num_train_timesteps=UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Union[str, Any]				):


         """simple docstring"""



         for beta_start, beta_end in zip([0.00001, 0.0001, 0.001] ,	[0.0002, 0.002, 0.02]				):
             self.check_over_configs(beta_start=UpperCamelCase_ ,	beta_end=UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



         for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			List[Any]				):


         """simple docstring"""



         __A         =	self.scheduler_classes[0]
         __A         =	self.get_scheduler_config(prediction_type="""v_prediction"""				)
         __A         =	scheduler_class(**UpperCamelCase_				)

         scheduler.set_timesteps(self.num_inference_steps				)

         __A         =	self.dummy_model()
         __A         =	self.dummy_sample_deter * scheduler.init_noise_sigma
         __A         =	sample.to(UpperCamelCase_				)

         for i, t in enumerate(scheduler.timesteps				):
             __A         =	scheduler.scale_model_input(UpperCamelCase_ ,	UpperCamelCase_				)

             __A         =	model(UpperCamelCase_ ,	UpperCamelCase_				)

             __A         =	scheduler.step(UpperCamelCase_ ,	UpperCamelCase_ ,	UpperCamelCase_				)
             __A         =	output.prev_sample

         __A         =	torch.sum(torch.abs(UpperCamelCase_				)				)
         __A         =	torch.mean(torch.abs(UpperCamelCase_				)				)

         if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 4.69_34e-07				) < 1e-2
             assert abs(result_mean.item() - 6.11_12e-10				) < 1e-3
         else:
             # CUDA
             assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07				) < 1e-2
             assert abs(result_mean.item() - 0.0002				) < 1e-3




     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



         if torch_device == "mps":
             return
         __A         =	self.scheduler_classes[0]
         __A         =	self.get_scheduler_config()
         __A         =	scheduler_class(**UpperCamelCase_				)

         scheduler.set_timesteps(self.num_inference_steps				)

         __A         =	self.dummy_model()
         __A         =	self.dummy_sample_deter * scheduler.init_noise_sigma
         __A         =	sample.to(UpperCamelCase_				)

         for i, t in enumerate(scheduler.timesteps				):
             __A         =	scheduler.scale_model_input(UpperCamelCase_ ,	UpperCamelCase_				)

             __A         =	model(UpperCamelCase_ ,	UpperCamelCase_				)

             __A         =	scheduler.step(UpperCamelCase_ ,	UpperCamelCase_ ,	UpperCamelCase_				)
             __A         =	output.prev_sample

         __A         =	torch.sum(torch.abs(UpperCamelCase_				)				)
         __A         =	torch.mean(torch.abs(UpperCamelCase_				)				)

         if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 20.4125				) < 1e-2
             assert abs(result_mean.item() - 0.0266				) < 1e-3
         else:
             # CUDA
             assert abs(result_sum.item() - 20.4125				) < 1e-2
             assert abs(result_mean.item() - 0.0266				) < 1e-3





     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



         if torch_device == "mps":
             return
         __A         =	self.scheduler_classes[0]
         __A         =	self.get_scheduler_config()
         __A         =	scheduler_class(**UpperCamelCase_				)

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

         __A         =	self.dummy_model()
         __A         =	self.dummy_sample_deter.to(UpperCamelCase_				) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
             __A         =	scheduler.scale_model_input(UpperCamelCase_ ,	UpperCamelCase_				)

             __A         =	model(UpperCamelCase_ ,	UpperCamelCase_				)

             __A         =	scheduler.step(UpperCamelCase_ ,	UpperCamelCase_ ,	UpperCamelCase_				)
             __A         =	output.prev_sample

         __A         =	torch.sum(torch.abs(UpperCamelCase_				)				)
         __A         =	torch.mean(torch.abs(UpperCamelCase_				)				)

         if str(UpperCamelCase_				).startswith("""cpu"""				):
             # The following sum varies between 148 and 156 on mps. Why?
             assert abs(result_sum.item() - 20.4125				) < 1e-2
             assert abs(result_mean.item() - 0.0266				) < 1e-3
         else:
             # CUDA
             assert abs(result_sum.item() - 20.4125				) < 1e-2
             assert abs(result_mean.item() - 0.0266				) < 1e-3












import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


__a      :       List[str]				  =   pytest.mark.integration



@pytest.mark.parametrize("""path"""			,				["""paws""", """csv"""]    )
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			Optional[int]    )						->      Tuple:





    """simple docstring"""

    inspect_dataset(__lowercase			,				__lowercase    )
    __A         =	path + """.py"""
    assert script_name in os.listdir(__lowercase    )
    assert "__pycache__" not in os.listdir(__lowercase    )



@pytest.mark.filterwarnings("""ignore:inspect_metric is deprecated:FutureWarning"""    )
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""    )
@pytest.mark.parametrize("""path"""			,				["""accuracy"""]    )
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]			,				__lowercase   :			Tuple    )						->      Any:





    """simple docstring"""

    inspect_metric(__lowercase			,				__lowercase    )
    __A         =	path + """.py"""
    assert script_name in os.listdir(__lowercase    )
    assert "__pycache__" not in os.listdir(__lowercase    )



@pytest.mark.parametrize(
    """path, config_name, expected_splits"""			,				[
        ("""squad""", """plain_text""", ["""train""", """validation"""]),
        ("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
        ("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Dict			,				__lowercase   :			Optional[int]			,				__lowercase   :			List[Any]    )						->      Optional[Any]:





    """simple docstring"""

    __A         =	get_dataset_config_info(__lowercase			,				config_name=__lowercase    )
    assert info.config_name == config_name
    assert list(info.splits.keys()    ) == expected_splits



@pytest.mark.parametrize(
    """path, config_name, expected_exception"""			,				[
        ("""paws""", None, ValueError),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			int			,				__lowercase   :			List[str]    )						->      int:





    """simple docstring"""

    with pytest.raises(__lowercase    ):
        get_dataset_config_info(__lowercase			,				config_name=__lowercase    )



@pytest.mark.parametrize(
    """path, expected"""			,				[
        ("""squad""", """plain_text"""),
        ("""acronym_identification""", """default"""),
        ("""lhoestq/squad""", """plain_text"""),
        ("""lhoestq/test""", """default"""),
        ("""lhoestq/demo1""", """lhoestq--demo1"""),
        ("""dalle-mini/wit""", """dalle-mini--wit"""),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Tuple			,				__lowercase   :			Optional[Any]    )						->      Any:





    """simple docstring"""

    __A         =	get_dataset_config_names(__lowercase    )
    assert expected in config_names



@pytest.mark.parametrize(
    """path, expected_configs, expected_splits_in_first_config"""			,				[
        ("""squad""", ["""plain_text"""], ["""train""", """validation"""]),
        ("""dalle-mini/wit""", ["""dalle-mini--wit"""], ["""train"""]),
        ("""paws""", ["""labeled_final""", """labeled_swap""", """unlabeled_final"""], ["""train""", """test""", """validation"""]),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			int			,				__lowercase   :			Tuple			,				__lowercase   :			List[Any]    )						->      List[str]:





    """simple docstring"""

    __A         =	get_dataset_infos(__lowercase    )
    assert list(infos.keys()    ) == expected_configs
    __A         =	expected_configs[0]
    assert expected_config in infos
    __A         =	infos[expected_config]
    assert info.config_name == expected_config
    assert list(info.splits.keys()    ) == expected_splits_in_first_config



@pytest.mark.parametrize(
    """path, expected_config, expected_splits"""			,				[
        ("""squad""", """plain_text""", ["""train""", """validation"""]),
        ("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
        ("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]			,				__lowercase   :			str			,				__lowercase   :			Union[str, Any]    )						->      Dict:





    """simple docstring"""

    __A         =	get_dataset_infos(__lowercase    )
    assert expected_config in infos
    __A         =	infos[expected_config]
    assert info.config_name == expected_config
    assert list(info.splits.keys()    ) == expected_splits






@pytest.mark.parametrize(
    """path, config_name, expected_exception"""			,				[
        ("""paws""", None, ValueError),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[str]			,				__lowercase   :			Optional[int]			,				__lowercase   :			Tuple    )						->      Union[str, Any]:





    """simple docstring"""

    with pytest.raises(__lowercase    ):
        get_dataset_split_names(__lowercase			,				config_name=__lowercase    )




