





"""simple docstring"""


import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class      lowercase_				(     UpperCamelCase__							):
       snake_case__				:							str											=     (EulerAncestralDiscreteScheduler,)
       snake_case__				:							Optional[int]											=     10

       def 		_SCREAMING_SNAKE_CASE(			self   ,					**__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        snake_case__    							=				{
            "num_train_timesteps": 1100,
            "beta_start": 0.0001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**lowerCAmelCase__     )
        return config

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        for timesteps in [10, 50, 100, 1000]:
         self.check_over_configs(num_train_timesteps=lowerCAmelCase__     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''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=lowerCAmelCase__   ,					beta_end=lowerCAmelCase__     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.scheduler_classes[0]
        snake_case__    							=				self.get_scheduler_config()
        snake_case__    							=				scheduler_class(**lowerCAmelCase__     )

        scheduler.set_timesteps(self.num_inference_steps     )

        snake_case__    							=				torch.manual_seed(0     )

        snake_case__    							=				self.dummy_model()
        snake_case__    							=				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        snake_case__    							=				sample.to(lowerCAmelCase__     )

        for i, t in enumerate(scheduler.timesteps     ):
         snake_case__    							=				scheduler.scale_model_input(lowerCAmelCase__   ,					lowerCAmelCase__     )

         snake_case__    							=				model(lowerCAmelCase__   ,					lowerCAmelCase__     )

         snake_case__    							=				scheduler.step(lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					generator=lowerCAmelCase__     )
         snake_case__    							=				output.prev_sample

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

        assert abs(result_sum.item() - 152.3192     ) < 1e-2
        assert abs(result_mean.item() - 0.1983     ) < 1e-3

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.scheduler_classes[0]
        snake_case__    							=				self.get_scheduler_config(prediction_type="v_prediction"     )
        snake_case__    							=				scheduler_class(**lowerCAmelCase__     )

        scheduler.set_timesteps(self.num_inference_steps     )

        snake_case__    							=				torch.manual_seed(0     )

        snake_case__    							=				self.dummy_model()
        snake_case__    							=				self.dummy_sample_deter * scheduler.init_noise_sigma
        snake_case__    							=				sample.to(lowerCAmelCase__     )

        for i, t in enumerate(scheduler.timesteps     ):
         snake_case__    							=				scheduler.scale_model_input(lowerCAmelCase__   ,					lowerCAmelCase__     )

         snake_case__    							=				model(lowerCAmelCase__   ,					lowerCAmelCase__     )

         snake_case__    							=				scheduler.step(lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					generator=lowerCAmelCase__     )
         snake_case__    							=				output.prev_sample

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

        assert abs(result_sum.item() - 108.4439     ) < 1e-2
        assert abs(result_mean.item() - 0.1412     ) < 1e-3

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.scheduler_classes[0]
        snake_case__    							=				self.get_scheduler_config()
        snake_case__    							=				scheduler_class(**lowerCAmelCase__     )

        scheduler.set_timesteps(self.num_inference_steps   ,					device=lowerCAmelCase__     )
        snake_case__    							=				torch.manual_seed(0     )

        snake_case__    							=				self.dummy_model()
        snake_case__    							=				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        snake_case__    							=				sample.to(lowerCAmelCase__     )

        for t in scheduler.timesteps:
         snake_case__    							=				scheduler.scale_model_input(lowerCAmelCase__   ,					lowerCAmelCase__     )

         snake_case__    							=				model(lowerCAmelCase__   ,					lowerCAmelCase__     )

         snake_case__    							=				scheduler.step(lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					generator=lowerCAmelCase__     )
         snake_case__    							=				output.prev_sample

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

        assert abs(result_sum.item() - 152.3192     ) < 1e-2
        assert abs(result_mean.item() - 0.1983     ) < 1e-3








"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__					:     Tuple							=				logging.get_logger(__name__)

a__					:     str							=				{
    """facebook/regnet-y-040""": """https://huggingface.co/facebook/regnet-y-040/blob/main/config.json""",
}



class      lowercase_				(     __lowerCAmelCase							):
       snake_case__				:							Tuple											=     """regnet"""
       snake_case__				:							List[Any]											=     ["""x""", """y"""]

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE=3   ,					__SCREAMING_SNAKE_CASE=32   ,					__SCREAMING_SNAKE_CASE=[128, 192, 512, 1088]   ,					__SCREAMING_SNAKE_CASE=[2, 6, 12, 2]   ,					__SCREAMING_SNAKE_CASE=64   ,					__SCREAMING_SNAKE_CASE="y"   ,					__SCREAMING_SNAKE_CASE="relu"   ,					**__SCREAMING_SNAKE_CASE   ,					):






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE     )
        if layer_type not in self.layer_types:
         raise ValueError(F"layer_type={layer_type} is not one of {','.join(self.layer_types     )}"     )
        snake_case__    							=				num_channels
        snake_case__    							=				embedding_size
        snake_case__    							=				hidden_sizes
        snake_case__    							=				depths
        snake_case__    							=				groups_width
        snake_case__    							=				layer_type
        snake_case__    							=				hidden_act
        # always downsample in the first stage
        snake_case__    							=				True

