


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class       UpperCAmelCase_		(							__snake_case):
       '''simple docstring'''
       __UpperCamelCase					:   Optional[int]							=    (LMSDiscreteScheduler,)
       __UpperCamelCase					:   Union[str, Any]							=    10






       def        _lowercase      (   self       ,    **__SCREAMING_SNAKE_CASE  ):




              """simple docstring"""




              UpperCamelCase			:		Optional[int]      		=						{
                  "num_train_timesteps": 1_100,
                  "beta_start": 0.0_001,
                  "beta_end": 0.02,
                  "beta_schedule": "linear",
              }

              config.update(**lowercase_  )
              return config






       def        _lowercase      (   self  ):




              """simple docstring"""




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






       def        _lowercase      (   self  ):




              """simple docstring"""




              for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]       ,    [0.0_002, 0.002, 0.02]  ):
                     self.check_over_configs(beta_start=lowercase_       ,    beta_end=lowercase_  )






       def        _lowercase      (   self  ):




              """simple docstring"""




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






       def        _lowercase      (   self  ):




              """simple docstring"""




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






       def        _lowercase      (   self  ):




              """simple docstring"""




              for t in [0, 500, 800]:
                     self.check_over_forward(time_step=lowercase_  )






       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		List[Any]      		=						self.scheduler_classes[0]
              UpperCamelCase			:		str      		=						self.get_scheduler_config()
              UpperCamelCase			:		str      		=						scheduler_class(**lowercase_  )

              scheduler.set_timesteps(self.num_inference_steps  )

              UpperCamelCase			:		Union[str, Any]      		=						self.dummy_model()
              UpperCamelCase			:		List[Any]      		=						self.dummy_sample_deter * scheduler.init_noise_sigma

              for i, t in enumerate(scheduler.timesteps  ):
                     UpperCamelCase			:		List[Any]      		=						scheduler.scale_model_input(lowercase_       ,    lowercase_  )

                     UpperCamelCase			:		str      		=						model(lowercase_       ,    lowercase_  )

                     UpperCamelCase			:		Tuple      		=						scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_  )
                     UpperCamelCase			:		Tuple      		=						output.prev_sample

              UpperCamelCase			:		str      		=						torch.sum(torch.abs(lowercase_  )  )
              UpperCamelCase			:		Tuple      		=						torch.mean(torch.abs(lowercase_  )  )

              assert abs(result_sum.item() - 1_006.388  ) < 1e-2
              assert abs(result_mean.item() - 1.31  ) < 1e-3






       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		Any      		=						self.scheduler_classes[0]
              UpperCamelCase			:		str      		=						self.get_scheduler_config(prediction_type='''v_prediction'''  )
              UpperCamelCase			:		Tuple      		=						scheduler_class(**lowercase_  )

              scheduler.set_timesteps(self.num_inference_steps  )

              UpperCamelCase			:		Optional[int]      		=						self.dummy_model()
              UpperCamelCase			:		List[str]      		=						self.dummy_sample_deter * scheduler.init_noise_sigma

              for i, t in enumerate(scheduler.timesteps  ):
                     UpperCamelCase			:		Tuple      		=						scheduler.scale_model_input(lowercase_       ,    lowercase_  )

                     UpperCamelCase			:		Tuple      		=						model(lowercase_       ,    lowercase_  )

                     UpperCamelCase			:		int      		=						scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_  )
                     UpperCamelCase			:		Any      		=						output.prev_sample

              UpperCamelCase			:		List[Any]      		=						torch.sum(torch.abs(lowercase_  )  )
              UpperCamelCase			:		Tuple      		=						torch.mean(torch.abs(lowercase_  )  )

              assert abs(result_sum.item() - 0.0_017  ) < 1e-2
              assert abs(result_mean.item() - 2.2676e-06  ) < 1e-3






       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		List[Any]      		=						self.scheduler_classes[0]
              UpperCamelCase			:		Any      		=						self.get_scheduler_config()
              UpperCamelCase			:		int      		=						scheduler_class(**lowercase_  )

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

              UpperCamelCase			:		int      		=						self.dummy_model()
              UpperCamelCase			:		str      		=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
              UpperCamelCase			:		int      		=						sample.to(lowercase_  )

              for i, t in enumerate(scheduler.timesteps  ):
                     UpperCamelCase			:		str      		=						scheduler.scale_model_input(lowercase_       ,    lowercase_  )

                     UpperCamelCase			:		List[Any]      		=						model(lowercase_       ,    lowercase_  )

                     UpperCamelCase			:		Optional[Any]      		=						scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_  )
                     UpperCamelCase			:		List[Any]      		=						output.prev_sample

              UpperCamelCase			:		Optional[Any]      		=						torch.sum(torch.abs(lowercase_  )  )
              UpperCamelCase			:		Union[str, Any]      		=						torch.mean(torch.abs(lowercase_  )  )

              assert abs(result_sum.item() - 1_006.388  ) < 1e-2
              assert abs(result_mean.item() - 1.31  ) < 1e-3






       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		List[Any]      		=						self.scheduler_classes[0]
              UpperCamelCase			:		Tuple      		=						self.get_scheduler_config()
              UpperCamelCase			:		List[str]      		=						scheduler_class(**lowercase_       ,    use_karras_sigmas=lowercase_  )

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

              UpperCamelCase			:		str      		=						self.dummy_model()
              UpperCamelCase			:		Dict      		=						self.dummy_sample_deter.to(lowercase_  ) * scheduler.init_noise_sigma
              UpperCamelCase			:		Any      		=						sample.to(lowercase_  )

              for t in scheduler.timesteps:
                     UpperCamelCase			:		int      		=						scheduler.scale_model_input(lowercase_       ,    lowercase_  )

                     UpperCamelCase			:		Any      		=						model(lowercase_       ,    lowercase_  )

                     UpperCamelCase			:		Dict      		=						scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_  )
                     UpperCamelCase			:		Any      		=						output.prev_sample

              UpperCamelCase			:		Any      		=						torch.sum(torch.abs(lowercase_  )  )
              UpperCamelCase			:		Any      		=						torch.mean(torch.abs(lowercase_  )  )

              assert abs(result_sum.item() - 3_812.9_927  ) < 2e-2
              assert abs(result_mean.item() - 4.9_648  ) < 1e-3


import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
				import tensorflow as tf

				from transformers.activations_tf import get_tf_activation



@require_tf
class    lowercase_    (     unittest.TestCase		):



							def        UpperCamelCase       (						self				):
													_snake_case						:     int				=    tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 100.0]				)
													_snake_case						:     List[Any]				=    get_tf_activation("gelu"				)
													_snake_case						:     Any				=    get_tf_activation("gelu_10"				)

													_snake_case						:     Dict				=    gelu(lowercase_				)
													_snake_case						:     Optional[int]				=    geluaa(lowercase_				)

													_snake_case						:     Tuple				=    tf.where(y_gelu_aa < 10.0      ,				1.0      ,				0.0				)

													self.assertEqual(tf.math.reduce_max(lowercase_				).numpy().item()      ,				10.0				)
													self.assertTrue(np.allclose(y_gelu * clipped_mask      ,				y_gelu_aa * clipped_mask				)				)



							def        UpperCamelCase       (						self				):
													get_tf_activation("gelu"				)
													get_tf_activation("gelu_10"				)
													get_tf_activation("gelu_fast"				)
													get_tf_activation("gelu_new"				)
													get_tf_activation("glu"				)
													get_tf_activation("mish"				)
													get_tf_activation("quick_gelu"				)
													get_tf_activation("relu"				)
													get_tf_activation("sigmoid"				)
													get_tf_activation("silu"				)
													get_tf_activation("swish"				)
													get_tf_activation("tanh"				)
													with self.assertRaises(lowercase_				):
																			get_tf_activation("bogus"				)
													with self.assertRaises(lowercase_				):
																			get_tf_activation(lowercase_				)