




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 						SCREAMING_SNAKE_CASE__		(UpperCamelCase__ ):
     lowercase_   :			str   =   (EulerAncestralDiscreteScheduler,)
     lowercase_   :			Optional[int]   =   10
     def 					A__			(	self  :		Any					,	**__lowerCamelCase  :		List[Any]	):



       """simple docstring"""
       lowerCAmelCase__								=			{
           "num_train_timesteps": 11_00,
           "beta_start": 0.0001,
           "beta_end": 0.02,
           "beta_schedule": "linear",
       }

       config.update(**lowerCAmelCase__	)
       return config
     def 					A__			(	self  :		int	):



       """simple docstring"""




       for timesteps in [10, 50, 1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=lowerCAmelCase__	)
     def 					A__			(	self  :		int	):



       """simple docstring"""




       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=lowerCAmelCase__					,	beta_end=lowerCAmelCase__	)
     def 					A__			(	self  :		Union[str, Any]	):



       """simple docstring"""




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



       """simple docstring"""




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



       """simple docstring"""
       lowerCAmelCase__								=			self.scheduler_classes[0]
       lowerCAmelCase__								=			self.get_scheduler_config()
       lowerCAmelCase__								=			scheduler_class(**lowerCAmelCase__	)

       scheduler.set_timesteps(self.num_inference_steps	)

       lowerCAmelCase__								=			torch.manual_seed(0	)

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

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

         lowerCAmelCase__								=			model(lowerCAmelCase__					,	lowerCAmelCase__	)

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

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

       assert abs(result_sum.item() - 1_52.31_92	) < 1e-2
       assert abs(result_mean.item() - 0.1983	) < 1e-3
     def 					A__			(	self  :		Optional[Any]	):



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

       scheduler.set_timesteps(self.num_inference_steps	)

       lowerCAmelCase__								=			torch.manual_seed(0	)

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

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

         lowerCAmelCase__								=			model(lowerCAmelCase__					,	lowerCAmelCase__	)

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

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

       assert abs(result_sum.item() - 1_08.44_39	) < 1e-2
       assert abs(result_mean.item() - 0.1412	) < 1e-3



     def 					A__			(	self  :		List[str]	):



       """simple docstring"""
       lowerCAmelCase__								=			self.scheduler_classes[0]
       lowerCAmelCase__								=			self.get_scheduler_config()
       lowerCAmelCase__								=			scheduler_class(**lowerCAmelCase__	)

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

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

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

         lowerCAmelCase__								=			model(lowerCAmelCase__					,	lowerCAmelCase__	)

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

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

       assert abs(result_sum.item() - 1_52.31_92	) < 1e-2
       assert abs(result_mean.item() - 0.1983	) < 1e-3








import json

import requests

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info
def 		a_       (   __lowerCAmelCase							):





  class 						SCREAMING_SNAKE_CASE__		:
       def __init__(	self  :		List[str]					,	__lowerCamelCase  :		Any	):



         """simple docstring"""
         assert isinstance(__lowerCamelCase					,	(bytes, str)	)
         lowerCAmelCase__								=			content



       def 					A__			(	self  :		int	):



         """simple docstring"""
         return json.loads(self.content	)

  def mock_response(*__lowerCAmelCase      ,   **__lowerCAmelCase							):
    assert args[0] == AUTHENTICATED_USER_ENDPOINT
    assert "Authorization" in kwargs["headers"]
    assert kwargs["headers"]["Authorization"].startswith('''token '''							)
    assert "Accept" in kwargs["headers"]
    return FakeResponse(B'''{"login":"test","id":1}'''							)

  monkeypatch.setattr(__lowerCAmelCase      ,   '''get'''      ,   __lowerCAmelCase							)
  lowerCAmelCase__								=			fetch_github_info('''token'''							)
  assert result["login"] == "test"
  assert result["id"] == 1

