




import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest





class 		A__    (       __A			):
 lowerCamelCase__		:			Dict       =(DDIMScheduler,)
 lowerCamelCase__		:			str       =(("eta", 0.0), ("num_inference_steps", 50))






 def 			lowercase				(     self    ,					**lowerCamelCase						)				->       Optional[int]:




      """simple docstring"""






      __magic_name__ :							Dict		       =							{
          "num_train_timesteps": 1000,
          "beta_start": 0.0_0_0_1,
          "beta_end": 0.0_2,
          "beta_schedule": "linear",
          "clip_sample": True,
      }

      config.update(**lowerCamelCase						)
      return config






 def 			lowercase				(     self    ,					**lowerCamelCase						)				->       Tuple:




      """simple docstring"""






      __magic_name__ :							int		       =							self.scheduler_classes[0]
      __magic_name__ :							Optional[Any]		       =							self.get_scheduler_config(**lowerCamelCase						)
      __magic_name__ :							Optional[Any]		       =							scheduler_class(**lowerCamelCase						)

      __magic_name__ :							Optional[Any]		       =							10, 0.0

      __magic_name__ :							str		       =							self.dummy_model()
      __magic_name__ :							List[Any]		       =							self.dummy_sample_deter

      scheduler.set_timesteps(lowerCamelCase						)

      for t in scheduler.timesteps:
           __magic_name__ :							List[str]		       =							model(lowerCamelCase    ,					lowerCamelCase						)
           __magic_name__ :							List[str]		       =							scheduler.step(lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						).prev_sample

      return sample






 def 			lowercase				(     self						)				->       Optional[int]:




      """simple docstring"""






      for timesteps in [100, 500, 1000]:
           self.check_over_configs(num_train_timesteps=lowerCamelCase						)






 def 			lowercase				(     self						)				->       Optional[Any]:




      """simple docstring"""






      for steps_offset in [0, 1]:
           self.check_over_configs(steps_offset=lowerCamelCase						)

      __magic_name__ :							Tuple		       =							self.scheduler_classes[0]
      __magic_name__ :							List[Any]		       =							self.get_scheduler_config(steps_offset=1						)
      __magic_name__ :							List[Any]		       =							scheduler_class(**lowerCamelCase						)
      scheduler.set_timesteps(5						)
      assert torch.equal(scheduler.timesteps    ,					torch.LongTensor([801, 601, 401, 201, 1]						)						)






 def 			lowercase				(     self						)				->       int:




      """simple docstring"""






      for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]    ,					[0.0_0_2, 0.0_2, 0.2, 2]						):
           self.check_over_configs(beta_start=lowerCamelCase    ,					beta_end=lowerCamelCase						)






 def 			lowercase				(     self						)				->       List[Any]:




      """simple docstring"""






      for schedule in ["linear", "squaredcos_cap_v2"]:
           self.check_over_configs(beta_schedule=lowerCamelCase						)






 def 			lowercase				(     self						)				->       int:




      """simple docstring"""






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






 def 			lowercase				(     self						)				->       Tuple:




      """simple docstring"""






      for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=lowerCamelCase						)






 def 			lowercase				(     self						)				->       Any:




      """simple docstring"""






      for timestep_spacing in ["trailing", "leading"]:
           self.check_over_configs(timestep_spacing=lowerCamelCase						)






 def 			lowercase				(     self						)				->       List[str]:




      """simple docstring"""






      for rescale_betas_zero_snr in [True, False]:
           self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase						)






 def 			lowercase				(     self						)				->       List[str]:




      """simple docstring"""






      self.check_over_configs(thresholding=lowerCamelCase						)
      for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(
                    thresholding=lowerCamelCase    ,					prediction_type=lowerCamelCase    ,					sample_max_value=lowerCamelCase    ,					)






 def 			lowercase				(     self						)				->       Tuple:




      """simple docstring"""






      for t in [1, 10, 49]:
           self.check_over_forward(time_step=lowerCamelCase						)






 def 			lowercase				(     self						)				->       Any:




      """simple docstring"""






      for t, num_inference_steps in zip([1, 10, 50]    ,					[10, 50, 500]						):
           self.check_over_forward(time_step=lowerCamelCase    ,					num_inference_steps=lowerCamelCase						)






 def 			lowercase				(     self						)				->       Any:




      """simple docstring"""






      for t, eta in zip([1, 10, 49]    ,					[0.0, 0.5, 1.0]						):
           self.check_over_forward(time_step=lowerCamelCase    ,					eta=lowerCamelCase						)






 def 			lowercase				(     self						)				->       List[str]:




      """simple docstring"""






      __magic_name__ :							Optional[int]		       =							self.scheduler_classes[0]
      __magic_name__ :							int		       =							self.get_scheduler_config()
      __magic_name__ :							int		       =							scheduler_class(**lowerCamelCase						)

      assert torch.sum(torch.abs(scheduler._get_variance(0    ,					0						) - 0.0						)						) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(420    ,					400						) - 0.1_4_7_7_1						)						) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(980    ,					960						) - 0.3_2_4_6_0						)						) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(0    ,					0						) - 0.0						)						) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(487    ,					486						) - 0.0_0_9_7_9						)						) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(999    ,					998						) - 0.0_2						)						) < 1e-5






 def 			lowercase				(     self						)				->       Any:




      """simple docstring"""






      __magic_name__ :							Any		       =							self.full_loop()

      __magic_name__ :							Dict		       =							torch.sum(torch.abs(lowerCamelCase						)						)
      __magic_name__ :							List[Any]		       =							torch.mean(torch.abs(lowerCamelCase						)						)

      assert abs(result_sum.item() - 1_7_2.0_0_6_7						) < 1e-2
      assert abs(result_mean.item() - 0.2_2_3_9_6_7						) < 1e-3






 def 			lowercase				(     self						)				->       int:




      """simple docstring"""






      __magic_name__ :							Optional[Any]		       =							self.full_loop(prediction_type='''v_prediction'''						)

      __magic_name__ :							Union[str, Any]		       =							torch.sum(torch.abs(lowerCamelCase						)						)
      __magic_name__ :							Dict		       =							torch.mean(torch.abs(lowerCamelCase						)						)

      assert abs(result_sum.item() - 5_2.5_3_0_2						) < 1e-2
      assert abs(result_mean.item() - 0.0_6_8_4						) < 1e-3






 def 			lowercase				(     self						)				->       Tuple:




      """simple docstring"""






      __magic_name__ :							str		       =							self.full_loop(set_alpha_to_one=lowerCamelCase    ,					beta_start=0.0_1						)
      __magic_name__ :							List[str]		       =							torch.sum(torch.abs(lowerCamelCase						)						)
      __magic_name__ :							Union[str, Any]		       =							torch.mean(torch.abs(lowerCamelCase						)						)

      assert abs(result_sum.item() - 1_4_9.8_2_9_5						) < 1e-2
      assert abs(result_mean.item() - 0.1_9_5_1						) < 1e-3






 def 			lowercase				(     self						)				->       Optional[Any]:




      """simple docstring"""






      __magic_name__ :							Union[str, Any]		       =							self.full_loop(set_alpha_to_one=lowerCamelCase    ,					beta_start=0.0_1						)
      __magic_name__ :							int		       =							torch.sum(torch.abs(lowerCamelCase						)						)
      __magic_name__ :							List[Any]		       =							torch.mean(torch.abs(lowerCamelCase						)						)

      assert abs(result_sum.item() - 1_4_9.0_7_8_4						) < 1e-2
      assert abs(result_mean.item() - 0.1_9_4_1						) < 1e-3









import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
				from PIL import Image





class 		A__    (       unittest.TestCase		,					__SCREAMING_SNAKE_CASE			):






	def 			lowercase				(     self						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							load_tool('''image-question-answering'''						)
						self.tool.setup()
						__magic_name__ :							Any		       =							load_tool('''image-question-answering'''    ,					remote=lowerCamelCase						)






	def 			lowercase				(     self						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							int		       =							self.tool(lowerCamelCase    ,					'''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)






	def 			lowercase				(     self						)				->       Union[str, Any]:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							int		       =							self.remote_tool(lowerCamelCase    ,					'''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)






	def 			lowercase				(     self						)				->       Any:




						"""simple docstring"""






						__magic_name__ :							int		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							Tuple		       =							self.tool(image=lowerCamelCase    ,					question='''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)






	def 			lowercase				(     self						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							Tuple		       =							self.remote_tool(image=lowerCamelCase    ,					question='''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)



