




import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin
class 			_UpperCamelCase      (				unittest.TestCase	,       lowerCAmelCase		):
      def 	UpperCAmelCase_						(			self   :int      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						load_tool("text-classification"      )
       self.tool.setup()
       UpperCAmelCase__							 =						load_tool("text-classification" ,      remote=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						self.tool("That's quite cool" ,      ["positive", "negative"]      )
       self.assertEqual(lowerCamelCase ,      "positive"      )
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			int:
       UpperCAmelCase__							 =						self.remote_tool("That's quite cool" ,      ["positive", "negative"]      )
       self.assertEqual(lowerCamelCase ,      "positive"      )
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			str:
       UpperCAmelCase__							 =						self.tool(text="That's quite cool" ,      labels=["positive", "negative"]      )
       self.assertEqual(lowerCamelCase ,      "positive"      )







      def 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			Any:
       UpperCAmelCase__							 =						self.remote_tool(text="That's quite cool" ,      labels=["positive", "negative"]      )
       self.assertEqual(lowerCamelCase ,      "positive"      )











import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		(IPNDMScheduler,)
      UpperCAmelCase_        =		(("""num_inference_steps""", 50),)
      def 	UpperCAmelCase_						(			self   :List[str] ,      **lowerCamelCase   :List[Any]      )     ->			List[str]:
       UpperCAmelCase__							 =						{"num_train_timesteps": 1000}
       config.update(**lowerCamelCase      )
       return config
      def 	UpperCAmelCase_						(			self   :str ,      lowerCamelCase   :Union[str, Any]=0 ,      **lowerCamelCase   :str      )     ->			str:
       UpperCAmelCase__							 =						dict(self.forward_default_kwargs      )
       UpperCAmelCase__							 =						kwargs.pop("num_inference_steps" ,      lowerCamelCase      )
       UpperCAmelCase__							 =						self.dummy_sample
       UpperCAmelCase__							 =						0.1 * sample
       UpperCAmelCase__							 =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
        UpperCAmelCase__							 =						self.get_scheduler_config(**lowerCamelCase      )
        UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )
        scheduler.set_timesteps(lowerCamelCase      )
        # copy over dummy past residuals
        UpperCAmelCase__							 =						dummy_past_residuals[:]

        if time_step is None:
         UpperCAmelCase__							 =						scheduler.timesteps[len(scheduler.timesteps      ) // 2]

        with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(lowerCamelCase      )
         UpperCAmelCase__							 =						scheduler_class.from_pretrained(lowerCamelCase      )
         new_scheduler.set_timesteps(lowerCamelCase      )
         # copy over dummy past residuals
         UpperCAmelCase__							 =						dummy_past_residuals[:]

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						new_scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						new_scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Tuple:
       pass
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :List[str]=0 ,      **lowerCamelCase   :List[str]      )     ->			Optional[int]:
       UpperCAmelCase__							 =						dict(self.forward_default_kwargs      )
       UpperCAmelCase__							 =						kwargs.pop("num_inference_steps" ,      lowerCamelCase      )
       UpperCAmelCase__							 =						self.dummy_sample
       UpperCAmelCase__							 =						0.1 * sample
       UpperCAmelCase__							 =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
        UpperCAmelCase__							 =						self.get_scheduler_config()
        UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )
        scheduler.set_timesteps(lowerCamelCase      )

        # copy over dummy past residuals (must be after setting timesteps)
        UpperCAmelCase__							 =						dummy_past_residuals[:]

        if time_step is None:
         UpperCAmelCase__							 =						scheduler.timesteps[len(scheduler.timesteps      ) // 2]

        with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(lowerCamelCase      )
         UpperCAmelCase__							 =						scheduler_class.from_pretrained(lowerCamelCase      )
         # copy over dummy past residuals
         new_scheduler.set_timesteps(lowerCamelCase      )

         # copy over dummy past residual (must be after setting timesteps)
         UpperCAmelCase__							 =						dummy_past_residuals[:]

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						new_scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						new_scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"
      def 	UpperCAmelCase_						(			self   :int ,      **lowerCamelCase   :Any      )     ->			int:
       UpperCAmelCase__							 =						self.scheduler_classes[0]
       UpperCAmelCase__							 =						self.get_scheduler_config(**lowerCamelCase      )
       UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )

       UpperCAmelCase__							 =						10
       UpperCAmelCase__							 =						self.dummy_model()
       UpperCAmelCase__							 =						self.dummy_sample_deter
       scheduler.set_timesteps(lowerCamelCase      )

       for i, t in enumerate(scheduler.timesteps      ):
        UpperCAmelCase__							 =						model(lowerCamelCase ,      lowerCamelCase      )
        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase      ).prev_sample

       for i, t in enumerate(scheduler.timesteps      ):
        UpperCAmelCase__							 =						model(lowerCamelCase ,      lowerCamelCase      )
        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase      ).prev_sample

       return sample
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						dict(self.forward_default_kwargs      )

       UpperCAmelCase__							 =						kwargs.pop("num_inference_steps" ,      lowerCamelCase      )

       for scheduler_class in self.scheduler_classes:
        UpperCAmelCase__							 =						self.get_scheduler_config()
        UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )

        UpperCAmelCase__							 =						self.dummy_sample
        UpperCAmelCase__							 =						0.1 * sample

        if num_inference_steps is not None and hasattr(lowerCamelCase ,      "set_timesteps"      ):
         scheduler.set_timesteps(lowerCamelCase      )
        elif num_inference_steps is not None and not hasattr(lowerCamelCase ,      "set_timesteps"      ):
         UpperCAmelCase__							 =						num_inference_steps

        # copy over dummy past residuals (must be done after set_timesteps)
        UpperCAmelCase__							 =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
        UpperCAmelCase__							 =						dummy_past_residuals[:]

        UpperCAmelCase__							 =						scheduler.timesteps[5]
        UpperCAmelCase__							 =						scheduler.timesteps[6]

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        self.assertEqual(output_a.shape ,      sample.shape      )
        self.assertEqual(output_a.shape ,      output_a.shape      )

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        self.assertEqual(output_a.shape ,      sample.shape      )
        self.assertEqual(output_a.shape ,      output_a.shape      )
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			Tuple:
       for timesteps in [100, 1000]:
        self.check_over_configs(num_train_timesteps=lowerCamelCase ,      time_step=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Optional[int]:
       for t, num_inference_steps in zip([1, 5, 10] ,      [10, 50, 100]      ):
        self.check_over_forward(num_inference_steps=lowerCamelCase ,      time_step=lowerCamelCase      )







      def 	UpperCAmelCase_						(			self   :Any      )     ->			Dict:
       UpperCAmelCase__							 =						self.full_loop()
       UpperCAmelCase__							 =						torch.mean(torch.abs(lowerCamelCase      )      )

       assert abs(result_mean.item() - 254_0529      ) < 10





