



"""simple docstring"""






from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image


snake_case   					=				['''text''', '''image''', '''audio''']




def 			snake_case		(		lowerCAmelCase_	)						->				Any:
  _snake_case   	=      []

  for input_type in input_types:
    if input_type == "text":
      inputs.append('''Text input'''	)
    elif input_type == "image":
      inputs.append(
          Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''	)	) / '''000000039769.png'''	).resize((512, 512)	)	)
    elif input_type == "audio":
      inputs.append(torch.ones(3000	)	)
    elif isinstance(lowerCAmelCase_  ,						lowerCAmelCase_	):
      inputs.append(create_inputs(lowerCAmelCase_	)	)
    else:
      raise ValueError(f"""Invalid type requested: {input_type}"""	)

  return inputs




def 			snake_case		(		lowerCAmelCase_	)						->				str:
  _snake_case   	=      []

  for output in outputs:
    if isinstance(lowerCAmelCase_  ,						(str, AgentText)	):
      output_types.append('''text'''	)
    elif isinstance(lowerCAmelCase_  ,						(Image.Image, AgentImage)	):
      output_types.append('''image'''	)
    elif isinstance(lowerCAmelCase_  ,						(torch.Tensor, AgentAudio)	):
      output_types.append('''audio'''	)
    else:
      raise ValueError(f"""Invalid output: {output}"""	)

  return output_types







@is_tool_test
class     UpperCAmelCase    :



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     self.assertTrue(hasattr(self.tool							,       '''inputs'''				)				)
     self.assertTrue(hasattr(self.tool							,       '''outputs'''				)				)

     _snake_case   	=      self.tool.inputs
     for _input in inputs:
       if isinstance(_input							,       __lowerCamelCase				):
         for __input in _input:
           self.assertTrue(__input in authorized_types				)
       else:
         self.assertTrue(_input in authorized_types				)

     _snake_case   	=      self.tool.outputs
     for _output in outputs:
       self.assertTrue(_output in authorized_types				)



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      create_inputs(self.tool.inputs				)
     _snake_case   	=      self.tool(*__lowerCamelCase				)

     # There is a single output
     if len(self.tool.outputs				) == 1:
       _snake_case   	=      [outputs]

     self.assertListEqual(output_types(__lowerCamelCase				)							,       self.tool.outputs				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     self.assertTrue(hasattr(self.tool							,       '''description'''				)				)
     self.assertTrue(hasattr(self.tool							,       '''default_checkpoint'''				)				)
     self.assertTrue(self.tool.description.startswith('''This is a tool that'''				)				)



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      create_inputs(self.tool.inputs				)
     _snake_case   	=      self.tool(*__lowerCamelCase				)

     if not isinstance(__lowerCamelCase							,       __lowerCamelCase				):
       _snake_case   	=      [outputs]

     self.assertEqual(len(__lowerCamelCase				)							,       len(self.tool.outputs				)				)

     for output, output_type in zip(__lowerCamelCase							,       self.tool.outputs				):
       _snake_case   	=      AGENT_TYPE_MAPPING[output_type]
       self.assertTrue(isinstance(__lowerCamelCase							,       __lowerCamelCase				)				)





   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case   	=      create_inputs(self.tool.inputs				)

     _snake_case   	=      []

     for _input, input_type in zip(__lowerCamelCase							,       self.tool.inputs				):
       if isinstance(__lowerCamelCase							,       __lowerCamelCase				):
         _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input				) for _input_type in input_type]				)
       else:
         _inputs.append(AGENT_TYPE_MAPPING[input_type](_input				)				)

        # Should not raise an error
     _snake_case   	=      self.tool(*__lowerCamelCase				)

     if not isinstance(__lowerCamelCase							,       __lowerCamelCase				):
       _snake_case   	=      [outputs]

     self.assertEqual(len(__lowerCamelCase				)							,       len(self.tool.outputs				)				)







import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     (KDPMaDiscreteScheduler,)
					SCREAMING_SNAKE_CASE           =     1_0

					def  lowercase__   (						self       :  str ,       **__UpperCamelCase       :  Optional[int] ):
												lowerCamelCase_					=       {
												    """num_train_timesteps""": 1_1_0_0,
												    """beta_start""": 0.0001,
												    """beta_end""": 0.02,
												    """beta_schedule""": """linear""",
												}

												config.update(**__UpperCamelCase )
												return config

					def  lowercase__   (						self       :  Optional[Any] ):
												for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
																			self.check_over_configs(num_train_timesteps=__UpperCamelCase )

					def  lowercase__   (						self       :  Dict ):
												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=__UpperCamelCase ,       beta_end=__UpperCamelCase )

					def  lowercase__   (						self       :  Union[str, Any] ):
												for schedule in ["linear", "scaled_linear"]:
																			self.check_over_configs(beta_schedule=__UpperCamelCase )

					def  lowercase__   (						self       :  List[str] ):
												for prediction_type in ["epsilon", "v_prediction"]:
																			self.check_over_configs(prediction_type=__UpperCamelCase )

					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       self.scheduler_classes[0]
												lowerCamelCase_					=       self.get_scheduler_config(prediction_type="""v_prediction""" )
												lowerCamelCase_					=       scheduler_class(**__UpperCamelCase )

												scheduler.set_timesteps(self.num_inference_steps )

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

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

																			lowerCamelCase_					=       model(__UpperCamelCase ,       __UpperCamelCase )

																			lowerCamelCase_					=       scheduler.step(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
																			lowerCamelCase_					=       output.prev_sample

												lowerCamelCase_					=       torch.sum(torch.abs(__UpperCamelCase ) )
												lowerCamelCase_					=       torch.mean(torch.abs(__UpperCamelCase ) )

												if torch_device in ["cpu", "mps"]:
																			assert abs(result_sum.item() - 4.6934E-07 ) < 1E-2
																			assert abs(result_mean.item() - 6.1112E-10 ) < 1E-3
												else:
																			# CUDA
																			assert abs(result_sum.item() - 4.693_4286_5017_0972E-07 ) < 1E-2
																			assert abs(result_mean.item() - 0.0002 ) < 1E-3

					def  lowercase__   (						self       :  List[Any] ):
												if torch_device == "mps":
																			return
												lowerCamelCase_					=       self.scheduler_classes[0]
												lowerCamelCase_					=       self.get_scheduler_config()
												lowerCamelCase_					=       scheduler_class(**__UpperCamelCase )

												scheduler.set_timesteps(self.num_inference_steps )

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

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

																			lowerCamelCase_					=       model(__UpperCamelCase ,       __UpperCamelCase )

																			lowerCamelCase_					=       scheduler.step(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
																			lowerCamelCase_					=       output.prev_sample

												lowerCamelCase_					=       torch.sum(torch.abs(__UpperCamelCase ) )
												lowerCamelCase_					=       torch.mean(torch.abs(__UpperCamelCase ) )

												if torch_device in ["cpu", "mps"]:
																			assert abs(result_sum.item() - 20.4125 ) < 1E-2
																			assert abs(result_mean.item() - 0.0266 ) < 1E-3
												else:
																			# CUDA
																			assert abs(result_sum.item() - 20.4125 ) < 1E-2
																			assert abs(result_mean.item() - 0.0266 ) < 1E-3



					def  lowercase__   (						self       :  Optional[int] ):
												if torch_device == "mps":
																			return
												lowerCamelCase_					=       self.scheduler_classes[0]
												lowerCamelCase_					=       self.get_scheduler_config()
												lowerCamelCase_					=       scheduler_class(**__UpperCamelCase )

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

												lowerCamelCase_					=       self.dummy_model()
												lowerCamelCase_					=       self.dummy_sample_deter.to(__UpperCamelCase ) * scheduler.init_noise_sigma

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

																			lowerCamelCase_					=       model(__UpperCamelCase ,       __UpperCamelCase )

																			lowerCamelCase_					=       scheduler.step(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
																			lowerCamelCase_					=       output.prev_sample

												lowerCamelCase_					=       torch.sum(torch.abs(__UpperCamelCase ) )
												lowerCamelCase_					=       torch.mean(torch.abs(__UpperCamelCase ) )

												if str(__UpperCamelCase ).startswith("""cpu""" ):
																			# The following sum varies between 148 and 156 on mps. Why?
																			assert abs(result_sum.item() - 20.4125 ) < 1E-2
																			assert abs(result_mean.item() - 0.0266 ) < 1E-3
												else:
																			# CUDA
																			assert abs(result_sum.item() - 20.4125 ) < 1E-2
																			assert abs(result_mean.item() - 0.0266 ) < 1E-3





