



"""simple docstring"""






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							(DPMSolverSinglestepScheduler,)
					_a   			=							(('num_inference_steps', 2_5),)



					def 		snake_case       (       self							:  Optional[int],  **lowerCamelCase							:  Optional[Any]     )->					Optional[Any]:
								lowerCamelCase__		:		str			={
								    '''num_train_timesteps''': 1000,
								    '''beta_start''': 0.0_001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								    '''solver_order''': 2,
								    '''prediction_type''': '''epsilon''',
								    '''thresholding''': False,
								    '''sample_max_value''': 1.0,
								    '''algorithm_type''': '''dpmsolver++''',
								    '''solver_type''': '''midpoint''',
								    '''lambda_min_clipped''': -float('''inf'''     ),
								    '''variance_type''': None,
								}

								config.update(**lowerCamelCase     )
								return config



					def 		snake_case       (       self							:  Optional[Any],  lowerCamelCase							:  Optional[int]=0,  **lowerCamelCase							:  Dict     )->					Any:
								lowerCamelCase__		:		Tuple			=dict(self.forward_default_kwargs     )
								lowerCamelCase__		:		str			=kwargs.pop('''num_inference_steps''',  lowerCamelCase     )
								lowerCamelCase__		:		Any			=self.dummy_sample
								lowerCamelCase__		:		Union[str, Any]			=0.1 * sample
								lowerCamelCase__		:		Optional[int]			=[residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
											lowerCamelCase__		:		str			=self.get_scheduler_config(**lowerCamelCase     )
											lowerCamelCase__		:		Optional[Any]			=scheduler_class(**lowerCamelCase     )
											scheduler.set_timesteps(lowerCamelCase     )
											# copy over dummy past residuals
											lowerCamelCase__		:		Any			=dummy_past_residuals[: scheduler.config.solver_order]

											with tempfile.TemporaryDirectory() as tmpdirname:
														scheduler.save_config(lowerCamelCase     )
														lowerCamelCase__		:		Optional[Any]			=scheduler_class.from_pretrained(lowerCamelCase     )
														new_scheduler.set_timesteps(lowerCamelCase     )
														# copy over dummy past residuals
														lowerCamelCase__		:		Tuple			=dummy_past_residuals[: new_scheduler.config.solver_order]

											lowerCamelCase__	,   lowerCamelCase__		:		Optional[int]			=sample, sample
											for t in range(lowerCamelCase,  time_step + scheduler.config.solver_order + 1     ):
														lowerCamelCase__		:		Tuple			=scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  **lowerCamelCase     ).prev_sample
														lowerCamelCase__		:		Optional[Any]			=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 		snake_case       (       self							:  List[str]     )->					Optional[int]:
								pass



					def 		snake_case       (       self							:  Optional[Any],  lowerCamelCase							:  str=0,  **lowerCamelCase							:  List[Any]     )->					List[Any]:
								lowerCamelCase__		:		Dict			=dict(self.forward_default_kwargs     )
								lowerCamelCase__		:		Tuple			=kwargs.pop('''num_inference_steps''',  lowerCamelCase     )
								lowerCamelCase__		:		Optional[int]			=self.dummy_sample
								lowerCamelCase__		:		Dict			=0.1 * sample
								lowerCamelCase__		:		Dict			=[residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
											lowerCamelCase__		:		int			=self.get_scheduler_config()
											lowerCamelCase__		:		Dict			=scheduler_class(**lowerCamelCase     )
											scheduler.set_timesteps(lowerCamelCase     )

											# copy over dummy past residuals (must be after setting timesteps)
											lowerCamelCase__		:		List[Any]			=dummy_past_residuals[: scheduler.config.solver_order]

											with tempfile.TemporaryDirectory() as tmpdirname:
														scheduler.save_config(lowerCamelCase     )
														lowerCamelCase__		:		List[str]			=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)
														lowerCamelCase__		:		Any			=dummy_past_residuals[: new_scheduler.config.solver_order]

											lowerCamelCase__		:		List[Any]			=scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  **lowerCamelCase     ).prev_sample
											lowerCamelCase__		:		Any			=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 		snake_case       (       self							:  Dict,  lowerCamelCase							:  Optional[int]=None,  **lowerCamelCase							:  Dict     )->					int:
								if scheduler is None:
											lowerCamelCase__		:		Optional[int]			=self.scheduler_classes[0]
											lowerCamelCase__		:		List[str]			=self.get_scheduler_config(**lowerCamelCase     )
											lowerCamelCase__		:		Dict			=scheduler_class(**lowerCamelCase     )

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

								lowerCamelCase__		:		int			=10
								lowerCamelCase__		:		Any			=self.dummy_model()
								lowerCamelCase__		:		Optional[int]			=self.dummy_sample_deter
								scheduler.set_timesteps(lowerCamelCase     )

								for i, t in enumerate(scheduler.timesteps     ):
											lowerCamelCase__		:		Optional[Any]			=model(lowerCamelCase,  lowerCamelCase     )
											lowerCamelCase__		:		str			=scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase     ).prev_sample

								return sample



					def 		snake_case       (       self							:  int     )->					Tuple:
								lowerCamelCase__		:		Dict			=DPMSolverSinglestepScheduler(**self.get_scheduler_config()     )
								lowerCamelCase__		:		Tuple			=50
								lowerCamelCase__		:		Dict			=self.dummy_model()
								lowerCamelCase__		:		Any			=self.dummy_sample_deter
								scheduler.set_timesteps(lowerCamelCase     )

								# make sure that the first t is uneven
								for i, t in enumerate(scheduler.timesteps[3:]     ):
											lowerCamelCase__		:		Any			=model(lowerCamelCase,  lowerCamelCase     )
											lowerCamelCase__		:		List[Any]			=scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase     ).prev_sample

								lowerCamelCase__		:		Optional[int]			=torch.mean(torch.abs(lowerCamelCase     )     )

								assert abs(result_mean.item() - 0.2_574     ) < 1E-3



					def 		snake_case       (       self							:  List[Any]     )->					List[str]:
								for timesteps in [25, 50, 100, 999, 1000]:
											self.check_over_configs(num_train_timesteps=lowerCamelCase     )



					def 		snake_case       (       self							:  int     )->					Optional[int]:
								# make sure that iterating over schedulers with same config names gives same results
								# for defaults
								lowerCamelCase__		:		Tuple			=DPMSolverSinglestepScheduler(**self.get_scheduler_config()     )
								lowerCamelCase__		:		List[Any]			=self.full_loop(scheduler=lowerCamelCase     )
								lowerCamelCase__		:		Tuple			=torch.mean(torch.abs(lowerCamelCase     )     )

								assert abs(result_mean.item() - 0.2_791     ) < 1E-3

								lowerCamelCase__		:		List[str]			=DEISMultistepScheduler.from_config(scheduler.config     )
								lowerCamelCase__		:		Tuple			=DPMSolverMultistepScheduler.from_config(scheduler.config     )
								lowerCamelCase__		:		List[str]			=UniPCMultistepScheduler.from_config(scheduler.config     )
								lowerCamelCase__		:		Any			=DPMSolverSinglestepScheduler.from_config(scheduler.config     )

								lowerCamelCase__		:		Optional[int]			=self.full_loop(scheduler=lowerCamelCase     )
								lowerCamelCase__		:		Dict			=torch.mean(torch.abs(lowerCamelCase     )     )

								assert abs(result_mean.item() - 0.2_791     ) < 1E-3



					def 		snake_case       (       self							:  str     )->					Any:
								self.check_over_configs(thresholding=lowerCamelCase     )
								for order in [1, 2, 3]:
											for solver_type in ["midpoint", "heun"]:
														for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample"]:
																				self.check_over_configs(
																				    thresholding=lowerCamelCase,  prediction_type=lowerCamelCase,  sample_max_value=lowerCamelCase,  algorithm_type='''dpmsolver++''',  solver_order=lowerCamelCase,  solver_type=lowerCamelCase,  )



					def 		snake_case       (       self							:  str     )->					Any:
								for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=lowerCamelCase     )



					def 		snake_case       (       self							:  Tuple     )->					Union[str, Any]:
								for algorithm_type in ["dpmsolver", "dpmsolver++"]:
											for solver_type in ["midpoint", "heun"]:
														for order in [1, 2, 3]:
																	for prediction_type in ["epsilon", "sample"]:
																				self.check_over_configs(
																				    solver_order=lowerCamelCase,  solver_type=lowerCamelCase,  prediction_type=lowerCamelCase,  algorithm_type=lowerCamelCase,  )
																				lowerCamelCase__		:		List[Any]			=self.full_loop(
																				    solver_order=lowerCamelCase,  solver_type=lowerCamelCase,  prediction_type=lowerCamelCase,  algorithm_type=lowerCamelCase,  )
																				assert not torch.isnan(lowerCamelCase     ).any(), "Samples have nan numbers"



					def 		snake_case       (       self							:  Dict     )->					Any:
								self.check_over_configs(lower_order_final=lowerCamelCase     )
								self.check_over_configs(lower_order_final=lowerCamelCase     )



					def 		snake_case       (       self							:  Any     )->					List[str]:
								self.check_over_configs(lambda_min_clipped=-float('''inf'''     )     )
								self.check_over_configs(lambda_min_clipped=-5.1     )



					def 		snake_case       (       self							:  List[Any]     )->					List[Any]:
								self.check_over_configs(variance_type=lowerCamelCase     )
								self.check_over_configs(variance_type='''learned_range'''     )



					def 		snake_case       (       self							:  List[str]     )->					List[str]:
								for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
											self.check_over_forward(num_inference_steps=lowerCamelCase,  time_step=0     )



					def 		snake_case       (       self							:  List[str]     )->					str:
								lowerCamelCase__		:		int			=self.full_loop()
								lowerCamelCase__		:		int			=torch.mean(torch.abs(lowerCamelCase     )     )

								assert abs(result_mean.item() - 0.2_791     ) < 1E-3



					def 		snake_case       (       self							:  Optional[int]     )->					Tuple:
								lowerCamelCase__		:		Optional[Any]			=self.full_loop(use_karras_sigmas=lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=torch.mean(torch.abs(lowerCamelCase     )     )

								assert abs(result_mean.item() - 0.2_248     ) < 1E-3



					def 		snake_case       (       self							:  List[str]     )->					Union[str, Any]:
								lowerCamelCase__		:		Dict			=self.full_loop(prediction_type='''v_prediction'''     )
								lowerCamelCase__		:		List[str]			=torch.mean(torch.abs(lowerCamelCase     )     )

								assert abs(result_mean.item() - 0.1_453     ) < 1E-3



					def 		snake_case       (       self							:  Optional[Any]     )->					Optional[Any]:
								lowerCamelCase__		:		int			=self.full_loop(prediction_type='''v_prediction''',  use_karras_sigmas=lowerCamelCase     )
								lowerCamelCase__		:		List[Any]			=torch.mean(torch.abs(lowerCamelCase     )     )

								assert abs(result_mean.item() - 0.0_649     ) < 1E-3




					def 		snake_case       (       self							:  Any     )->					Dict:
								lowerCamelCase__		:		Optional[int]			=self.scheduler_classes[0]
								lowerCamelCase__		:		Any			=self.get_scheduler_config(thresholding=lowerCamelCase,  dynamic_thresholding_ratio=0     )
								lowerCamelCase__		:		int			=scheduler_class(**lowerCamelCase     )

								lowerCamelCase__		:		Any			=10
								lowerCamelCase__		:		List[str]			=self.dummy_model()
								lowerCamelCase__		:		Dict			=self.dummy_sample_deter.half()
								scheduler.set_timesteps(lowerCamelCase     )

								for i, t in enumerate(scheduler.timesteps     ):
											lowerCamelCase__		:		int			=model(lowerCamelCase,  lowerCamelCase     )
											lowerCamelCase__		:		Optional[int]			=scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase     ).prev_sample

								assert sample.dtype == torch.floataa









"""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


_lowercase : Union[str, Any]      						=						["text", "image", "audio"]






def      snake_case__   (		__lowerCamelCase				:    List[str]	):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]			=[]

			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				:    List	):
			"""simple docstring"""
			lowerCamelCase__		:		Tuple			=[]

			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        __SCREAMING_SNAKE_CASE	:


					'''simple docstring'''





					def 		snake_case       (       self							:  Any     )->					Optional[Any]:
								self.assertTrue(hasattr(self.tool,  '''inputs'''     )     )
								self.assertTrue(hasattr(self.tool,  '''outputs'''     )     )

								lowerCamelCase__		:		Tuple			=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     )

								lowerCamelCase__		:		Optional[Any]			=self.tool.outputs
								for _output in outputs:
											self.assertTrue(_output in authorized_types     )



					def 		snake_case       (       self							:  Optional[int]     )->					Union[str, Any]:
								lowerCamelCase__		:		Optional[int]			=create_inputs(self.tool.inputs     )
								lowerCamelCase__		:		List[Any]			=self.tool(*lowerCamelCase     )

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

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



					def 		snake_case       (       self							:  Union[str, Any]     )->					List[str]:
								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 		snake_case       (       self							:  Union[str, Any]     )->					str:
								lowerCamelCase__		:		List[str]			=create_inputs(self.tool.inputs     )
								lowerCamelCase__		:		Optional[Any]			=self.tool(*lowerCamelCase     )

								if not isinstance(lowerCamelCase,  lowerCamelCase     ):
											lowerCamelCase__		:		Any			=[outputs]

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

								for output, output_type in zip(lowerCamelCase,  self.tool.outputs     ):
											lowerCamelCase__		:		List[Any]			=AGENT_TYPE_MAPPING[output_type]
											self.assertTrue(isinstance(lowerCamelCase,  lowerCamelCase     )     )




					def 		snake_case       (       self							:  Optional[Any]     )->					List[Any]:
								lowerCamelCase__		:		Optional[Any]			=create_inputs(self.tool.inputs     )

								lowerCamelCase__		:		List[str]			=[]

								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
								lowerCamelCase__		:		Any			=self.tool(*lowerCamelCase     )

								if not isinstance(lowerCamelCase,  lowerCamelCase     ):
											lowerCamelCase__		:		Optional[int]			=[outputs]

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




