"""simple docstring"""



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						List[str]					     =				(DEISMultistepScheduler,)
			_UpperCamelCase  :						Any					     =				(('num_inference_steps', 25),)





			def 		lowerCamelCase					(			self   ,      **snake_case_							):







										'''simple docstring'''


										A__       :     Any								=			{
										    """num_train_timesteps""": 1_000,
										    """beta_start""": 0.00_01,
										    """beta_end""": 0.02,
										    """beta_schedule""": """linear""",
										    """solver_order""": 2,
										}

										config.update(**snake_case_							)
										return config





			def 		lowerCamelCase					(			self   ,      snake_case_=0   ,      **snake_case_							):







										'''simple docstring'''


										A__       :     Dict								=			dict(self.forward_default_kwargs							)
										A__       :     Optional[int]								=			kwargs.pop("""num_inference_steps"""   ,      snake_case_							)
										A__       :     str								=			self.dummy_sample
										A__       :     Any								=			0.1 * sample
										A__       :     Tuple								=			[residual + 0.2, residual + 0.15, residual + 0.10]

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

																	with tempfile.TemporaryDirectory() as tmpdirname:
																								scheduler.save_config(snake_case_							)
																								A__       :     List[str]								=			scheduler_class.from_pretrained(snake_case_							)
																								new_scheduler.set_timesteps(snake_case_							)
																								# copy over dummy past residuals
																								A__       :     Optional[int]								=			dummy_past_residuals[: new_scheduler.config.solver_order]

																	A__				,							A__       :     Any								=			sample, sample
																	for t in range(snake_case_   ,      time_step + scheduler.config.solver_order + 1							):
																								A__       :     Optional[Any]								=			scheduler.step(snake_case_   ,      snake_case_   ,      snake_case_   ,      **snake_case_							).prev_sample
																								A__       :     Any								=			new_scheduler.step(snake_case_   ,      snake_case_   ,      snake_case_   ,      **snake_case_							).prev_sample

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





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										pass





			def 		lowerCamelCase					(			self   ,      snake_case_=0   ,      **snake_case_							):







										'''simple docstring'''


										A__       :     Optional[int]								=			dict(self.forward_default_kwargs							)
										A__       :     Dict								=			kwargs.pop("""num_inference_steps"""   ,      snake_case_							)
										A__       :     List[str]								=			self.dummy_sample
										A__       :     List[Any]								=			0.1 * sample
										A__       :     str								=			[residual + 0.2, residual + 0.15, residual + 0.10]

										for scheduler_class in self.scheduler_classes:
																	A__       :     Dict								=			self.get_scheduler_config()
																	A__       :     List[str]								=			scheduler_class(**snake_case_							)
																	scheduler.set_timesteps(snake_case_							)

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

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

																								# copy over dummy past residual (must be after setting timesteps)
																								A__       :     Dict								=			dummy_past_residuals[: new_scheduler.config.solver_order]

																	A__       :     str								=			scheduler.step(snake_case_   ,      snake_case_   ,      snake_case_   ,      **snake_case_							).prev_sample
																	A__       :     Union[str, Any]								=			new_scheduler.step(snake_case_   ,      snake_case_   ,      snake_case_   ,      **snake_case_							).prev_sample

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





			def 		lowerCamelCase					(			self   ,      snake_case_=None   ,      **snake_case_							):







										'''simple docstring'''


										if scheduler is None:
																	A__       :     int								=			self.scheduler_classes[0]
																	A__       :     Dict								=			self.get_scheduler_config(**snake_case_							)
																	A__       :     List[str]								=			scheduler_class(**snake_case_							)

										A__       :     Tuple								=			self.scheduler_classes[0]
										A__       :     List[Any]								=			self.get_scheduler_config(**snake_case_							)
										A__       :     List[Any]								=			scheduler_class(**snake_case_							)

										A__       :     Any								=			10
										A__       :     int								=			self.dummy_model()
										A__       :     int								=			self.dummy_sample_deter
										scheduler.set_timesteps(snake_case_							)

										for i, t in enumerate(scheduler.timesteps							):
																	A__       :     List[str]								=			model(snake_case_   ,      snake_case_							)
																	A__       :     List[str]								=			scheduler.step(snake_case_   ,      snake_case_   ,      snake_case_							).prev_sample

										return sample





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Any								=			dict(self.forward_default_kwargs							)

										A__       :     Optional[Any]								=			kwargs.pop("""num_inference_steps"""   ,      snake_case_							)

										for scheduler_class in self.scheduler_classes:
																	A__       :     Tuple								=			self.get_scheduler_config()
																	A__       :     List[str]								=			scheduler_class(**snake_case_							)

																	A__       :     Any								=			self.dummy_sample
																	A__       :     Optional[int]								=			0.1 * sample

																	if num_inference_steps is not None and hasattr(snake_case_   ,      """set_timesteps"""							):
																								scheduler.set_timesteps(snake_case_							)
																	elif num_inference_steps is not None and not hasattr(snake_case_   ,      """set_timesteps"""							):
																								A__       :     str								=			num_inference_steps

																	# copy over dummy past residuals (must be done after set_timesteps)
																	A__       :     List[str]								=			[residual + 0.2, residual + 0.15, residual + 0.10]
																	A__       :     Any								=			dummy_past_residuals[: scheduler.config.solver_order]

																	A__       :     int								=			scheduler.timesteps[5]
																	A__       :     Any								=			scheduler.timesteps[6]

																	A__       :     Optional[int]								=			scheduler.step(snake_case_   ,      snake_case_   ,      snake_case_   ,      **snake_case_							).prev_sample
																	A__       :     Dict								=			scheduler.step(snake_case_   ,      snake_case_   ,      snake_case_   ,      **snake_case_							).prev_sample

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





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[int]								=			DEISMultistepScheduler(**self.get_scheduler_config()							)
										A__       :     int								=			self.full_loop(scheduler=snake_case_							)
										A__       :     Optional[int]								=			torch.mean(torch.abs(snake_case_							)							)

										assert abs(result_mean.item() - 0.2_39_16							) < 1E-3

										A__       :     Optional[Any]								=			DPMSolverSinglestepScheduler.from_config(scheduler.config							)
										A__       :     Optional[Any]								=			DPMSolverMultistepScheduler.from_config(scheduler.config							)
										A__       :     Union[str, Any]								=			UniPCMultistepScheduler.from_config(scheduler.config							)
										A__       :     Any								=			DEISMultistepScheduler.from_config(scheduler.config							)

										A__       :     int								=			self.full_loop(scheduler=snake_case_							)
										A__       :     Dict								=			torch.mean(torch.abs(snake_case_							)							)

										assert abs(result_mean.item() - 0.2_39_16							) < 1E-3





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										for timesteps in [25, 50, 100, 999, 1_000]:
																	self.check_over_configs(num_train_timesteps=snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										self.check_over_configs(thresholding=snake_case_							)
										for order in [1, 2, 3]:
																	for solver_type in ["logrho"]:
																								for threshold in [0.5, 1.0, 2.0]:
																															for prediction_type in ["epsilon", "sample"]:
																																						self.check_over_configs(
																																						    thresholding=snake_case_   ,      prediction_type=snake_case_   ,      sample_max_value=snake_case_   ,      algorithm_type="""deis"""   ,      solver_order=snake_case_   ,      solver_type=snake_case_   ,      )





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


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





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										for algorithm_type in ["deis"]:
																	for solver_type in ["logrho"]:
																								for order in [1, 2, 3]:
																															for prediction_type in ["epsilon", "sample"]:
																																						self.check_over_configs(
																																						    solver_order=snake_case_   ,      solver_type=snake_case_   ,      prediction_type=snake_case_   ,      algorithm_type=snake_case_   ,      )
																																						A__       :     List[str]								=			self.full_loop(
																																						    solver_order=snake_case_   ,      solver_type=snake_case_   ,      prediction_type=snake_case_   ,      algorithm_type=snake_case_   ,      )
																																						assert not torch.isnan(snake_case_							).any(), "Samples have nan numbers"





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										self.check_over_configs(lower_order_final=snake_case_							)
										self.check_over_configs(lower_order_final=snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
																	self.check_over_forward(num_inference_steps=snake_case_   ,      time_step=0							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			self.full_loop()
										A__       :     Dict								=			torch.mean(torch.abs(snake_case_							)							)

										assert abs(result_mean.item() - 0.2_39_16							) < 1E-3





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Any								=			self.full_loop(prediction_type="""v_prediction"""							)
										A__       :     Union[str, Any]								=			torch.mean(torch.abs(snake_case_							)							)

										assert abs(result_mean.item() - 0.0_91							) < 1E-3





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     str								=			self.scheduler_classes[0]
										A__       :     Tuple								=			self.get_scheduler_config(thresholding=snake_case_   ,      dynamic_thresholding_ratio=0							)
										A__       :     str								=			scheduler_class(**snake_case_							)

										A__       :     Tuple								=			10
										A__       :     str								=			self.dummy_model()
										A__       :     List[str]								=			self.dummy_sample_deter.half()
										scheduler.set_timesteps(snake_case_							)

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

										assert sample.dtype == torch.floataa






"""simple docstring"""



import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin
class 						__UpperCAmelCase    (unittest.TestCase							,   __A     ):







			'''simple docstring'''





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[Any]								=			load_tool("""text-classification"""							)
										self.tool.setup()
										A__       :     Any								=			load_tool("""text-classification"""   ,      remote=snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     int								=			self.tool("""That's quite cool"""   ,      ["""positive""", """negative"""]							)
										self.assertEqual(snake_case_   ,      """positive"""							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			self.remote_tool("""That's quite cool"""   ,      ["""positive""", """negative"""]							)
										self.assertEqual(snake_case_   ,      """positive"""							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     str								=			self.tool(text="""That's quite cool"""   ,      labels=["""positive""", """negative"""]							)
										self.assertEqual(snake_case_   ,      """positive"""							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     int								=			self.remote_tool(text="""That's quite cool"""   ,      labels=["""positive""", """negative"""]							)
										self.assertEqual(snake_case_   ,      """positive"""							)





