





'''simple docstring'''

from __future__ import annotations

from typing import TypedDict
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	str
       _snake_case      :	int







def    a__							(							lowercase   :	str )						->						list[str]:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ):
          raise TypeError('''The parameter s type must be str.''' )

     return [s[i:] + s[:i] for i in range(len(lowercase ) )]







def    a__							(							lowercase   :	str )						->						BWTTransformDict:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ):
          raise TypeError('''The parameter s type must be str.''' )
     if not s:
          raise ValueError('''The parameter s must not be empty.''' )

     _UpperCamelCase							       =			all_rotations(lowercase )
     rotations.sort()  # sort the list of rotations in alphabetically order
     # make a string composed of the last char of each rotation
     _UpperCamelCase							       =			{
         "bwt_string": "".join([word[-1] for word in rotations] ),
         "idx_original_string": rotations.index(lowercase ),
     }
     return response







def    a__							(							lowercase   :	str,   lowercase   :	int )						->						str:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ):
          raise TypeError('''The parameter bwt_string type must be str.''' )
     if not bwt_string:
          raise ValueError('''The parameter bwt_string must not be empty.''' )
     try:
          _UpperCamelCase							       =			int(lowercase )
     except ValueError:
          raise TypeError(
              '''The parameter idx_original_string type must be int or passive'''
              ''' of cast to int.''' )
     if idx_original_string < 0:
          raise ValueError('''The parameter idx_original_string must not be lower than 0.''' )
     if idx_original_string >= len(lowercase ):
          raise ValueError(
              '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).''' )

     _UpperCamelCase							       =			[''''''] * len(lowercase )
     for _ in range(len(lowercase ) ):
          for i in range(len(lowercase ) ):
               _UpperCamelCase							       =			bwt_string[i] + ordered_rotations[i]
          ordered_rotations.sort()
     return ordered_rotations[idx_original_string]


if __name__ == "__main__":
   lowercase__		:     List[str]			=							'Provide a string that I will generate its BWT transform: '
   lowercase__		:     Optional[int]			=							input(entry_msg).strip()
   lowercase__		:     Dict			=							bwt_transform(s)
   print(
       F"""Burrows Wheeler transform for string '{s}' results """
       F"""in '{result['bwt_string']}'"""
   )
   lowercase__		:     Optional[Any]			=							reverse_bwt(result['bwt_string'], result['idx_original_string'])
   print(
       F"""Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' """
       F"""we get original string '{original_string}'"""
   )









import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  (DPMSolverSinglestepScheduler,)
						lowerCAmelCase_							=  (('''num_inference_steps''', 25),)






						def 			snake_case__		(		self				:	str			,      **__lowercase				:	Any		):






							"""simple docstring"""






							snake_case_						       =	{
							    "num_train_timesteps": 10_00,
							    "beta_start": 0.0001,
							    "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(**__lowercase		)
							return config






						def 			snake_case__		(		self				:	str			,      __lowercase				:	int=0			,      **__lowercase				:	str		):






							"""simple docstring"""






							snake_case_						       =	dict(self.forward_default_kwargs		)
							snake_case_						       =	kwargs.pop("num_inference_steps"			,      __lowercase		)
							snake_case_						       =	self.dummy_sample
							snake_case_						       =	0.1 * sample
							snake_case_						       =	[residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
								snake_case_						       =	self.get_scheduler_config(**__lowercase		)
								snake_case_						       =	scheduler_class(**__lowercase		)
								scheduler.set_timesteps(__lowercase		)
								# copy over dummy past residuals
								snake_case_						       =	dummy_past_residuals[: scheduler.config.solver_order]

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

								snake_case_       , snake_case_						       =	sample, sample
								for t in range(__lowercase			,      time_step + scheduler.config.solver_order + 1		):
									snake_case_						       =	scheduler.step(__lowercase			,      __lowercase			,      __lowercase			,      **__lowercase		).prev_sample
									snake_case_						       =	new_scheduler.step(__lowercase			,      __lowercase			,      __lowercase			,      **__lowercase		).prev_sample

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






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






							"""simple docstring"""






							pass






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	Optional[int]=0			,      **__lowercase				:	int		):






							"""simple docstring"""






							snake_case_						       =	dict(self.forward_default_kwargs		)
							snake_case_						       =	kwargs.pop("num_inference_steps"			,      __lowercase		)
							snake_case_						       =	self.dummy_sample
							snake_case_						       =	0.1 * sample
							snake_case_						       =	[residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
								snake_case_						       =	self.get_scheduler_config()
								snake_case_						       =	scheduler_class(**__lowercase		)
								scheduler.set_timesteps(__lowercase		)

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

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

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

								snake_case_						       =	scheduler.step(__lowercase			,      __lowercase			,      __lowercase			,      **__lowercase		).prev_sample
								snake_case_						       =	new_scheduler.step(__lowercase			,      __lowercase			,      __lowercase			,      **__lowercase		).prev_sample

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






						def 			snake_case__		(		self				:	List[str]			,      __lowercase				:	Any=None			,      **__lowercase				:	List[Any]		):






							"""simple docstring"""






							if scheduler is None:
								snake_case_						       =	self.scheduler_classes[0]
								snake_case_						       =	self.get_scheduler_config(**__lowercase		)
								snake_case_						       =	scheduler_class(**__lowercase		)

							snake_case_						       =	self.scheduler_classes[0]
							snake_case_						       =	self.get_scheduler_config(**__lowercase		)
							snake_case_						       =	scheduler_class(**__lowercase		)

							snake_case_						       =	10
							snake_case_						       =	self.dummy_model()
							snake_case_						       =	self.dummy_sample_deter
							scheduler.set_timesteps(__lowercase		)

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

							return sample






						def 			snake_case__		(		self				:	Any		):






							"""simple docstring"""






							snake_case_						       =	DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
							snake_case_						       =	50
							snake_case_						       =	self.dummy_model()
							snake_case_						       =	self.dummy_sample_deter
							scheduler.set_timesteps(__lowercase		)

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

							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.2574		) < 1E-3






						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							for timesteps in [25, 50, 1_00, 9_99, 10_00]:
								self.check_over_configs(num_train_timesteps=__lowercase		)






						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
							snake_case_						       =	self.full_loop(scheduler=__lowercase		)
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.2791		) < 1E-3

							snake_case_						       =	DEISMultistepScheduler.from_config(scheduler.config		)
							snake_case_						       =	DPMSolverMultistepScheduler.from_config(scheduler.config		)
							snake_case_						       =	UniPCMultistepScheduler.from_config(scheduler.config		)
							snake_case_						       =	DPMSolverSinglestepScheduler.from_config(scheduler.config		)

							snake_case_						       =	self.full_loop(scheduler=__lowercase		)
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.2791		) < 1E-3






						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							self.check_over_configs(thresholding=__lowercase		)
							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=__lowercase			,      prediction_type=__lowercase			,      sample_max_value=__lowercase			,      algorithm_type="dpmsolver++"			,      solver_order=__lowercase			,      solver_type=__lowercase			,      )






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






							"""simple docstring"""






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






						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							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=__lowercase			,      solver_type=__lowercase			,      prediction_type=__lowercase			,      algorithm_type=__lowercase			,      )
											snake_case_						       =	self.full_loop(
											    solver_order=__lowercase			,      solver_type=__lowercase			,      prediction_type=__lowercase			,      algorithm_type=__lowercase			,      )
											assert not torch.isnan(__lowercase		).any(), "Samples have nan numbers"






						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							self.check_over_configs(lower_order_final=__lowercase		)
							self.check_over_configs(lower_order_final=__lowercase		)






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






							"""simple docstring"""






							self.check_over_configs(lambda_min_clipped=-float("inf"		)		)
							self.check_over_configs(lambda_min_clipped=-5.1		)






						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							self.check_over_configs(variance_type=__lowercase		)
							self.check_over_configs(variance_type="learned_range"		)






						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
								self.check_over_forward(num_inference_steps=__lowercase			,      time_step=0		)






						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	self.full_loop()
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.2791		) < 1E-3






						def 			snake_case__		(		self				:	Any		):






							"""simple docstring"""






							snake_case_						       =	self.full_loop(use_karras_sigmas=__lowercase		)
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.2248		) < 1E-3






						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							snake_case_						       =	self.full_loop(prediction_type="v_prediction"		)
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.1453		) < 1E-3






						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	self.full_loop(prediction_type="v_prediction"			,      use_karras_sigmas=__lowercase		)
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.0649		) < 1E-3






						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							snake_case_						       =	self.scheduler_classes[0]
							snake_case_						       =	self.get_scheduler_config(thresholding=__lowercase			,      dynamic_thresholding_ratio=0		)
							snake_case_						       =	scheduler_class(**__lowercase		)

							snake_case_						       =	10
							snake_case_						       =	self.dummy_model()
							snake_case_						       =	self.dummy_sample_deter.half()
							scheduler.set_timesteps(__lowercase		)

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

							assert sample.dtype == torch.floataa
