





'''simple docstring'''

import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class       UpperCAmelCase (						_lowercase  ):
  UpperCAmelCase					:	int 			=		(DDPMParallelScheduler,)




  def 				UpperCAmelCase__   (self      :	int							,			**A__      :	List[Any]				)					-> Tuple:
      lowercase         = {
          "num_train_timesteps": 1_0_0_0,
          "beta_start": 0.0_0_0_1,
          "beta_end": 0.0_2,
          "beta_schedule": "linear",
          "variance_type": "fixed_small",
          "clip_sample": True,
      }

      config.update(**A__				)
      return config




  def 				UpperCAmelCase__   (self      :	Dict				)					-> Union[str, Any]:
      for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
          self.check_over_configs(num_train_timesteps=A__				)




  def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> str:
      for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]							,			[0.0_0_2, 0.0_2, 0.2, 2]				):
          self.check_over_configs(beta_start=A__							,			beta_end=A__				)




  def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Tuple:
      for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=A__				)




  def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Optional[int]:
      for variance in ["fixed_small", "fixed_large", "other"]:
          self.check_over_configs(variance_type=A__				)




  def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Optional[int]:
      for clip_sample in [True, False]:
          self.check_over_configs(clip_sample=A__				)




  def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Optional[int]:
      self.check_over_configs(thresholding=A__				)
      for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(
                  thresholding=A__							,			prediction_type=A__							,			sample_max_value=A__							,			)




  def 				UpperCAmelCase__   (self      :	List[str]				)					-> Any:
      for prediction_type in ["epsilon", "sample", "v_prediction"]:
          self.check_over_configs(prediction_type=A__				)




  def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> Union[str, Any]:
      for t in [0, 5_0_0, 9_9_9]:
          self.check_over_forward(time_step=A__				)




  def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> Optional[Any]:
      lowercase         = self.scheduler_classes[0]
      lowercase         = self.get_scheduler_config()
      lowercase         = scheduler_class(**A__				)

      assert torch.sum(torch.abs(scheduler._get_variance(0				) - 0.0				)				) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_8_7				) - 0.0_0_9_7_9				)				) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_9_9				) - 0.0_2				)				) < 1e-5




  def 				UpperCAmelCase__   (self      :	Tuple				)					-> int:
      lowercase         = self.scheduler_classes[0]
      lowercase         = self.get_scheduler_config()
      lowercase         = scheduler_class(**A__				)

      lowercase         = len(A__				)

      lowercase         = self.dummy_model()
      lowercase         = self.dummy_sample_deter
      lowercase         = self.dummy_sample_deter + 0.1
      lowercase         = self.dummy_sample_deter - 0.1

      lowercase         = samplea.shape[0]
      lowercase         = torch.stack([samplea, samplea, samplea]							,			dim=0				)
      lowercase         = torch.arange(A__				)[0:3, None].repeat(1							,			A__				)

      lowercase         = model(samples.flatten(0							,			1				)							,			timesteps.flatten(0							,			1				)				)
      lowercase         = scheduler.batch_step_no_noise(A__							,			timesteps.flatten(0							,			1				)							,			samples.flatten(0							,			1				)				)

      lowercase         = torch.sum(torch.abs(A__				)				)
      lowercase         = torch.mean(torch.abs(A__				)				)

      assert abs(result_sum.item() - 1_1_5_3.1_8_3_3				) < 1e-2
      assert abs(result_mean.item() - 0.5_0_0_5				) < 1e-3




  def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> int:
      lowercase         = self.scheduler_classes[0]
      lowercase         = self.get_scheduler_config()
      lowercase         = scheduler_class(**A__				)

      lowercase         = len(A__				)

      lowercase         = self.dummy_model()
      lowercase         = self.dummy_sample_deter
      lowercase         = torch.manual_seed(0				)

      for t in reversed(range(A__				)				):
          # 1. predict noise residual
          lowercase         = model(A__							,			A__				)

          # 2. predict previous mean of sample x_t-1
          lowercase         = scheduler.step(A__							,			A__							,			A__							,			generator=A__				).prev_sample

          lowercase         = pred_prev_sample

      lowercase         = torch.sum(torch.abs(A__				)				)
      lowercase         = torch.mean(torch.abs(A__				)				)

      assert abs(result_sum.item() - 2_5_8.9_6_0_6				) < 1e-2
      assert abs(result_mean.item() - 0.3_3_7_2				) < 1e-3




  def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Tuple:
      lowercase         = self.scheduler_classes[0]
      lowercase         = self.get_scheduler_config(prediction_type="v_prediction"				)
      lowercase         = scheduler_class(**A__				)

      lowercase         = len(A__				)

      lowercase         = self.dummy_model()
      lowercase         = self.dummy_sample_deter
      lowercase         = torch.manual_seed(0				)

      for t in reversed(range(A__				)				):
          # 1. predict noise residual
          lowercase         = model(A__							,			A__				)

          # 2. predict previous mean of sample x_t-1
          lowercase         = scheduler.step(A__							,			A__							,			A__							,			generator=A__				).prev_sample

          lowercase         = pred_prev_sample

      lowercase         = torch.sum(torch.abs(A__				)				)
      lowercase         = torch.mean(torch.abs(A__				)				)

      assert abs(result_sum.item() - 2_0_2.0_2_9_6				) < 1e-2
      assert abs(result_mean.item() - 0.2_6_3_1				) < 1e-3




  def 				UpperCAmelCase__   (self      :	Dict				)					-> Dict:
      lowercase         = self.scheduler_classes[0]
      lowercase         = self.get_scheduler_config()
      lowercase         = scheduler_class(**A__				)

      lowercase         = [1_0_0, 8_7, 5_0, 1, 0]

      scheduler.set_timesteps(timesteps=A__				)

      lowercase         = scheduler.timesteps

      for i, timestep in enumerate(A__				):
          if i == len(A__				) - 1:
              lowercase         = -1
          else:
              lowercase         = timesteps[i + 1]

          lowercase         = scheduler.previous_timestep(A__				)
          lowercase         = prev_t.item()

          self.assertEqual(A__							,			A__				)




  def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> List[Any]:
      lowercase         = self.scheduler_classes[0]
      lowercase         = self.get_scheduler_config()
      lowercase         = scheduler_class(**A__				)

      lowercase         = [1_0_0, 8_7, 5_0, 5_1, 0]

      with self.assertRaises(A__							,			msg="`custom_timesteps` must be in descending order."				):
          scheduler.set_timesteps(timesteps=A__				)




  def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> List[Any]:
      lowercase         = self.scheduler_classes[0]
      lowercase         = self.get_scheduler_config()
      lowercase         = scheduler_class(**A__				)

      lowercase         = [1_0_0, 8_7, 5_0, 1, 0]
      lowercase         = len(A__				)

      with self.assertRaises(A__							,			msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."				):
          scheduler.set_timesteps(num_inference_steps=A__							,			timesteps=A__				)





  def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Optional[Any]:
      lowercase         = self.scheduler_classes[0]
      lowercase         = self.get_scheduler_config()
      lowercase         = scheduler_class(**A__				)

      lowercase         = [scheduler.config.num_train_timesteps]

      with self.assertRaises(
          A__							,			msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"							,			):
          scheduler.set_timesteps(timesteps=A__				)











'''simple docstring'''

from __future__ import annotations

class       UpperCAmelCase :




		def __init__(self      :	Dict							,			A__      :	str							,			A__      :	str				)					-> str:
						lowercase   ,							lowercase         = text, pattern
						lowercase   ,							lowercase         = len(A__				), len(A__				)




		def 				UpperCAmelCase__   (self      :	Any							,			A__      :	str				)					-> int:

						for i in range(self.patLen - 1							,			-1							,			-1				):
										if char == self.pattern[i]:
														return i
						return -1




		def 				UpperCAmelCase__   (self      :	List[Any]							,			A__      :	int				)					-> int:

						for i in range(self.patLen - 1							,			-1							,			-1				):
										if self.pattern[i] != self.text[current_pos + i]:
														return current_pos + i
						return -1





		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> list[int]:
						# searches pattern in text and returns index positions
						lowercase         = []
						for i in range(self.textLen - self.patLen + 1				):
										lowercase         = self.mismatch_in_text(A__				)
										if mismatch_index == -1:
														positions.append(A__				)
										else:
														lowercase         = self.match_in_pattern(self.text[mismatch_index]				)
														lowercase         = (
														    mismatch_index - match_index
														)  # shifting index lgtm [py/multiple-definition]
						return positions




__lowerCamelCase : List[str]											=			"ABAABA"
__lowerCamelCase : List[str]											=			"AB"
__lowerCamelCase : Union[str, Any]											=			BoyerMooreSearch(text, pattern)
__lowerCamelCase : List[str]											=			bms.bad_character_heuristic()

if len(positions) == 0:
			print("No match found")
else:
			print("Pattern found in following positions: ")
			print(positions)




