

import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   str     					=				(DDPMParallelScheduler,)




	def        lowerCAmelCase_       (     self						:     List[Any]					,      **__A						:     int							):
		__A :			int		    =			{
		    """num_train_timesteps""": 1000,
		    """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        lowerCAmelCase_       (     self						:     List[str]							):
		for timesteps in [1, 5, 100, 1000]:
			self.check_over_configs(num_train_timesteps=__A							)




	def        lowerCAmelCase_       (     self						:     int							):
		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        lowerCAmelCase_       (     self						:     Any							):
		for schedule in ["linear", "squaredcos_cap_v2"]:
			self.check_over_configs(beta_schedule=__A							)




	def        lowerCAmelCase_       (     self						:     Any							):
		for variance in ["fixed_small", "fixed_large", "other"]:
			self.check_over_configs(variance_type=__A							)




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		for clip_sample in [True, False]:
			self.check_over_configs(clip_sample=__A							)




	def        lowerCAmelCase_       (     self						:     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        lowerCAmelCase_       (     self						:     int							):
		for prediction_type in ["epsilon", "sample", "v_prediction"]:
			self.check_over_configs(prediction_type=__A							)




	def        lowerCAmelCase_       (     self						:     int							):
		for t in [0, 500, 999]:
			self.check_over_forward(time_step=__A							)




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		__A :			str		    =			self.scheduler_classes[0]
		__A :			Tuple		    =			self.get_scheduler_config()
		__A :			int		    =			scheduler_class(**__A							)

		assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(487							) - 0.0_0_9_7_9							)							) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.0_2							)							) < 1e-5




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			int		    =			self.scheduler_classes[0]
		__A :			Optional[int]		    =			self.get_scheduler_config()
		__A :			List[Any]		    =			scheduler_class(**__A							)

		__A :			Any		    =			len(__A							)

		__A :			Tuple		    =			self.dummy_model()
		__A :			Union[str, Any]		    =			self.dummy_sample_deter
		__A :			List[Any]		    =			self.dummy_sample_deter + 0.1
		__A :			Optional[Any]		    =			self.dummy_sample_deter - 0.1

		__A :			Dict		    =			samplea.shape[0]
		__A :			Optional[int]		    =			torch.stack([samplea, samplea, samplea]					,      dim=0							)
		__A :			Dict		    =			torch.arange(__A							)[0:3, None].repeat(1					,      __A							)

		__A :			Optional[Any]		    =			model(samples.flatten(0					,      1							)					,      timesteps.flatten(0					,      1							)							)
		__A :			Dict		    =			scheduler.batch_step_no_noise(__A					,      timesteps.flatten(0					,      1							)					,      samples.flatten(0					,      1							)							)

		__A :			List[str]		    =			torch.sum(torch.abs(__A							)							)
		__A :			List[str]		    =			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        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			int		    =			self.scheduler_classes[0]
		__A :			int		    =			self.get_scheduler_config()
		__A :			Dict		    =			scheduler_class(**__A							)

		__A :			Optional[Any]		    =			len(__A							)

		__A :			Tuple		    =			self.dummy_model()
		__A :			Optional[Any]		    =			self.dummy_sample_deter
		__A :			List[str]		    =			torch.manual_seed(0							)

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

			# 2. predict previous mean of sample x_t-1
			__A :			List[str]		    =			scheduler.step(__A					,      __A					,      __A					,      generator=__A							).prev_sample

			__A :			Union[str, Any]		    =			pred_prev_sample

		__A :			List[Any]		    =			torch.sum(torch.abs(__A							)							)
		__A :			Union[str, Any]		    =			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        lowerCAmelCase_       (     self						:     int							):
		__A :			int		    =			self.scheduler_classes[0]
		__A :			int		    =			self.get_scheduler_config(prediction_type="""v_prediction"""							)
		__A :			int		    =			scheduler_class(**__A							)

		__A :			Dict		    =			len(__A							)

		__A :			Any		    =			self.dummy_model()
		__A :			str		    =			self.dummy_sample_deter
		__A :			Union[str, Any]		    =			torch.manual_seed(0							)

		for t in reversed(range(__A							)							):
			# 1. predict noise residual
			__A :			Optional[Any]		    =			model(__A					,      __A							)

			# 2. predict previous mean of sample x_t-1
			__A :			Tuple		    =			scheduler.step(__A					,      __A					,      __A					,      generator=__A							).prev_sample

			__A :			Dict		    =			pred_prev_sample

		__A :			Union[str, Any]		    =			torch.sum(torch.abs(__A							)							)
		__A :			Union[str, Any]		    =			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        lowerCAmelCase_       (     self						:     Any							):
		__A :			List[str]		    =			self.scheduler_classes[0]
		__A :			Any		    =			self.get_scheduler_config()
		__A :			Optional[Any]		    =			scheduler_class(**__A							)

		__A :			Optional[Any]		    =			[100, 87, 50, 1, 0]

		scheduler.set_timesteps(timesteps=__A							)

		__A :			List[Any]		    =			scheduler.timesteps

		for i, timestep in enumerate(__A							):
			if i == len(__A							) - 1:
				__A :			List[str]		    =			-1
			else:
				__A :			int		    =			timesteps[i + 1]

			__A :			Any		    =			scheduler.previous_timestep(__A							)
			__A :			Any		    =			prev_t.item()

			self.assertEqual(__A					,      __A							)




	def        lowerCAmelCase_       (     self						:     Any							):
		__A :			str		    =			self.scheduler_classes[0]
		__A :			int		    =			self.get_scheduler_config()
		__A :			Optional[int]		    =			scheduler_class(**__A							)

		__A :			Any		    =			[100, 87, 50, 51, 0]

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




	def        lowerCAmelCase_       (     self						:     Any							):
		__A :			List[str]		    =			self.scheduler_classes[0]
		__A :			Dict		    =			self.get_scheduler_config()
		__A :			Tuple		    =			scheduler_class(**__A							)

		__A :			Tuple		    =			[100, 87, 50, 1, 0]
		__A :			Union[str, Any]		    =			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        lowerCAmelCase_       (     self						:     Any							):
		__A :			Tuple		    =			self.scheduler_classes[0]
		__A :			Optional[int]		    =			self.get_scheduler_config()
		__A :			Dict		    =			scheduler_class(**__A							)

		__A :			List[Any]		    =			[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 manim import *





class 				a__					(						A__       ):



     def  lowerCamelCase_						(							self :List[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Tuple   	=Rectangle(height=0.5      ,  width=0.5   )
      UpperCamelCase_     :					Any   	=Rectangle(height=0.46      ,  width=0.46   ).set_stroke(width=0   )

      UpperCamelCase_     :					Any   	=[mem.copy() for i in range(6   )]
      UpperCamelCase_     :					Optional[int]   	=[mem.copy() for i in range(6   )]
      UpperCamelCase_     :					List[str]   	=VGroup(*_lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0   )
      UpperCamelCase_     :					Optional[int]   	=VGroup(*_lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0   )
      UpperCamelCase_     :					Union[str, Any]   	=VGroup(_lowerCamelCase      ,  _lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0   )
      UpperCamelCase_     :					str   	=Text('CPU'      ,  font_size=24   )
      UpperCamelCase_     :					str   	=Group(_lowerCamelCase      ,  _lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0.5      ,  aligned_edge=_lowerCamelCase   )
      cpu.move_to([-2.5, -0.5, 0]   )
      self.add(_lowerCamelCase   )

      UpperCamelCase_     :					int   	=[mem.copy() for i in range(4   )]
      UpperCamelCase_     :					Any   	=VGroup(*_lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0   )
      UpperCamelCase_     :					List[Any]   	=Text('GPU'      ,  font_size=24   )
      UpperCamelCase_     :					Any   	=Group(_lowerCamelCase      ,  _lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0.5      ,  aligned_edge=_lowerCamelCase   )
      gpu.move_to([-1, -1, 0]   )
      self.add(_lowerCamelCase   )

      UpperCamelCase_     :					List[str]   	=[mem.copy() for i in range(6   )]
      UpperCamelCase_     :					str   	=VGroup(*_lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0   )

      UpperCamelCase_     :					Optional[int]   	=Text('Model'      ,  font_size=24   )
      UpperCamelCase_     :					List[Any]   	=Group(_lowerCamelCase      ,  _lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0.5      ,  aligned_edge=_lowerCamelCase   )
      model.move_to([3, -1.0, 0]   )
      self.add(_lowerCamelCase   )

      UpperCamelCase_     :					int   	=[]
      for i, rect in enumerate(_lowerCamelCase   ):
       rect.set_stroke(_lowerCamelCase   )
       # target = fill.copy().set_fill(YELLOW, opacity=0.7)
       # target.move_to(rect)
       # self.add(target)

       UpperCamelCase_     :					str   	=Rectangle(height=0.46 / 4      ,  width=0.46 / 3   ).set_stroke(width=0.0   ).set_fill(_lowerCamelCase      ,  opacity=0.7   )

       if i == 0:
        cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT   )      ,  buff=0.02      ,  direction=_lowerCamelCase   )
        cpu_target.set_x(cpu_target.get_x() + 0.1   )
       elif i == 3:
        cpu_target.next_to(cpu_targs[0]      ,  direction=_lowerCamelCase      ,  buff=0.0   )
       else:
        cpu_target.next_to(cpu_targs[i - 1]      ,  direction=_lowerCamelCase      ,  buff=0.0   )
       self.add(_lowerCamelCase   )
       cpu_targs.append(_lowerCamelCase   )

      UpperCamelCase_     :					List[str]   	=[mem.copy() for i in range(6   )]
      UpperCamelCase_     :					Any   	=VGroup(*_lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0   )

      UpperCamelCase_     :					Optional[int]   	=Text('Loaded Checkpoint'      ,  font_size=24   )
      UpperCamelCase_     :					List[Any]   	=Group(_lowerCamelCase      ,  _lowerCamelCase   ).arrange(_lowerCamelCase      ,  aligned_edge=_lowerCamelCase      ,  buff=0.4   )
      checkpoint.move_to([3, 0.5, 0]   )

      UpperCamelCase_     :					int   	=Square(side_length=2.2   )
      key.move_to([-5, 2, 0]   )

      UpperCamelCase_     :					Optional[Any]   	=MarkupText(
          f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''      ,  font_size=18      ,  )

      key_text.move_to([-5, 2.4, 0]   )

      self.add(_lowerCamelCase      ,  _lowerCamelCase   )

      UpperCamelCase_     :					Any   	=MarkupText(
          f'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''      ,  font_size=18      ,  )

      blue_text.next_to(_lowerCamelCase      ,  DOWN * 2.4      ,  aligned_edge=key_text.get_left()   )

      UpperCamelCase_     :					Dict   	=MarkupText(
          f'''Next, a <i><span fgcolor="{BLUE}">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor="{BLUE}">single shard</span>.'''      ,  font_size=24      ,  )
      step_a.move_to([2, 2, 0]   )
      self.play(Write(_lowerCamelCase   )      ,  Write(_lowerCamelCase   )   )

      self.play(Write(_lowerCamelCase      ,  run_time=1   )      ,  Create(_lowerCamelCase      ,  run_time=1   )   )

      UpperCamelCase_     :					Union[str, Any]   	=[]
      UpperCamelCase_     :					List[Any]   	=[]
      for i, rect in enumerate(_lowerCamelCase   ):
       UpperCamelCase_     :					str   	=fill.copy().set_fill(_lowerCamelCase      ,  opacity=0.7   )
       target.move_to(_lowerCamelCase   )
       first_animations.append(GrowFromCenter(_lowerCamelCase      ,  run_time=1   )   )

       UpperCamelCase_     :					Any   	=target.copy()
       cpu_target.generate_target()
       if i < 5:
        cpu_target.target.move_to(cpu_left_col_base[i + 1]   )
       else:
        cpu_target.target.move_to(cpu_right_col_base[i - 5]   )
       second_animations.append(MoveToTarget(_lowerCamelCase      ,  run_time=1.5   )   )

      self.play(*_lowerCamelCase   )
      self.play(*_lowerCamelCase   )
      self.wait()
