


'''simple docstring'''
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class 						_lowerCamelCase					(    snake_case_				):




	'''simple docstring'''
	__lowercase				:		List[Any]    							=						(DDIMParallelScheduler,)
	__lowercase				:		Optional[int]    							=						(('''eta''', 0.0), ('''num_inference_steps''', 5_0))


	def  snake_case__   (      self      ,      **__lowercase						):

		"""simple docstring"""







		__A  :		Dict       =			{
		    'num_train_timesteps': 1_000,
		    'beta_start': 0.0_0_0_1,
		    'beta_end': 0.0_2,
		    'beta_schedule': 'linear',
		    'clip_sample': True,
		}

		config.update(**__lowercase						)
		return config


	def  snake_case__   (      self      ,      **__lowercase						):

		"""simple docstring"""







		__A  :		Union[str, Any]       =			self.scheduler_classes[0]
		__A  :		Optional[Any]       =			self.get_scheduler_config(**__lowercase						)
		__A  :		int       =			scheduler_class(**__lowercase						)

		__A  :		List[str]       =			10, 0.0

		__A  :		Tuple       =			self.dummy_model()
		__A  :		Any       =			self.dummy_sample_deter

		scheduler.set_timesteps(__lowercase						)

		for t in scheduler.timesteps:
			__A  :		Dict       =			model(__lowercase      ,      __lowercase						)
			__A  :		List[Any]       =			scheduler.step(__lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase						).prev_sample

		return sample


	def  snake_case__   (      self						):

		"""simple docstring"""







		for timesteps in [100, 500, 1_000]:
			self.check_over_configs(num_train_timesteps=__lowercase						)


	def  snake_case__   (      self						):

		"""simple docstring"""







		for steps_offset in [0, 1]:
			self.check_over_configs(steps_offset=__lowercase						)

		__A  :		str       =			self.scheduler_classes[0]
		__A  :		Optional[Any]       =			self.get_scheduler_config(steps_offset=1						)
		__A  :		List[str]       =			scheduler_class(**__lowercase						)
		scheduler.set_timesteps(5						)
		assert torch.equal(scheduler.timesteps      ,      torch.LongTensor([801, 601, 401, 201, 1]						)						)


	def  snake_case__   (      self						):

		"""simple docstring"""







		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=__lowercase      ,      beta_end=__lowercase						)


	def  snake_case__   (      self						):

		"""simple docstring"""







		for schedule in ["linear", "squaredcos_cap_v2"]:
			self.check_over_configs(beta_schedule=__lowercase						)


	def  snake_case__   (      self						):

		"""simple docstring"""







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


	def  snake_case__   (      self						):

		"""simple docstring"""







		for clip_sample in [True, False]:
			self.check_over_configs(clip_sample=__lowercase						)


	def  snake_case__   (      self						):

		"""simple docstring"""







		for timestep_spacing in ["trailing", "leading"]:
			self.check_over_configs(timestep_spacing=__lowercase						)


	def  snake_case__   (      self						):

		"""simple docstring"""







		for rescale_betas_zero_snr in [True, False]:
			self.check_over_configs(rescale_betas_zero_snr=__lowercase						)


	def  snake_case__   (      self						):

		"""simple docstring"""







		self.check_over_configs(thresholding=__lowercase						)
		for threshold in [0.5, 1.0, 2.0]:
			for prediction_type in ["epsilon", "v_prediction"]:
				self.check_over_configs(
				    thresholding=__lowercase      ,      prediction_type=__lowercase      ,      sample_max_value=__lowercase      ,      )


	def  snake_case__   (      self						):

		"""simple docstring"""







		for t in [1, 10, 49]:
			self.check_over_forward(time_step=__lowercase						)


	def  snake_case__   (      self						):

		"""simple docstring"""







		for t, num_inference_steps in zip([1, 10, 50]      ,      [10, 50, 500]						):
			self.check_over_forward(time_step=__lowercase      ,      num_inference_steps=__lowercase						)


	def  snake_case__   (      self						):

		"""simple docstring"""







		for t, eta in zip([1, 10, 49]      ,      [0.0, 0.5, 1.0]						):
			self.check_over_forward(time_step=__lowercase      ,      eta=__lowercase						)


	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		Optional[int]       =			self.scheduler_classes[0]
		__A  :		Tuple       =			self.get_scheduler_config()
		__A  :		Optional[int]       =			scheduler_class(**__lowercase						)

		assert torch.sum(torch.abs(scheduler._get_variance(0      ,      0						) - 0.0						)						) < 1E-5
		assert torch.sum(torch.abs(scheduler._get_variance(420      ,      400						) - 0.1_4_7_7_1						)						) < 1E-5
		assert torch.sum(torch.abs(scheduler._get_variance(980      ,      960						) - 0.3_2_4_6_0						)						) < 1E-5
		assert torch.sum(torch.abs(scheduler._get_variance(0      ,      0						) - 0.0						)						) < 1E-5
		assert torch.sum(torch.abs(scheduler._get_variance(487      ,      486						) - 0.0_0_9_7_9						)						) < 1E-5
		assert torch.sum(torch.abs(scheduler._get_variance(999      ,      998						) - 0.0_2						)						) < 1E-5


	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		Dict       =			self.scheduler_classes[0]
		__A  :		Tuple       =			self.get_scheduler_config()
		__A  :		Optional[int]       =			scheduler_class(**__lowercase						)

		__A  :		Dict       =			10, 0.0
		scheduler.set_timesteps(__lowercase						)

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

		__A  :		Union[str, Any]       =			samplea.shape[0]
		__A  :		Any       =			torch.stack([samplea, samplea, samplea]      ,      dim=0						)
		__A  :		Dict       =			torch.arange(__lowercase						)[0:3, None].repeat(1      ,      __lowercase						)

		__A  :		List[Any]       =			model(samples.flatten(0      ,      1						)      ,      timesteps.flatten(0      ,      1						)						)
		__A  :		str       =			scheduler.batch_step_no_noise(__lowercase      ,      timesteps.flatten(0      ,      1						)      ,      samples.flatten(0      ,      1						)      ,      __lowercase						)

		__A  :		Tuple       =			torch.sum(torch.abs(__lowercase						)						)
		__A  :		List[Any]       =			torch.mean(torch.abs(__lowercase						)						)

		assert abs(result_sum.item() - 1_147.7_904						) < 1E-2
		assert abs(result_mean.item() - 0.4_9_8_2						) < 1E-3


	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		Optional[int]       =			self.full_loop()

		__A  :		List[str]       =			torch.sum(torch.abs(__lowercase						)						)
		__A  :		int       =			torch.mean(torch.abs(__lowercase						)						)

		assert abs(result_sum.item() - 172.0_067						) < 1E-2
		assert abs(result_mean.item() - 0.2_2_3_9_6_7						) < 1E-3


	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		Any       =			self.full_loop(prediction_type='v_prediction'						)

		__A  :		List[str]       =			torch.sum(torch.abs(__lowercase						)						)
		__A  :		Union[str, Any]       =			torch.mean(torch.abs(__lowercase						)						)

		assert abs(result_sum.item() - 52.5_302						) < 1E-2
		assert abs(result_mean.item() - 0.0_6_8_4						) < 1E-3


	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		List[Any]       =			self.full_loop(set_alpha_to_one=__lowercase      ,      beta_start=0.0_1						)
		__A  :		Dict       =			torch.sum(torch.abs(__lowercase						)						)
		__A  :		str       =			torch.mean(torch.abs(__lowercase						)						)

		assert abs(result_sum.item() - 149.8_295						) < 1E-2
		assert abs(result_mean.item() - 0.1_9_5_1						) < 1E-3







	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		Dict       =			self.full_loop(set_alpha_to_one=__lowercase      ,      beta_start=0.0_1						)
		__A  :		Any       =			torch.sum(torch.abs(__lowercase						)						)
		__A  :		Optional[Any]       =			torch.mean(torch.abs(__lowercase						)						)

		assert abs(result_sum.item() - 149.0_784						) < 1E-2
		assert abs(result_mean.item() - 0.1_9_4_1						) < 1E-3










'''simple docstring'''
import unittest

from transformers import DonutProcessor


UpperCAmelCase_ :     str								=      'naver-clova-ix/donut-base'

class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			DonutProcessor.from_pretrained(__lowercase						)







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			{
      'name': 'John Doe',
      'age': '99',
      'city': 'Atlanta',
      'state': 'GA',
      'zip': '30301',
      'phone': '123-4567',
      'nicknames': [{'nickname': 'Johnny'}, {'nickname': 'JD'}],
  }

  __A  :		Optional[Any]       =			(
      '<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'
      '<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'
      '<s_nicknames><s_nickname>Johnny</s_nickname>'
      '<sep/><s_nickname>JD</s_nickname></s_nicknames>'
  )
  __A  :		List[Any]       =			self.processor.tokenajson(__lowercase						)

  self.assertDictEqual(__lowercase      ,      __lowercase						)






