

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   str     					=				(PNDMScheduler,)
	_lowercase		:   List[Any]     					=				(('''num_inference_steps''', 50),)




	def        lowerCAmelCase_       (     self						:     str					,      **__A						:     Optional[int]							):
		__A :			List[str]		    =			{
		    """num_train_timesteps""": 1000,
		    """beta_start""": 0.0_0_0_1,
		    """beta_end""": 0.0_2,
		    """beta_schedule""": """linear""",
		}

		config.update(**__A							)
		return config




	def        lowerCAmelCase_       (     self						:     List[str]					,      __A						:     str=0					,      **__A						:     List[str]							):
		__A :			Tuple		    =			dict(self.forward_default_kwargs							)
		__A :			str		    =			kwargs.pop("""num_inference_steps"""					,      __A							)
		__A :			str		    =			self.dummy_sample
		__A :			Any		    =			0.1 * sample
		__A :			List[str]		    =			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

		for scheduler_class in self.scheduler_classes:
			__A :			Dict		    =			self.get_scheduler_config(**__A							)
			__A :			Optional[Any]		    =			scheduler_class(**__A							)
			scheduler.set_timesteps(__A							)
			# copy over dummy past residuals
			__A :			Union[str, Any]		    =			dummy_past_residuals[:]

			with tempfile.TemporaryDirectory() as tmpdirname:
				scheduler.save_config(__A							)
				__A :			str		    =			scheduler_class.from_pretrained(__A							)
				new_scheduler.set_timesteps(__A							)
				# copy over dummy past residuals
				__A :			Any		    =			dummy_past_residuals[:]

			__A :			List[str]		    =			scheduler.step_prk(__A					,      __A					,      __A					,      **__A							).prev_sample
			__A :			int		    =			new_scheduler.step_prk(__A					,      __A					,      __A					,      **__A							).prev_sample

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

			__A :			int		    =			scheduler.step_plms(__A					,      __A					,      __A					,      **__A							).prev_sample
			__A :			List[str]		    =			new_scheduler.step_plms(__A					,      __A					,      __A					,      **__A							).prev_sample

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




	def        lowerCAmelCase_       (     self						:     str							):
		pass




	def        lowerCAmelCase_       (     self						:     Optional[int]					,      __A						:     Dict=0					,      **__A						:     str							):
		__A :			List[str]		    =			dict(self.forward_default_kwargs							)
		__A :			Dict		    =			kwargs.pop("""num_inference_steps"""					,      __A							)
		__A :			int		    =			self.dummy_sample
		__A :			Tuple		    =			0.1 * sample
		__A :			Optional[int]		    =			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

		for scheduler_class in self.scheduler_classes:
			__A :			Any		    =			self.get_scheduler_config()
			__A :			Union[str, Any]		    =			scheduler_class(**__A							)
			scheduler.set_timesteps(__A							)

			# copy over dummy past residuals (must be after setting timesteps)
			__A :			Dict		    =			dummy_past_residuals[:]

			with tempfile.TemporaryDirectory() as tmpdirname:
				scheduler.save_config(__A							)
				__A :			List[str]		    =			scheduler_class.from_pretrained(__A							)
				# copy over dummy past residuals
				new_scheduler.set_timesteps(__A							)

				# copy over dummy past residual (must be after setting timesteps)
				__A :			List[Any]		    =			dummy_past_residuals[:]

			__A :			List[str]		    =			scheduler.step_prk(__A					,      __A					,      __A					,      **__A							).prev_sample
			__A :			Any		    =			new_scheduler.step_prk(__A					,      __A					,      __A					,      **__A							).prev_sample

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

			__A :			Tuple		    =			scheduler.step_plms(__A					,      __A					,      __A					,      **__A							).prev_sample
			__A :			Tuple		    =			new_scheduler.step_plms(__A					,      __A					,      __A					,      **__A							).prev_sample

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




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

		__A :			Optional[int]		    =			10
		__A :			Optional[Any]		    =			self.dummy_model()
		__A :			Optional[Any]		    =			self.dummy_sample_deter
		scheduler.set_timesteps(__A							)

		for i, t in enumerate(scheduler.prk_timesteps							):
			__A :			Tuple		    =			model(__A					,      __A							)
			__A :			List[str]		    =			scheduler.step_prk(__A					,      __A					,      __A							).prev_sample

		for i, t in enumerate(scheduler.plms_timesteps							):
			__A :			Optional[Any]		    =			model(__A					,      __A							)
			__A :			Tuple		    =			scheduler.step_plms(__A					,      __A					,      __A							).prev_sample

		return sample




	def        lowerCAmelCase_       (     self						:     str							):
		__A :			Tuple		    =			dict(self.forward_default_kwargs							)

		__A :			Optional[int]		    =			kwargs.pop("""num_inference_steps"""					,      __A							)

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

			__A :			Any		    =			self.dummy_sample
			__A :			str		    =			0.1 * sample

			if num_inference_steps is not None and hasattr(__A					,      """set_timesteps"""							):
				scheduler.set_timesteps(__A							)
			elif num_inference_steps is not None and not hasattr(__A					,      """set_timesteps"""							):
				__A :			Union[str, Any]		    =			num_inference_steps

			# copy over dummy past residuals (must be done after set_timesteps)
			__A :			Optional[int]		    =			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
			__A :			Any		    =			dummy_past_residuals[:]

			__A :			Any		    =			scheduler.step_prk(__A					,      0					,      __A					,      **__A							).prev_sample
			__A :			Tuple		    =			scheduler.step_prk(__A					,      1					,      __A					,      **__A							).prev_sample

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

			__A :			Dict		    =			scheduler.step_plms(__A					,      0					,      __A					,      **__A							).prev_sample
			__A :			List[Any]		    =			scheduler.step_plms(__A					,      1					,      __A					,      **__A							).prev_sample

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




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		for timesteps in [100, 1000]:
			self.check_over_configs(num_train_timesteps=__A							)




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		for steps_offset in [0, 1]:
			self.check_over_configs(steps_offset=__A							)

		__A :			int		    =			self.scheduler_classes[0]
		__A :			Any		    =			self.get_scheduler_config(steps_offset=1							)
		__A :			List[str]		    =			scheduler_class(**__A							)
		scheduler.set_timesteps(10							)
		assert torch.equal(
		    scheduler.timesteps					,      torch.LongTensor(
		        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]							)					,      )




	def        lowerCAmelCase_       (     self						:     Any							):
		for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]					,      [0.0_0_2, 0.0_2]							):
			self.check_over_configs(beta_start=__A					,      beta_end=__A							)




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




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




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		for t in [1, 5, 10]:
			self.check_over_forward(time_step=__A							)




	def        lowerCAmelCase_       (     self						:     Any							):
		for t, num_inference_steps in zip([1, 5, 10]					,      [10, 50, 100]							):
			self.check_over_forward(num_inference_steps=__A							)




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
		__A :			Dict		    =			27

		for scheduler_class in self.scheduler_classes:
			__A :			Tuple		    =			self.dummy_sample
			__A :			List[str]		    =			0.1 * sample

			__A :			List[Any]		    =			self.get_scheduler_config()
			__A :			int		    =			scheduler_class(**__A							)

			scheduler.set_timesteps(__A							)

			# before power of 3 fix, would error on first step, so we only need to do two
			for i, t in enumerate(scheduler.prk_timesteps[:2]							):
				__A :			Any		    =			scheduler.step_prk(__A					,      __A					,      __A							).prev_sample




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

			scheduler.step_plms(self.dummy_sample					,      1					,      self.dummy_sample							).prev_sample




	def        lowerCAmelCase_       (     self						:     Dict							):
		__A :			Dict		    =			self.full_loop()
		__A :			int		    =			torch.sum(torch.abs(__A							)							)
		__A :			str		    =			torch.mean(torch.abs(__A							)							)

		assert abs(result_sum.item() - 1_9_8.1_3_1_8							) < 1e-2
		assert abs(result_mean.item() - 0.2_5_8_0							) < 1e-3




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		__A :			Union[str, Any]		    =			self.full_loop(prediction_type="""v_prediction"""							)
		__A :			Tuple		    =			torch.sum(torch.abs(__A							)							)
		__A :			List[Any]		    =			torch.mean(torch.abs(__A							)							)

		assert abs(result_sum.item() - 6_7.3_9_8_6							) < 1e-2
		assert abs(result_mean.item() - 0.0_8_7_8							) < 1e-3




	def        lowerCAmelCase_       (     self						:     str							):
		# We specify different beta, so that the first alpha is 0.99
		__A :			Optional[Any]		    =			self.full_loop(set_alpha_to_one=__A					,      beta_start=0.0_1							)
		__A :			List[Any]		    =			torch.sum(torch.abs(__A							)							)
		__A :			Optional[int]		    =			torch.mean(torch.abs(__A							)							)

		assert abs(result_sum.item() - 2_3_0.0_3_9_9							) < 1e-2
		assert abs(result_mean.item() - 0.2_9_9_5							) < 1e-3




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		# We specify different beta, so that the first alpha is 0.99
		__A :			Optional[int]		    =			self.full_loop(set_alpha_to_one=__A					,      beta_start=0.0_1							)
		__A :			Optional[int]		    =			torch.sum(torch.abs(__A							)							)
		__A :			List[Any]		    =			torch.mean(torch.abs(__A							)							)

		assert abs(result_sum.item() - 1_8_6.9_4_8_2							) < 1e-2
		assert abs(result_mean.item() - 0.2_4_3_4							) < 1e-3












def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:

		def count_of_possible_combinations(snake_case							) -> int:
				if target < 0:
						return 0
				if target == 0:
						return 1
				return sum(count_of_possible_combinations(target - item							) for item in array							)

		return count_of_possible_combinations(snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:

		def count_of_possible_combinations_with_dp_array(
		    snake_case						,   snake_case							) -> int:
				if target < 0:
						return 0
				if target == 0:
						return 1
				if dp_array[target] != -1:
						return dp_array[target]
				__lowercase											=     sum(
				    count_of_possible_combinations_with_dp_array(target - item						,   snake_case							)
				    for item in array							)
				__lowercase											=     answer
				return answer

		__lowercase											=     [-1] * (target + 1)
		return count_of_possible_combinations_with_dp_array(snake_case						,   snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:
		__lowercase											=     [0] * (target + 1)
		__lowercase											=     1

		for i in range(1						,   target + 1							):
				for j in range(snake_case							):
						if i - array[j] >= 0:
								dp_array[i] += dp_array[i - array[j]]

		return dp_array[target]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		SCREAMING_SNAKE_CASE_   :      Tuple			=   3
		SCREAMING_SNAKE_CASE_   :      Dict			=   5
		SCREAMING_SNAKE_CASE_   :      Any			=   [1, 2, 5]
		print(combination_sum_iv(n, array, target))




