




"""simple docstring"""

import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger

class 							lowercase   (     __UpperCAmelCase):
				__lowerCAmelCase     :     str								= """config.json"""







				@register_to_config
				def __init__(				self :       str   ,  _lowerCamelCase :       int=2   ,  _lowerCamelCase :       int=5   ,  _lowerCamelCase :       Tuple=(2, 5)   ,  _lowerCamelCase :       Tuple="for diffusion"   ,  _lowerCamelCase :       int=[1, 3]   ,  ):



					"""simple docstring"""

					pass

class 							lowercase   (     __UpperCAmelCase):
				__lowerCAmelCase     :     int								= """config.json"""







				@register_to_config
				def __init__(				self :       Union[str, Any]   ,  _lowerCamelCase :       Union[str, Any]=2   ,  _lowerCamelCase :       Optional[Any]=5   ,  _lowerCamelCase :       Any=(2, 5)   ,  _lowerCamelCase :       Any="for diffusion"   ,  _lowerCamelCase :       List[Any]=[1, 3]   ,  ):



					"""simple docstring"""

					pass

class 							lowercase   (     __UpperCAmelCase):
				__lowerCAmelCase     :     Tuple								= """config.json"""







				@register_to_config
				def __init__(				self :       List[str]   ,  _lowerCamelCase :       int=2   ,  _lowerCamelCase :       Optional[int]=5   ,  _lowerCamelCase :       Any=(2, 5)   ,  _lowerCamelCase :       List[Any]="for diffusion"   ,  _lowerCamelCase :       Optional[Any]=[1, 3]   ,  _lowerCamelCase :       Dict=[1, 3]   ,  ):



					"""simple docstring"""

					pass

class 							lowercase   (     __UpperCAmelCase):
				__lowerCAmelCase     :     Tuple								= """config.json"""







				@register_to_config
				def __init__(				self :       str   ,  _lowerCamelCase :       str=2   ,  _lowerCamelCase :       Union[str, Any]=5   ,  _lowerCamelCase :       List[Any]=(2, 5)   ,  _lowerCamelCase :       Optional[Any]="for diffusion"   ,  _lowerCamelCase :       List[str]=[1, 5]   ,  _lowerCamelCase :       List[str]=[5, 4]   ,  ):



					"""simple docstring"""

					pass

class 							lowercase   (     unittest.TestCase):







				def        a_	(				self :       List[str]							):



					"""simple docstring"""

					with self.assertRaises(_lowerCamelCase							):
						ConfigMixin.load_config('''dummy_path'''							)







				def        a_	(				self :       Any							):



					"""simple docstring"""

					A_	:					Tuple						 =		SampleObject()
					A_	:					List[str]						 =		obj.config
					assert config["a"] == 2
					assert config["b"] == 5
					assert config["c"] == (2, 5)
					assert config["d"] == "for diffusion"
					assert config["e"] == [1, 3]

					# init ignore private arguments
					A_	:					Any						 =		SampleObject(_name_or_path='''lalala'''							)
					A_	:					Optional[int]						 =		obj.config
					assert config["a"] == 2
					assert config["b"] == 5
					assert config["c"] == (2, 5)
					assert config["d"] == "for diffusion"
					assert config["e"] == [1, 3]

					# can override default
					A_	:					Optional[Any]						 =		SampleObject(c=6							)
					A_	:					Any						 =		obj.config
					assert config["a"] == 2
					assert config["b"] == 5
					assert config["c"] == 6
					assert config["d"] == "for diffusion"
					assert config["e"] == [1, 3]

					# can use positional arguments.
					A_	:					List[str]						 =		SampleObject(1   ,  c=6							)
					A_	:					Dict						 =		obj.config
					assert config["a"] == 1
					assert config["b"] == 5
					assert config["c"] == 6
					assert config["d"] == "for diffusion"
					assert config["e"] == [1, 3]







				def        a_	(				self :       Optional[int]							):



					"""simple docstring"""

					A_	:					Tuple						 =		SampleObject()
					A_	:					int						 =		obj.config

					assert config["a"] == 2
					assert config["b"] == 5
					assert config["c"] == (2, 5)
					assert config["d"] == "for diffusion"
					assert config["e"] == [1, 3]

					with tempfile.TemporaryDirectory() as tmpdirname:
						obj.save_config(_lowerCamelCase							)
						A_	:					Dict						 =		SampleObject.from_config(SampleObject.load_config(_lowerCamelCase							)							)
						A_	:					str						 =		new_obj.config

					# unfreeze configs
					A_	:					Tuple						 =		dict(_lowerCamelCase							)
					A_	:					Optional[Any]						 =		dict(_lowerCamelCase							)

					assert config.pop('''c'''							) == (2, 5)  # instantiated as tuple
					assert new_config.pop('''c'''							) == [2, 5]  # saved & loaded as list because of json
					config.pop('''_use_default_values'''							)
					assert config == new_config







				def        a_	(				self :       int							):



					"""simple docstring"""

					A_	:					Optional[Any]						 =		logging.get_logger('''diffusers.configuration_utils'''							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(_lowerCamelCase							) as cap_logger:
						A_	:					str						 =		DDIMScheduler.from_pretrained(
						    '''hf-internal-testing/tiny-stable-diffusion-torch'''   ,  subfolder='''scheduler'''							)

					assert ddim.__class__ == DDIMScheduler
					# no warning should be thrown
					assert cap_logger.out == ""







				def        a_	(				self :       List[Any]							):



					"""simple docstring"""

					A_	:					Optional[int]						 =		logging.get_logger('''diffusers.configuration_utils'''							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(_lowerCamelCase							) as cap_logger:
						A_	:					Optional[int]						 =		EulerDiscreteScheduler.from_pretrained(
						    '''hf-internal-testing/tiny-stable-diffusion-torch'''   ,  subfolder='''scheduler'''							)

					assert euler.__class__ == EulerDiscreteScheduler
					# no warning should be thrown
					assert cap_logger.out == ""







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



					"""simple docstring"""

					A_	:					List[Any]						 =		logging.get_logger('''diffusers.configuration_utils'''							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(_lowerCamelCase							) as cap_logger:
						A_	:					Any						 =		EulerAncestralDiscreteScheduler.from_pretrained(
						    '''hf-internal-testing/tiny-stable-diffusion-torch'''   ,  subfolder='''scheduler'''							)

					assert euler.__class__ == EulerAncestralDiscreteScheduler
					# no warning should be thrown
					assert cap_logger.out == ""







				def        a_	(				self :       Any							):



					"""simple docstring"""

					A_	:					List[Any]						 =		logging.get_logger('''diffusers.configuration_utils'''							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(_lowerCamelCase							) as cap_logger:
						A_	:					Union[str, Any]						 =		PNDMScheduler.from_pretrained(
						    '''hf-internal-testing/tiny-stable-diffusion-torch'''   ,  subfolder='''scheduler'''							)

					assert pndm.__class__ == PNDMScheduler
					# no warning should be thrown
					assert cap_logger.out == ""







				def        a_	(				self :       List[Any]							):



					"""simple docstring"""

					A_	:					List[str]						 =		logging.get_logger('''diffusers.configuration_utils'''							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(_lowerCamelCase							) as cap_logger:
						A_	:					Dict						 =		DDPMScheduler.from_pretrained(
						    '''hf-internal-testing/tiny-stable-diffusion-torch'''   ,  subfolder='''scheduler'''   ,  prediction_type='''sample'''   ,  beta_end=8   ,  )

					with CaptureLogger(_lowerCamelCase							) as cap_logger_a:
						A_	:					str						 =		DDPMScheduler.from_pretrained('''google/ddpm-celebahq-256'''   ,  beta_start=88							)

					assert ddpm.__class__ == DDPMScheduler
					assert ddpm.config.prediction_type == "sample"
					assert ddpm.config.beta_end == 8
					assert ddpm_a.config.beta_start == 88

					# no warning should be thrown
					assert cap_logger.out == ""
					assert cap_logger_a.out == ""







				def        a_	(				self :       List[str]							):



					"""simple docstring"""

					A_	:					str						 =		logging.get_logger('''diffusers.configuration_utils'''							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(_lowerCamelCase							) as cap_logger:
						A_	:					str						 =		DPMSolverMultistepScheduler.from_pretrained(
						    '''hf-internal-testing/tiny-stable-diffusion-torch'''   ,  subfolder='''scheduler'''							)

					assert dpm.__class__ == DPMSolverMultistepScheduler
					# no warning should be thrown
					assert cap_logger.out == ""







				def        a_	(				self :       List[Any]							):



					"""simple docstring"""

					A_	:					Optional[int]						 =		SampleObject()

					A_	:					Dict						 =		{k: v for k, v in config.config.items() if not k.startswith('''_'''							)}

					# make sure that default config has all keys in `_use_default_values`
					assert set(config_dict.keys()							) == set(config.config._use_default_values							)

					with tempfile.TemporaryDirectory() as tmpdirname:
						config.save_config(_lowerCamelCase							)

						# now loading it with SampleObject2 should put f into `_use_default_values`
						A_	:					str						 =		SampleObjecta.from_config(_lowerCamelCase							)

						assert "f" in config._use_default_values
						assert config.f == [1, 3]

					# now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
					# **BECAUSE** it is part of `config._use_default_values`
					A_	:					Tuple						 =		SampleObjecta.from_config(config.config							)
					assert new_config.f == [5, 4]

					config.config._use_default_values.pop()
					A_	:					str						 =		SampleObjecta.from_config(config.config							)
					assert new_config_a.f == [1, 3]

					# Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
					assert new_config_a.e == [1, 3]











"""simple docstring"""

# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint

class 							lowercase   :







				def __init__(				self :       Optional[int]   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       Optional[Any]							):



					"""simple docstring"""

					A_	:					Union[str, Any]						 =		key
					A_	:					str						 =		freq







				def __str__(				self :       Optional[Any]							):



					"""simple docstring"""

					return F"""Node(key={self.key}, freq={self.freq})"""

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	if i > j or i < 0 or j > len(_UpperCAmelCase						) - 1:
		return

	A_	:					Union[str, Any]						 =		root[i][j]
	if parent == -1:  # root does not have a parent
		print(f"""{key[node]} is the root of the binary search tree."""						)
	elif is_left:
		print(f"""{key[node]} is the left child of key {parent}."""						)
	else:
		print(f"""{key[node]} is the right child of key {parent}."""						)

	print_binary_search_tree(_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   node - 1	,   key[node]	,   _UpperCAmelCase						)
	print_binary_search_tree(_UpperCAmelCase	,   _UpperCAmelCase	,   node + 1	,   _UpperCAmelCase	,   key[node]	,   _UpperCAmelCase						)

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	nodes.sort(key=lambda _UpperCAmelCase						: node.key						)

	A_	:					Any						 =		len(_UpperCAmelCase						)

	A_	:					int						 =		[nodes[i].key for i in range(_UpperCAmelCase						)]
	A_	:					List[str]						 =		[nodes[i].freq for i in range(_UpperCAmelCase						)]

	# This 2D array stores the overall tree cost (which's as minimized as possible);
	# for a single key, cost is equal to frequency of the key.
	A_	:					List[str]						 =		[[freqs[i] if i == j else 0 for j in range(_UpperCAmelCase						)] for i in range(_UpperCAmelCase						)]
	# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
	# array
	A_	:					Optional[int]						 =		[[freqs[i] if i == j else 0 for j in range(_UpperCAmelCase						)] for i in range(_UpperCAmelCase						)]
	# stores tree roots that will be used later for constructing binary search tree
	A_	:					List[Any]						 =		[[i if i == j else 0 for j in range(_UpperCAmelCase						)] for i in range(_UpperCAmelCase						)]

	for interval_length in range(2	,   n + 1						):
		for i in range(n - interval_length + 1						):
			A_	:					List[Any]						 =		i + interval_length - 1

			A_	:					List[Any]						 =		sys.maxsize  # set the value to "infinity"
			A_	:					Union[str, Any]						 =		total[i][j - 1] + freqs[j]

			# Apply Knuth's optimization
			# Loop without optimization: for r in range(i, j + 1):
			for r in range(root[i][j - 1]	,   root[i + 1][j] + 1						):  # r is a temporal root
				A_	:					Tuple						 =		dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
				A_	:					List[str]						 =		dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
				A_	:					Optional[int]						 =		left + total[i][j] + right

				if dp[i][j] > cost:
					A_	:					Dict						 =		cost
					A_	:					List[str]						 =		r

	print('''Binary search tree nodes:'''						)
	for node in nodes:
		print(_UpperCAmelCase						)

	print(f"""\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."""						)
	print_binary_search_tree(_UpperCAmelCase	,   _UpperCAmelCase	,   0	,   n - 1	,   -1	,   _UpperCAmelCase						)

def 		lowercase_   (		):




	"""simple docstring"""






	A_	:					List[Any]						 =		[Node(_UpperCAmelCase	,   randint(1	,   50						)						) for i in range(10	,   0	,   -1						)]
	find_optimal_binary_search_tree(_UpperCAmelCase						)


if __name__ == "__main__":
		main()





