


"""simple docstring"""
import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch



@require_torch
class 			_UpperCAmelCase :






		'''simple docstring'''

		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return self.get_dummy_input()



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			if self.block_type == "down":
				return (4, 32, 16, 16)
			elif self.block_type == "mid":
				return (4, 32, 32, 32)
			elif self.block_type == "up":
				return (4, 32, 64, 64)

			raise ValueError(f"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_=True				,       a_=False				,       a_=False				,       a_=False				,       ):





			'''simple docstring'''

			__snake_case	:						str						=				4
			__snake_case	:						List[Any]						=				32
			__snake_case	:						Union[str, Any]						=				(32, 32)

			__snake_case	:						Dict						=				torch.manual_seed(0					)
			__snake_case	:						int						=				torch.device(a_					)
			__snake_case	:						str						=				(batch_size, num_channels) + sizes
			__snake_case	:						List[Any]						=				randn_tensor(a_				,       generator=a_				,       device=a_					)
			__snake_case	:						List[str]						=				{'''hidden_states''': hidden_states}

			if include_temb:
				__snake_case	:						Any						=				1_28
				__snake_case	:						Optional[int]						=				randn_tensor((batch_size, temb_channels)				,       generator=a_				,       device=a_					)

			if include_res_hidden_states_tuple:
				__snake_case	:						Any						=				torch.manual_seed(1					)
				__snake_case	:						int						=				(randn_tensor(a_				,       generator=a_				,       device=a_					),)

			if include_encoder_hidden_states:
				__snake_case	:						Optional[Any]						=				floats_tensor((batch_size, 32, 32)					).to(a_					)

			if include_skip_sample:
				__snake_case	:						Optional[Any]						=				randn_tensor(((batch_size, 3) + sizes)				,       generator=a_				,       device=a_					)

			return dummy_input



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				{
			    '''in_channels''': 32,
			    '''out_channels''': 32,
			    '''temb_channels''': 1_28,
			}
			if self.block_type == "up":
				__snake_case	:						Optional[int]						=				32

			if self.block_type == "mid":
				init_dict.pop('''out_channels'''					)

			__snake_case	:						int						=				self.dummy_input
			return init_dict, inputs_dict



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			__snake_case	,							__snake_case	:						List[Any]						=				self.prepare_init_args_and_inputs_for_common()
			__snake_case	:						List[Any]						=				self.block_class(**a_					)
			unet_block.to(a_					)
			unet_block.eval()

			with torch.no_grad():
				__snake_case	:						Union[str, Any]						=				unet_block(**a_					)

			if isinstance(a_				,       a_					):
				__snake_case	:						Tuple						=				output[0]

			self.assertEqual(output.shape				,       self.output_shape					)

			__snake_case	:						Dict						=				output[0, -1, -3:, -3:]
			__snake_case	:						int						=				torch.tensor(a_					).to(a_					)
			assert torch_all_close(output_slice.flatten()				,       a_				,       atol=5E-3					)



		@unittest.skipIf(torch_device == '''mps'''				,       '''Training is not supported in mps'''					)
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	,							__snake_case	:						str						=				self.prepare_init_args_and_inputs_for_common()
			__snake_case	:						Optional[int]						=				self.block_class(**a_					)
			model.to(a_					)
			model.train()
			__snake_case	:						int						=				model(**a_					)

			if isinstance(a_				,       a_					):
				__snake_case	:						List[Any]						=				output[0]

			__snake_case	:						Optional[Any]						=				torch.device(a_					)
			__snake_case	:						Tuple						=				randn_tensor(output.shape				,       device=a_					)
			__snake_case	:						str						=				torch.nn.functional.mse_loss(a_				,       a_					)
			loss.backward()









"""simple docstring"""







from __future__ import annotations

import math


class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       def __init__(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							size
         # approximate the overall size of segment tree with given value
         lowerCAmelCase     :     Optional[int]								=							[0 for i in range(0		,     4 * size						)]
         # create array to store lazy update
         lowerCAmelCase     :     List[str]								=							[0 for i in range(0		,     4 * size						)]
         lowerCAmelCase     :     Dict								=							[0 for i in range(0		,     4 * size						)]  # flag for lazy update

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         return idx * 2

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         return idx * 2 + 1

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         if left_element == right_element:
           lowerCAmelCase     :     List[str]								=							a[left_element - 1]
         else:
           lowerCAmelCase     :     Tuple								=							(left_element + right_element) // 2
           self.build(self.left(snake_case__						)		,     snake_case__		,     snake_case__		,     snake_case__						)
           self.build(self.right(snake_case__						)		,     mid + 1		,     snake_case__		,     snake_case__						)
           lowerCAmelCase     :     Tuple								=							max(
               self.segment_tree[self.left(snake_case__						)]		,     self.segment_tree[self.right(snake_case__						)]						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         if self.flag[idx] is True:
           lowerCAmelCase     :     Optional[int]								=							self.lazy[idx]
           lowerCAmelCase     :     List[str]								=							False
           if left_element != right_element:
             lowerCAmelCase     :     Optional[Any]								=							self.lazy[idx]
             lowerCAmelCase     :     List[Any]								=							self.lazy[idx]
             lowerCAmelCase     :     List[Any]								=							True
             lowerCAmelCase     :     Optional[Any]								=							True

         if right_element < a or left_element > b:
           return True
         if left_element >= a and right_element <= b:
           lowerCAmelCase     :     str								=							val
           if left_element != right_element:
             lowerCAmelCase     :     Optional[Any]								=							val
             lowerCAmelCase     :     Union[str, Any]								=							val
             lowerCAmelCase     :     int								=							True
             lowerCAmelCase     :     int								=							True
           return True
         lowerCAmelCase     :     List[str]								=							(left_element + right_element) // 2
         self.update(self.left(snake_case__						)		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						)
         self.update(self.right(snake_case__						)		,     mid + 1		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						)
         lowerCAmelCase     :     Optional[int]								=							max(
             self.segment_tree[self.left(snake_case__						)]		,     self.segment_tree[self.right(snake_case__						)]						)
         return True

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         if self.flag[idx] is True:
           lowerCAmelCase     :     List[Any]								=							self.lazy[idx]
           lowerCAmelCase     :     str								=							False
           if left_element != right_element:
             lowerCAmelCase     :     Tuple								=							self.lazy[idx]
             lowerCAmelCase     :     List[Any]								=							self.lazy[idx]
             lowerCAmelCase     :     Optional[int]								=							True
             lowerCAmelCase     :     str								=							True
         if right_element < a or left_element > b:
           return -math.inf
         if left_element >= a and right_element <= b:
           return self.segment_tree[idx]
         lowerCAmelCase     :     Any								=							(left_element + right_element) // 2
         lowerCAmelCase     :     Optional[int]								=							self.query(self.left(snake_case__						)		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						)
         lowerCAmelCase     :     Dict								=							self.query(self.right(snake_case__						)		,     mid + 1		,     snake_case__		,     snake_case__		,     snake_case__						)
         return max(snake_case__		,     snake_case__						)







       def __str__(						self						):






         """simple docstring"""





         return str([self.query(1		,     1		,     self.size		,     snake_case__		,     snake_case__						) for i in range(1		,     self.size + 1						)]						)


if __name__ == "__main__":
     lowerCAmelCase__      				=   [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
     lowerCAmelCase__      				=   15
     lowerCAmelCase__      				=   SegmentTree(size)
     segt.build(1, 1, size, A)
     print(segt.query(1, 1, size, 4, 6))
     print(segt.query(1, 1, size, 7, 11))
     print(segt.query(1, 1, size, 7, 12))
     segt.update(1, 1, size, 1, 3, 111)
     print(segt.query(1, 1, size, 1, 15))
     segt.update(1, 1, size, 7, 8, 235)
     print(segt)





