from collections.abc import Sequence
from queue import Queue





class 		_lowercase						:

 def __init__( self	:							int   ,      snake_case	:							Tuple   ,      snake_case	:							int   ,      snake_case	:							int   ,      snake_case	:							Any=None   ,      snake_case	:							Optional[Any]=None			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					start
  UpperCamelCase_					:						Optional[int] =					end
  UpperCamelCase_					:						List[Any] =					val
  UpperCamelCase_					:						Dict =					(start + end) // 2
  UpperCamelCase_					:						List[Any] =					left
  UpperCamelCase_					:						List[str] =					right


 def __repr__( self	:							Any			)   ->						Union[str, Any]:

  """simple docstring"""





  return f"SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"





class 		_lowercase						:

 def __init__( self	:							List[str]   ,      snake_case	:							Sequence   ,      snake_case	:							Dict			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					collection
  UpperCamelCase_					:						List[str] =					function
  if self.collection:
   UpperCamelCase_					:						List[str] =					self._build_tree(0   ,      len(snake_case			) - 1			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str   ,      snake_case	:							Tuple   ,      snake_case	:							Tuple			)   ->						str:

  """simple docstring"""





  self._update_tree(self.root   ,      snake_case   ,      snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str   ,      snake_case	:							List[str]   ,      snake_case	:							List[str]			)   ->						Union[str, Any]:

  """simple docstring"""





  return self._query_range(self.root   ,      snake_case   ,      snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							int   ,      snake_case	:							Union[str, Any]			)   ->						int:

  """simple docstring"""





  if start == end:
   return SegmentTreeNode(snake_case   ,      snake_case   ,      self.collection[start]			)
  UpperCamelCase_					:						Tuple =					(start + end) // 2
  UpperCamelCase_					:						Union[str, Any] =					self._build_tree(snake_case   ,      snake_case			)
  UpperCamelCase_					:						List[str] =					self._build_tree(mid + 1   ,      snake_case			)
  return SegmentTreeNode(snake_case   ,      snake_case   ,      self.fn(left.val   ,      right.val			)   ,      snake_case   ,      snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]   ,      snake_case	:							Tuple   ,      snake_case	:							Dict   ,      snake_case	:							str			)   ->						str:

  """simple docstring"""





  if node.start == i and node.end == i:
   UpperCamelCase_					:						Any =					val
   return
  if i <= node.mid:
   self._update_tree(node.left   ,      snake_case   ,      snake_case			)
  else:
   self._update_tree(node.right   ,      snake_case   ,      snake_case			)
  UpperCamelCase_					:						Any =					self.fn(node.left.val   ,      node.right.val			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]   ,      snake_case	:							Any   ,      snake_case	:							Tuple   ,      snake_case	:							List[Any]			)   ->						int:

  """simple docstring"""





  if node.start == i and node.end == j:
   return node.val

  if i <= node.mid:
   if j <= node.mid:
    # range in left child tree
    return self._query_range(node.left   ,      snake_case   ,      snake_case			)
   else:
    # range in left child tree and right child tree
    return self.fn(
        self._query_range(node.left   ,      snake_case   ,      node.mid			)   ,      self._query_range(node.right   ,      node.mid + 1   ,      snake_case			)   ,      )
  else:
   # range in right child tree
   return self._query_range(node.right   ,      snake_case   ,      snake_case			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						Any:

  """simple docstring"""





  if self.root is not None:
   UpperCamelCase_					:						int =					Queue()
   queue.put(self.root			)
   while not queue.empty():
    UpperCamelCase_					:						str =					queue.get()
    yield node

    if node.left is not None:
     queue.put(node.left			)

    if node.right is not None:
     queue.put(node.right			)


if __name__ == "__main__":
   import operator

   for fn in [operator.add, max, min]:
      print('*' * 50)
      a_    					=       SegmentTree([2, 1, 5, 3, 4], fn)
      for node in arr.traverse():
         print(node)
      print()

      arr.update(1, 5)
      for node in arr.traverse():
         print(node)
      print()

      print(arr.query_range(3, 4))  # 7
      print(arr.query_range(2, 2))  # 5
      print(arr.query_range(1, 3))  # 13
      print()




import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
   import jax
   import jax.numpy as jnp
   from flax.jax_utils import replicate
   from flax.training.common_utils import shard





@slow
@require_flax
class 		_lowercase						(					unittest.TestCase       ):

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						str:

  """simple docstring"""





  super().tearDown()
  gc.collect()

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_					:						str =					FlaxStableDiffusionPipeline.from_pretrained(
      'stabilityai/stable-diffusion-2'   ,      revision='bf16'   ,      dtype=jnp.bfloataa   ,      )

  UpperCamelCase_					:						str =					'A painting of a squirrel eating a burger'
  UpperCamelCase_					:						Any =					jax.device_count()
  UpperCamelCase_					:						List[str] =					num_samples * [prompt]
  UpperCamelCase_					:						List[Any] =					sd_pipe.prepare_inputs(snake_case			)

  UpperCamelCase_					:						Dict =					replicate(snake_case			)
  UpperCamelCase_					:						Optional[Any] =					shard(snake_case			)

  UpperCamelCase_					:						Dict =					jax.random.PRNGKey(0			)
  UpperCamelCase_					:						Tuple =					jax.random.split(snake_case   ,      jax.device_count()			)

  UpperCamelCase_					:						Optional[Any] =					sd_pipe(snake_case   ,      snake_case   ,      snake_case   ,      num_inference_steps=2_5   ,      jit=snake_case			)[0]
  assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

  UpperCamelCase_					:						int =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
  UpperCamelCase_					:						Tuple =					images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

  UpperCamelCase_					:						str =					jnp.asarray(jax.device_get(image_slice.flatten()			)			)
  UpperCamelCase_					:						List[str] =					jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]			)
  print(f"output_slice: {output_slice}"			)
  assert jnp.abs(output_slice - expected_slice			).max() < 1e-2


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'stabilityai/stable-diffusion-2'
  UpperCamelCase_, UpperCamelCase_					:						Tuple =					FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case   ,      subfolder='scheduler'			)
  UpperCamelCase_, UpperCamelCase_					:						Union[str, Any] =					FlaxStableDiffusionPipeline.from_pretrained(
      snake_case   ,      scheduler=snake_case   ,      revision='bf16'   ,      dtype=jnp.bfloataa   ,      )
  UpperCamelCase_					:						Optional[int] =					scheduler_params

  UpperCamelCase_					:						Optional[Any] =					'A painting of a squirrel eating a burger'
  UpperCamelCase_					:						Union[str, Any] =					jax.device_count()
  UpperCamelCase_					:						Union[str, Any] =					num_samples * [prompt]
  UpperCamelCase_					:						Tuple =					sd_pipe.prepare_inputs(snake_case			)

  UpperCamelCase_					:						List[Any] =					replicate(snake_case			)
  UpperCamelCase_					:						Optional[Any] =					shard(snake_case			)

  UpperCamelCase_					:						Tuple =					jax.random.PRNGKey(0			)
  UpperCamelCase_					:						str =					jax.random.split(snake_case   ,      jax.device_count()			)

  UpperCamelCase_					:						str =					sd_pipe(snake_case   ,      snake_case   ,      snake_case   ,      num_inference_steps=2_5   ,      jit=snake_case			)[0]
  assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

  UpperCamelCase_					:						str =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
  UpperCamelCase_					:						int =					images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

  UpperCamelCase_					:						int =					jnp.asarray(jax.device_get(image_slice.flatten()			)			)
  UpperCamelCase_					:						Union[str, Any] =					jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]			)
  print(f"output_slice: {output_slice}"			)
  assert jnp.abs(output_slice - expected_slice			).max() < 1e-2



