





"""simple docstring"""




from collections.abc import Sequence
from queue import Queue

class  _UpperCAmelCase  :



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_=None					,      snake_case_=None  ):

        """simple docstring"""

        A_  :       Dict										=  start
        A_  :       Optional[Any]										=  end
        A_  :       Any										=  val
        A_  :       Union[str, Any]										=  (start + end) // 2
        A_  :       List[str]										=  left
        A_  :       Any										=  right

      def __repr__(     self  ):

        """simple docstring"""

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


class  _UpperCAmelCase  :



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       Tuple										=  collection
        A_  :       Any										=  function
        if self.collection:
          A_  :       Dict										=  self._build_tree(0					,      len(snake_case_  ) - 1  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        self._update_tree(self.root					,      snake_case_					,      snake_case_  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

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

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        if start == end:
          return SegmentTreeNode(snake_case_					,      snake_case_					,      self.collection[start]  )
        A_  :       int										=  (start + end) // 2
        A_  :       Dict										=  self._build_tree(snake_case_					,      snake_case_  )
        A_  :       Any										=  self._build_tree(mid + 1					,      snake_case_  )
        return SegmentTreeNode(snake_case_					,      snake_case_					,      self.fn(left.val					,      right.val  )					,      snake_case_					,      snake_case_  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        if node.start == i and node.end == i:
          A_  :       List[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_  )
        A_  :       Optional[Any]										=  self.fn(node.left.val					,      node.right.val  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """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 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        if self.root is not None:
          A_  :       Optional[int]										=  Queue()
          queue.put(self.root  )
          while not queue.empty():
            A_  :       Tuple										=  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)
      lowerCamelCase_     :						Dict						=					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()






"""simple docstring"""




import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCamelCase_     :						Tuple						=					logging.get_logger(__name__)





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Optional[Any]										=  R'\w+[.]\d+'
  A_  :       int										=  re.findall(_UpperCAmelCase ,					_UpperCAmelCase					)
  for pat in pats:
    A_  :       Optional[int]										=  key.replace(_UpperCAmelCase ,					'_'.join(pat.split('.'					)					)					)
  return key





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[Any]										=  pt_tuple_key[:-1] + ('scale',)
  if (
      any('norm' in str_ for str_ in pt_tuple_key					)
      and (pt_tuple_key[-1] == "bias")
      and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
      and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
  ):
    A_  :       Union[str, Any]										=  pt_tuple_key[:-1] + ('scale',)
    return renamed_pt_tuple_key, pt_tensor
  elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
    A_  :       List[str]										=  pt_tuple_key[:-1] + ('scale',)
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
    A_  :       Optional[Any]										=  pt_tuple_key[:-1] + ('embedding',)
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  A_  :       int										=  pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
    A_  :       str										=  pt_tensor.transpose(2 ,					3 ,					1 ,					0					)
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  A_  :       Optional[Any]										=  pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight":
    A_  :       Optional[Any]										=  pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm weight
  A_  :       Tuple										=  pt_tuple_key[:-1] + ('weight',)
  if pt_tuple_key[-1] == "gamma":
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm bias
  A_  :       Optional[int]										=  pt_tuple_key[:-1] + ('bias',)
  if pt_tuple_key[-1] == "beta":
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase=42					):






  """simple docstring"""





  A_  :       int										=  {k: v.numpy() for k, v in pt_state_dict.items()}

  # Step 2: Since the model is stateless, get random Flax params
  A_  :       Union[str, Any]										=  flax_model.init_weights(PRNGKey(_UpperCAmelCase					)					)

  A_  :       Optional[Any]										=  flatten_dict(_UpperCAmelCase					)
  A_  :       Tuple										=  {}

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    A_  :       Any										=  rename_key(_UpperCAmelCase					)
    A_  :       List[str]										=  tuple(renamed_pt_key.split('.'					)					)

    # Correctly rename weight parameters
    A_						,       A_  :       Union[str, Any]										=  rename_key_and_reshape_tensor(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					)

    if flax_key in random_flax_state_dict:
      if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
        raise ValueError(
            f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
            f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""					)

        # also add unexpected weight so that warning is thrown
    A_  :       str										=  jnp.asarray(_UpperCAmelCase					)

  return unflatten_dict(_UpperCAmelCase					)