



'''simple docstring'''





from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


__a          =		logging.get_logger(__name__)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Optional[int]:
      try:
            with open(_lowerCAmelCase     ,   """rb"""		) as flax_state_f:
                  snake_case__  :    Any    						=      from_bytes(_lowerCAmelCase     ,   flax_state_f.read()		)
      except UnpicklingError as e:
            try:
                  with open(_lowerCAmelCase		) as f:
                        if f.read().startswith("""version"""		):
                              raise OSError(
                                  """You seem to have cloned a repository without having git-lfs installed. Please"""
                                  """ install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
                                  """ folder you cloned."""		)
                        else:
                              raise ValueError from e
            except (UnicodeDecodeError, ValueError):
                  raise EnvironmentError(f"Unable to convert {model_file} to Flax deserializable object. "		)

      return load_flax_weights_in_pytorch_model(_lowerCAmelCase     ,   _lowerCAmelCase		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:

      try:
            import torch  # noqa: F401
      except ImportError:
            logger.error(
                """Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"""
                """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
                """ instructions."""		)
            raise

      # check if we have bf16 weights
      snake_case__  :    Optional[int]    						=      flatten_dict(jax.tree_util.tree_map(lambda _lowerCAmelCase		: x.dtype == jnp.bfloataa     ,   _lowerCAmelCase		)		).values()
      if any(_lowerCAmelCase		):
            # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

            # and bf16 is not fully supported in PT yet.
            logger.warning(
                """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
                """before loading those in PyTorch model."""		)
            snake_case__  :    Optional[Any]    						=      jax.tree_util.tree_map(
                lambda _lowerCAmelCase		: params.astype(np.floataa		) if params.dtype == jnp.bfloataa else params     ,   _lowerCAmelCase		)

      snake_case__  :    Optional[int]    						=      """"""

      snake_case__  :    Any    						=      flatten_dict(_lowerCAmelCase     ,   sep="""."""		)
      snake_case__  :    Union[str, Any]    						=      pt_model.state_dict()

      # keep track of unexpected & missing keys
      snake_case__  :    Any    						=      []
      snake_case__  :    List[Any]    						=      set(pt_model_dict.keys()		)

      for flax_key_tuple, flax_tensor in flax_state_dict.items():
            snake_case__  :    str    						=      flax_key_tuple.split("""."""		)

            if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
                  snake_case__  :    Dict    						=      flax_key_tuple_array[:-1] + ["""weight"""]
                  snake_case__  :    List[Any]    						=      jnp.transpose(_lowerCAmelCase     ,   (3, 2, 0, 1)		)
            elif flax_key_tuple_array[-1] == "kernel":
                  snake_case__  :    str    						=      flax_key_tuple_array[:-1] + ["""weight"""]
                  snake_case__  :    Dict    						=      flax_tensor.T
            elif flax_key_tuple_array[-1] == "scale":
                  snake_case__  :    Dict    						=      flax_key_tuple_array[:-1] + ["""weight"""]

            if "time_embedding" not in flax_key_tuple_array:
                  for i, flax_key_tuple_string in enumerate(_lowerCAmelCase		):
                        snake_case__  :    int    						=      (
                            flax_key_tuple_string.replace("""_0"""     ,   """.0"""		)
                            .replace("""_1"""     ,   """.1"""		)
                            .replace("""_2"""     ,   """.2"""		)
                            .replace("""_3"""     ,   """.3"""		)
                            .replace("""_4"""     ,   """.4"""		)
                            .replace("""_5"""     ,   """.5"""		)
                            .replace("""_6"""     ,   """.6"""		)
                            .replace("""_7"""     ,   """.7"""		)
                            .replace("""_8"""     ,   """.8"""		)
                            .replace("""_9"""     ,   """.9"""		)
                        )

            snake_case__  :    List[Any]    						=      """.""".join(_lowerCAmelCase		)

            if flax_key in pt_model_dict:
                  if flax_tensor.shape != pt_model_dict[flax_key].shape:
                        raise ValueError(
                            f"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
                            f"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."		)
                  else:
                        # add weight to pytorch dict
                        snake_case__  :    Tuple    						=      np.asarray(_lowerCAmelCase		) if not isinstance(_lowerCAmelCase     ,   np.ndarray		) else flax_tensor
                        snake_case__  :    Optional[int]    						=      torch.from_numpy(_lowerCAmelCase		)
                        # remove from missing keys
                        missing_keys.remove(_lowerCAmelCase		)
            else:
                  # weight is not expected by PyTorch model
                  unexpected_keys.append(_lowerCAmelCase		)

      pt_model.load_state_dict(_lowerCAmelCase		)

      # re-transform missing_keys to list
      snake_case__  :    Tuple    						=      list(_lowerCAmelCase		)

      if len(_lowerCAmelCase		) > 0:
            logger.warning(
                """Some weights of the Flax model were not used when initializing the PyTorch model"""
                f" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
                f" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
                """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
                f" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
                """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
                """ FlaxBertForSequenceClassification model)."""		)
      if len(_lowerCAmelCase		) > 0:
            logger.warning(
                f"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
                f" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
                """ use it for predictions and inference."""		)

      return pt_model







'''simple docstring'''




from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

__SCREAMING_SNAKE_CASE     :Optional[int]       =				TypeVar('''T''')







class   A_	(  Generic[T]			):




 def __init__(					self       :    List[Any]	,			snake_case_       :    list[T]	,			snake_case_       :    Callable[[T, T], T]    ):
      _UpperCAmelCase     							=   None

      _UpperCAmelCase     							=   len(snake_case_    )
      _UpperCAmelCase     							=   [any_type for _ in range(self.N    )] + arr
      _UpperCAmelCase     							=   fnc
      self.build()




 def      lowercase	(					self       :    List[Any]    ):
      for p in range(self.N - 1	,			0	,			-1    ):
           _UpperCAmelCase     							=   self.fn(self.st[p * 2]	,			self.st[p * 2 + 1]    )




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    int	,			snake_case_       :    T    ):
      p += self.N
      _UpperCAmelCase     							=   v
      while p > 1:
           _UpperCAmelCase     							=   p // 2
           _UpperCAmelCase     							=   self.fn(self.st[p * 2]	,			self.st[p * 2 + 1]    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    int	,			snake_case_       :    int    ):  # noqa: E741
      _UpperCAmelCase							,  _UpperCAmelCase     							=   l + self.N, r + self.N

      _UpperCAmelCase     							=   None
      while l <= r:
           if l % 2 == 1:
                _UpperCAmelCase     							=   self.st[l] if res is None else self.fn(snake_case_	,			self.st[l]    )
           if r % 2 == 0:
                _UpperCAmelCase     							=   self.st[r] if res is None else self.fn(snake_case_	,			self.st[r]    )
           _UpperCAmelCase							,  _UpperCAmelCase     							=   (l + 1) // 2, (r - 1) // 2
      return res


if __name__ == "__main__":
  from functools import reduce

  __SCREAMING_SNAKE_CASE     :Union[str, Any]       =				[1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

  __SCREAMING_SNAKE_CASE     :List[str]       =				{
      0: 7,
      1: 2,
      2: 6,
      3: -14,
      4: 5,
      5: 4,
      6: 7,
      7: -10,
      8: 9,
      9: 10,
      10: 12,
      11: 1,
  }

  __SCREAMING_SNAKE_CASE     :Any       =				SegmentTree(test_array, min)
  __SCREAMING_SNAKE_CASE     :Any       =				SegmentTree(test_array, max)
  __SCREAMING_SNAKE_CASE     :Any       =				SegmentTree(test_array, lambda a, b: a + b)
  def 	UpperCAmelCase_							(					)		->       None:


       '''simple docstring'''



       for i in range(len(__lowercase       )       ):
            for j in range(__lowercase			,     len(__lowercase       )       ):
                 _UpperCAmelCase     							=   reduce(__lowercase			,     test_array[i : j + 1]       )
                 _UpperCAmelCase     							=   reduce(__lowercase			,     test_array[i : j + 1]       )
                 _UpperCAmelCase     							=   reduce(lambda __lowercase			,     __lowercase       : a + b			,     test_array[i : j + 1]       )
                 assert min_range == min_segment_tree.query(__lowercase			,     __lowercase       )
                 assert max_range == max_segment_tree.query(__lowercase			,     __lowercase       )
                 assert sum_range == sum_segment_tree.query(__lowercase			,     __lowercase       )

  test_all_segments()

  for index, value in test_updates.items():
    __SCREAMING_SNAKE_CASE     :str       =				value
    min_segment_tree.update(index, value)
    max_segment_tree.update(index, value)
    sum_segment_tree.update(index, value)
    test_all_segments()




