


'''simple docstring'''



import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()




def 			_a			(		_lowercase      :			Union[str, Any]    ,      _lowercase      :			Union[str, Any]    ,      _lowercase      :			int    ,      _lowercase      :			Dict="attention"						):





     '''simple docstring'''






     __UpperCAmelCase			:				Optional[Any]													=    params[F'{prefix}/layers_{i}/{layer_name}/key/kernel']
     __UpperCAmelCase			:				int													=    params[F'{prefix}/layers_{i}/{layer_name}/out/kernel']
     __UpperCAmelCase			:				Dict													=    params[F'{prefix}/layers_{i}/{layer_name}/query/kernel']
     __UpperCAmelCase			:				List[Any]													=    params[F'{prefix}/layers_{i}/{layer_name}/value/kernel']
     return k, o, q, v




def 			_a			(		_lowercase      :			str    ,      _lowercase      :			Dict    ,      _lowercase      :			Optional[int]    ,      _lowercase      :			Optional[int]=False						):





     '''simple docstring'''






     if split_mlp_wi:
          __UpperCAmelCase			:				Union[str, Any]													=    params[F'{prefix}/layers_{i}/mlp/wi_0/kernel']
          __UpperCAmelCase			:				Tuple													=    params[F'{prefix}/layers_{i}/mlp/wi_1/kernel']
          __UpperCAmelCase			:				int													=    (wi_a, wi_a)
     else:
          __UpperCAmelCase			:				Tuple													=    params[F'{prefix}/layers_{i}/mlp/wi/kernel']

     __UpperCAmelCase			:				List[str]													=    params[F'{prefix}/layers_{i}/mlp/wo/kernel']
     return wi, wo




def 			_a			(		_lowercase      :			Any    ,      _lowercase      :			List[str]    ,      _lowercase      :			int    ,      _lowercase      :			List[Any]						):





     '''simple docstring'''






     return params[F'{prefix}/layers_{i}/{layer_name}/scale']




def 			_a			(		_lowercase      :			Optional[int]    ,      *, _lowercase      :			Optional[Any]    ,      _lowercase      :			Tuple						):





     '''simple docstring'''






     __UpperCAmelCase			:				Dict													=    traverse_util.flatten_dict(variables['''target''']						)
     __UpperCAmelCase			:				Any													=    {'''/'''.join(SCREAMING_SNAKE_CASE_						): v for k, v in old.items()}

     # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
     __UpperCAmelCase			:				Optional[int]													=    '''encoder/layers_0/mlp/wi_0/kernel''' in old
     print('''Split MLP:'''    ,      SCREAMING_SNAKE_CASE_						)

     __UpperCAmelCase			:				Union[str, Any]													=    collections.OrderedDict()

     # Shared embeddings.
     __UpperCAmelCase			:				Dict													=    old['''token_embedder/embedding''']

     # Encoder.
     for i in range(SCREAMING_SNAKE_CASE_						):
          # Block i, layer 0 (Self Attention).
          __UpperCAmelCase			:				Tuple													=    tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      '''encoder'''    ,      '''pre_attention_layer_norm'''						)
          __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase			:				Tuple													=    tax_attention_lookup(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      '''encoder'''    ,      '''attention'''						)
          __UpperCAmelCase			:				Optional[Any]													=    layer_norm
          __UpperCAmelCase			:				List[str]													=    k.T
          __UpperCAmelCase			:				Any													=    o.T
          __UpperCAmelCase			:				List[Any]													=    q.T
          __UpperCAmelCase			:				List[str]													=    v.T

          # Block i, layer 1 (MLP).
          __UpperCAmelCase			:				int													=    tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      '''encoder'''    ,      '''pre_mlp_layer_norm'''						)
          __UpperCAmelCase							,     __UpperCAmelCase			:				int													=    tax_mlp_lookup(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      '''encoder'''    ,      SCREAMING_SNAKE_CASE_						)
          __UpperCAmelCase			:				Any													=    layer_norm
          if split_mlp_wi:
               __UpperCAmelCase			:				int													=    wi[0].T
               __UpperCAmelCase			:				str													=    wi[1].T
          else:
               __UpperCAmelCase			:				List[Any]													=    wi.T
          __UpperCAmelCase			:				List[Any]													=    wo.T

     __UpperCAmelCase			:				int													=    old[
         '''encoder/relpos_bias/rel_embedding'''
     ].T
     __UpperCAmelCase			:				Tuple													=    old['''encoder/encoder_norm/scale''']

     if not is_encoder_only:
          # Decoder.
          for i in range(SCREAMING_SNAKE_CASE_						):
               # Block i, layer 0 (Self Attention).
               __UpperCAmelCase			:				Optional[int]													=    tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      '''decoder'''    ,      '''pre_self_attention_layer_norm'''						)
               __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase			:				Union[str, Any]													=    tax_attention_lookup(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      '''decoder'''    ,      '''self_attention'''						)
               __UpperCAmelCase			:				Any													=    layer_norm
               __UpperCAmelCase			:				int													=    k.T
               __UpperCAmelCase			:				Any													=    o.T
               __UpperCAmelCase			:				str													=    q.T
               __UpperCAmelCase			:				Any													=    v.T

               # Block i, layer 1 (Cross Attention).
               __UpperCAmelCase			:				Union[str, Any]													=    tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      '''decoder'''    ,      '''pre_cross_attention_layer_norm'''						)
               __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase			:				Union[str, Any]													=    tax_attention_lookup(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      '''decoder'''    ,      '''encoder_decoder_attention'''						)
               __UpperCAmelCase			:				List[Any]													=    layer_norm
               __UpperCAmelCase			:				Dict													=    k.T
               __UpperCAmelCase			:				str													=    o.T
               __UpperCAmelCase			:				Any													=    q.T
               __UpperCAmelCase			:				str													=    v.T

               # Block i, layer 2 (MLP).
               __UpperCAmelCase			:				Any													=    tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      '''decoder'''    ,      '''pre_mlp_layer_norm'''						)
               __UpperCAmelCase							,     __UpperCAmelCase			:				int													=    tax_mlp_lookup(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      '''decoder'''    ,      SCREAMING_SNAKE_CASE_						)
               __UpperCAmelCase			:				Union[str, Any]													=    layer_norm
               if split_mlp_wi:
                    __UpperCAmelCase			:				Union[str, Any]													=    wi[0].T
                    __UpperCAmelCase			:				Optional[int]													=    wi[1].T
               else:
                    __UpperCAmelCase			:				Any													=    wi.T
               __UpperCAmelCase			:				List[str]													=    wo.T

          __UpperCAmelCase			:				List[str]													=    old['''decoder/decoder_norm/scale''']
          __UpperCAmelCase			:				Any													=    old[
              '''decoder/relpos_bias/rel_embedding'''
          ].T

          # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
          if "decoder/logits_dense/kernel" in old:
               __UpperCAmelCase			:				str													=    old['''decoder/logits_dense/kernel'''].T

     return new




def 			_a			(		_lowercase      :			int    ,      _lowercase      :			int						):





     '''simple docstring'''






     __UpperCAmelCase			:				Optional[Any]													=    collections.OrderedDict([(k, torch.from_numpy(v.copy()						)) for (k, v) in converted_params.items()]						)

     # Add what is missing.
     if "encoder.embed_tokens.weight" not in state_dict:
          __UpperCAmelCase			:				Tuple													=    state_dict['''shared.weight''']

     if not is_encoder_only:
          if "decoder.embed_tokens.weight" not in state_dict:
               __UpperCAmelCase			:				Any													=    state_dict['''shared.weight''']

          if "lm_head.weight" not in state_dict:  # For old 1.0 models.
               print('''Using shared word embeddings as lm_head.'''						)
               __UpperCAmelCase			:				List[Any]													=    state_dict['''shared.weight''']

     return state_dict




def 			_a			(		_lowercase      :			Tuple    ,      _lowercase      :			Union[str, Any]    ,      _lowercase      :			int    ,      _lowercase      :			Dict						):





     '''simple docstring'''






     __UpperCAmelCase			:				List[Any]													=    checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_						)
     __UpperCAmelCase			:				int													=    convert_tax_to_pytorch(SCREAMING_SNAKE_CASE_    ,      num_layers=config.num_layers    ,      is_encoder_only=SCREAMING_SNAKE_CASE_						)
     __UpperCAmelCase			:				Union[str, Any]													=    make_state_dict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
     model.load_state_dict(SCREAMING_SNAKE_CASE_    ,      strict=SCREAMING_SNAKE_CASE_						)




def 			_a			(		_lowercase      :			Tuple    ,      _lowercase      :			Optional[int]    ,      _lowercase      :			List[Any]    ,      _lowercase      :			Dict = False						):





     '''simple docstring'''






     __UpperCAmelCase			:				int													=    TaConfig.from_json_file(SCREAMING_SNAKE_CASE_						)
     print(F'Building PyTorch model from configuration: {config}'						)
     # Non-v1.1 checkpoints could also use T5Model, but this works for all.
     # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
     if is_encoder_only:
          __UpperCAmelCase			:				Union[str, Any]													=    TaEncoderModel(SCREAMING_SNAKE_CASE_						)
     else:
          __UpperCAmelCase			:				int													=    TaForConditionalGeneration(SCREAMING_SNAKE_CASE_						)

     # Load weights from tf checkpoint
     load_tax_weights_in_ta(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)

     # Save pytorch-model
     print(F'Save PyTorch model to {pytorch_dump_path}'						)
     model.save_pretrained(SCREAMING_SNAKE_CASE_						)

     # Verify that we can load the checkpoint.
     model.from_pretrained(SCREAMING_SNAKE_CASE_						)
     print('''Done'''						)


if __name__ == "__main__":
      snake_case_    :Optional[Any]				       =     argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
      # Required parameters
      parser.add_argument(
          "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
      )
      parser.add_argument(
          "--config_file",
          default=None,
          type=str,
          required=True,
          help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
      )
      snake_case_    :int				       =     parser.parse_args()
      convert_tax_checkpoint_to_pytorch(
          args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
      )



'''simple docstring'''



from collections.abc import Iterable
from typing import Any



class 			a       :


     """simple docstring"""






     def __init__(       self  :	Any							,				snake_case  :	int | None = None    )		->							int:
          __UpperCAmelCase			:				str													=    value
          __UpperCAmelCase			:				Node | None													=    None  # Added in order to delete a node easier
          __UpperCAmelCase			:				Node | None													=    None
          __UpperCAmelCase			:				Node | None													=    None






     def __repr__(       self  :	str    )		->							str:
          from pprint import pformat

          if self.left is None and self.right is None:
               return str(self.value    )
          return pformat({f'{self.value}': (self.left, self.right)}							,				indent=1    )



class 			a       :


     """simple docstring"""






     def __init__(       self  :	Optional[int]							,				snake_case  :	Node | None = None    )		->							str:
          __UpperCAmelCase			:				Optional[Any]													=    root

     def __str__(       self  :	str    )		->							str:
          return str(self.root    )

     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	Node							,				snake_case  :	Node | None    )		->							None:
          if new_children is not None:  # reset its kids
               __UpperCAmelCase			:				List[str]													=    node.parent
          if node.parent is not None:  # reset its parent
               if self.is_right(snake_case    ):  # If it is the right children
                    __UpperCAmelCase			:				int													=    new_children
               else:
                    __UpperCAmelCase			:				Tuple													=    new_children
          else:
               __UpperCAmelCase			:				List[Any]													=    new_children

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	Node    )		->							bool:
          if node.parent and node.parent.right:
               return node == node.parent.right
          return False

     def 				lowerCamelCase__				(       self  :	int    )		->							bool:
          return self.root is None

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	Optional[Any]    )		->							None:
          __UpperCAmelCase			:				int													=    Node(snake_case    )  # create a new Node
          if self.empty():  # if Tree is empty
               __UpperCAmelCase			:				List[Any]													=    new_node  # set its root
          else:  # Tree is not empty
               __UpperCAmelCase			:				Tuple													=    self.root  # from root
               if parent_node is None:
                    return
               while True:  # While we don't get to a leaf
                    if value < parent_node.value:  # We go left
                         if parent_node.left is None:
                              __UpperCAmelCase			:				Optional[int]													=    new_node  # We insert the new node in a leaf
                              break
                         else:
                              __UpperCAmelCase			:				List[Any]													=    parent_node.left
                    else:
                         if parent_node.right is None:
                              __UpperCAmelCase			:				Optional[int]													=    new_node
                              break
                         else:
                              __UpperCAmelCase			:				List[str]													=    parent_node.right
               __UpperCAmelCase			:				int													=    parent_node

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				*snake_case  :	List[Any]    )		->							None:
          for value in values:
               self.__insert(snake_case    )

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	Dict    )		->							Node | None:
          if self.empty():
               raise IndexError('''Warning: Tree is empty! please use another.'''    )
          else:
               __UpperCAmelCase			:				List[Any]													=    self.root
               # use lazy evaluation here to avoid NoneType Attribute error
               while node is not None and node.value is not value:
                    __UpperCAmelCase			:				Union[str, Any]													=    node.left if value < node.value else node.right
               return node

     def 				lowerCamelCase__				(       self  :	str							,				snake_case  :	Node | None = None    )		->							Node | None:
          if node is None:
               if self.root is None:
                    return None
               __UpperCAmelCase			:				Optional[Any]													=    self.root

          if not self.empty():
               while node.right is not None:
                    __UpperCAmelCase			:				str													=    node.right
          return node

     def 				lowerCamelCase__				(       self  :	int							,				snake_case  :	Node | None = None    )		->							Node | None:
          if node is None:
               __UpperCAmelCase			:				str													=    self.root
          if self.root is None:
               return None
          if not self.empty():
               __UpperCAmelCase			:				List[str]													=    self.root
               while node.left is not None:
                    __UpperCAmelCase			:				str													=    node.left
          return node

     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	int    )		->							None:
          __UpperCAmelCase			:				List[str]													=    self.search(snake_case    )  # Look for the node with that label
          if node is not None:
               if node.left is None and node.right is None:  # If it has no children
                    self.__reassign_nodes(snake_case							,				snake_case    )
               elif node.left is None:  # Has only right children
                    self.__reassign_nodes(snake_case							,				node.right    )
               elif node.right is None:  # Has only left children
                    self.__reassign_nodes(snake_case							,				node.left    )
               else:
                    __UpperCAmelCase			:				Optional[int]													=    self.get_max(
                        node.left    )  # Gets the max value of the left branch
                    self.remove(tmp_node.value    )  # type: ignore
                    __UpperCAmelCase			:				List[str]													=    (
                        tmp_node.value  # type: ignore
                    )  # Assigns the value to the node to delete and keep tree structure

     def 				lowerCamelCase__				(       self  :	List[str]							,				snake_case  :	Node | None    )		->							Iterable:
          if node is not None:
               yield node  # Preorder Traversal
               yield from self.preorder_traverse(node.left    )
               yield from self.preorder_traverse(node.right    )

     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	int=None    )		->							Any:
          if traversal_function is None:
               return self.preorder_traverse(self.root    )
          else:
               return traversal_function(self.root    )

     def 				lowerCamelCase__				(       self  :	str							,				snake_case  :	list							,				snake_case  :	Node | None    )		->							None:
          if node:
               self.inorder(snake_case							,				node.left    )
               arr.append(node.value    )
               self.inorder(snake_case							,				node.right    )






     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	int							,				snake_case  :	Node    )		->							int:
          __UpperCAmelCase			:				list[int]													=    []
          self.inorder(snake_case							,				snake_case    )  # append all values to list using inorder traversal
          return arr[k - 1]




def 			_a			(		_lowercase      :			Node | None						):





     '''simple docstring'''






     __UpperCAmelCase			:				List[str]													=    []
     if curr_node is not None:
          __UpperCAmelCase			:				Union[str, Any]													=    postorder(curr_node.left						) + postorder(curr_node.right						) + [curr_node]
     return node_list




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				Optional[Any]													=    (8, 3, 6, 1, 10, 14, 13, 4, 7)
     __UpperCAmelCase			:				Union[str, Any]													=    BinarySearchTree()
     for i in testlist:
          t.insert(_lowercase						)

     # Prints all the elements of the list in order traversal
     print(_lowercase						)

     if t.search(6						) is not None:
          print('''The value 6 exists'''						)
     else:
          print('''The value 6 doesn\'t exist'''						)

     if t.search(-1						) is not None:
          print('''The value -1 exists'''						)
     else:
          print('''The value -1 doesn\'t exist'''						)

     if not t.empty():
          print('''Max Value: '''    ,      t.get_max().value						)  # type: ignore
          print('''Min Value: '''    ,      t.get_min().value						)  # type: ignore

     for i in testlist:
          t.remove(_lowercase						)
          print(_lowercase						)


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)