


'''simple docstring'''






import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch


def  _snake_case       (					A	)						->   Any:
       lowerCAmelCase__	       =     os.path.join(args.tf_model_dir			,   '''parameters.json'''	)
       lowerCAmelCase__	       =     json.loads(open(__snake_case	).read()	)
       if not params:
              raise ValueError(
                  F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""	)
       if not args.output.endswith('''.pt'''	):
              lowerCAmelCase__	       =     args.output + ".pt"
       lowerCAmelCase__	       =     OrderedDict()
       with tf.device('''/CPU:0'''	):
              lowerCAmelCase__	       =     tf.train.load_checkpoint(args.tf_model_dir	)
              lowerCAmelCase__	       =     reader.get_variable_to_shape_map()
              for key_name in shapes.keys():
                     lowerCAmelCase__	       =     reader.get_tensor(__snake_case	).astype(np.floataa	)
                     if key_name.endswith('''/adam_m'''	) or key_name.endswith('''/adam_v'''	):
                            continue
                     if key_name.startswith('''pasts/'''	):
                            if key_name.startswith('''pasts/mlp'''	):
                                   lowerCAmelCase__	       =     int(key_name[9]	)
                            elif key_name.startswith('''pasts/out'''	):
                                   lowerCAmelCase__	       =     8
                            lowerCAmelCase__	       =     "model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
                            lowerCAmelCase__	       =     vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
                            lowerCAmelCase__	       =     torch.tensor(__snake_case	)
                     elif key_name.startswith('''model/moe'''	):
                            lowerCAmelCase__	       =     int(key_name[9:].split('''/'''	)[0]	)
                            if key_name.endswith('''/switch_gating/kernel'''	):
                                   lowerCAmelCase__	       =     "model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
                                   lowerCAmelCase__	       =     vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   lowerCAmelCase__	       =     torch.tensor(__snake_case	)
                            elif key_name.endswith('''/softmlp/kernel'''	):
                                   lowerCAmelCase__	       =     "model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
                                   lowerCAmelCase__	       =     vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   lowerCAmelCase__	       =     torch.tensor(__snake_case	)
                            elif key_name.endswith('''/wo/kernel'''	) or key_name.endswith('''/wi/kernel'''	):
                                   lowerCAmelCase__	       =     key_name[-9:-7]
                                   for i in range(16	):
                                          lowerCAmelCase__	       =     "model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
                                          lowerCAmelCase__	       =     (
                                              vnp[i].transpose([1, 0]	).copy()
                                          )  # In Mesh-Tensorflow, it is one array, so it is divided
                                          lowerCAmelCase__	       =     torch.tensor(__snake_case	)
                     elif key_name.startswith('''model/mlp'''	):
                            lowerCAmelCase__	       =     int(key_name[9:].split('''/'''	)[0]	)
                            if key_name.endswith('''/p1/kernel'''	):
                                   lowerCAmelCase__	       =     "model.blocks.%d.feed_forward.mlp.wi.weight" % player
                                   lowerCAmelCase__	       =     vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   lowerCAmelCase__	       =     torch.tensor(__snake_case	)
                            elif key_name.endswith('''/p1/bias'''	):
                                   lowerCAmelCase__	       =     "model.blocks.%d.feed_forward.mlp.wi.bias" % player
                                   lowerCAmelCase__	       =     vnp.copy()  # same because it is one dimensional
                                   lowerCAmelCase__	       =     torch.tensor(__snake_case	)
                            elif key_name.endswith('''/p2/kernel'''	):
                                   lowerCAmelCase__	       =     "model.blocks.%d.feed_forward.mlp.wo.weight" % player
                                   lowerCAmelCase__	       =     vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   lowerCAmelCase__	       =     torch.tensor(__snake_case	)
                            elif key_name.endswith('''/p2/bias'''	):
                                   lowerCAmelCase__	       =     "model.blocks.%d.feed_forward.mlp.wo.bias" % player
                                   lowerCAmelCase__	       =     vnp.copy()  # same because it is one dimensional
                                   lowerCAmelCase__	       =     torch.tensor(__snake_case	)
                     elif key_name.startswith('''model/ln'''	):
                            lowerCAmelCase__	       =     int(key_name[8:].split('''/'''	)[0]	)
                            if key_name.endswith('''/b'''	):
                                   lowerCAmelCase__	       =     "model.blocks.%d.feed_forward.norm.bias" % player
                                   lowerCAmelCase__	       =     vnp.copy()  # same because it is one dimensional
                                   lowerCAmelCase__	       =     torch.tensor(__snake_case	)
                            elif key_name.endswith('''/g'''	):
                                   lowerCAmelCase__	       =     "model.blocks.%d.feed_forward.norm.weight" % player
                                   lowerCAmelCase__	       =     vnp.copy()  # same because it is one dimensional
                                   lowerCAmelCase__	       =     torch.tensor(__snake_case	)
                     elif key_name.startswith('''model/att'''	):
                            lowerCAmelCase__	       =     int(key_name[9:].split('''/'''	)[0]	)
                            if key_name.endswith('''/qkv/kernel'''	):
                                   lowerCAmelCase__	       =     vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
                                   lowerCAmelCase__	       =     state[:, 0, :, :]
                                   lowerCAmelCase__	       =     state[:, 1, :, :]
                                   lowerCAmelCase__	       =     state[:, 2, :, :]
                                   lowerCAmelCase__	       =     (
                                       state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]	)
                                       .transpose([1, 0]	)
                                       .copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   lowerCAmelCase__	       =     (
                                       state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]	)
                                       .transpose([1, 0]	)
                                       .copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   lowerCAmelCase__	       =     (
                                       state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]	)
                                       .transpose([1, 0]	)
                                       .copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   lowerCAmelCase__	       =     "model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
                                   lowerCAmelCase__	       =     torch.tensor(__snake_case	)
                                   lowerCAmelCase__	       =     "model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
                                   lowerCAmelCase__	       =     torch.tensor(__snake_case	)
                                   lowerCAmelCase__	       =     "model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
                                   lowerCAmelCase__	       =     torch.tensor(__snake_case	)
                            elif key_name.endswith('''/o/kernel'''	):
                                   lowerCAmelCase__	       =     "model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
                                   lowerCAmelCase__	       =     (
                                       vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]	).transpose([1, 0]	).copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   lowerCAmelCase__	       =     torch.tensor(__snake_case	)
                     elif key_name.startswith('''model/an'''	):
                            lowerCAmelCase__	       =     int(key_name[8:].split('''/'''	)[0]	)
                            if key_name.endswith('''/b'''	):
                                   lowerCAmelCase__	       =     "model.blocks.%d.self_attn.norm.bias" % player
                                   lowerCAmelCase__	       =     vnp.copy()  # same because it is one dimensional
                                   lowerCAmelCase__	       =     torch.tensor(__snake_case	)
                            elif key_name.endswith('''/g'''	):
                                   lowerCAmelCase__	       =     "model.blocks.%d.self_attn.norm.weight" % player
                                   lowerCAmelCase__	       =     vnp.copy()  # same because it is one dimensional
                                   lowerCAmelCase__	       =     torch.tensor(__snake_case	)
                     elif (
                         key_name.startswith('''model/wte'''	)
                         or key_name.startswith('''model/wpe'''	)
                         or key_name.startswith('''model/ete'''	)
                     ):
                            lowerCAmelCase__	       =     {"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
                                key_name[-3:]
                            ]
                            lowerCAmelCase__	       =     "model.%s.weight" % nlayer
                            lowerCAmelCase__	       =     vnp.copy()  # same in embedded
                            lowerCAmelCase__	       =     torch.tensor(__snake_case	)
                            if key_name.startswith('''model/wte'''	):
                                   lowerCAmelCase__	       =     "lm_head.weight"
                                   lowerCAmelCase__	       =     vnp.copy()  # same in embedded
                                   lowerCAmelCase__	       =     torch.tensor(__snake_case	)
                     elif key_name.startswith('''model/wob'''	):
                            lowerCAmelCase__	       =     "final_logits_bias"
                            lowerCAmelCase__	       =     vnp.copy()  # same in embedded
                            lowerCAmelCase__	       =     state.reshape((1, -1)	)
                            lowerCAmelCase__	       =     torch.tensor(__snake_case	)
                     elif key_name == "model/dense/kernel":
                            lowerCAmelCase__	       =     "model.last_project.weight"
                            lowerCAmelCase__	       =     vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
                            lowerCAmelCase__	       =     torch.tensor(__snake_case	)
                     elif key_name == "model/dense_1/bias":
                            lowerCAmelCase__	       =     "model.last_project.bias"
                            lowerCAmelCase__	       =     vnp.copy()  # same because it is one dimensional
                            lowerCAmelCase__	       =     torch.tensor(__snake_case	)
       torch.save(__snake_case			,   args.output	)


if __name__ == "__main__":
  __UpperCAmelCase       =							argparse.ArgumentParser(
      description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
  )
  parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
  parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
  __UpperCAmelCase       =							parser.parse_args()
  convert_tf_gptsan_to_pt(args)



'''simple docstring'''






from collections.abc import Iterable
from typing import Any




class 		a__    :





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_ = None       )	->							List[str]:
														lowerCAmelCase__	       =     value
														lowerCAmelCase__	       =     None  # Added in order to delete a node easier
														lowerCAmelCase__	       =     None
														lowerCAmelCase__	       =     None







							def __repr__(		self       )	->							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 ,   lowerCamelCase_ = None       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     root







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







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							None:
														if new_children is not None:  # reset its kids
																					lowerCAmelCase__	       =     node.parent
														if node.parent is not None:  # reset its parent
																					if self.is_right(lowerCamelCase_       ):  # If it is the right children
																												lowerCAmelCase__	       =     new_children
																					else:
																												lowerCAmelCase__	       =     new_children
														else:
																					lowerCAmelCase__	       =     new_children







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							bool:
														if node.parent and node.parent.right:
																					return node == node.parent.right
														return False







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







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							None:
														lowerCAmelCase__	       =     Node(lowerCamelCase_       )  # create a new Node
														if self.empty():  # if Tree is empty
																					lowerCAmelCase__	       =     new_node  # set its root
														else:  # Tree is not empty
																					lowerCAmelCase__	       =     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:
																																										lowerCAmelCase__	       =     new_node  # We insert the new node in a leaf
																																										break
																																			else:
																																										lowerCAmelCase__	       =     parent_node.left
																												else:
																																			if parent_node.right is None:
																																										lowerCAmelCase__	       =     new_node
																																										break
																																			else:
																																										lowerCAmelCase__	       =     parent_node.right
																					lowerCAmelCase__	       =     parent_node







							def   __SCREAMING_SNAKE_CASE							(		self ,   *lowerCamelCase_       )	->							None:
														for value in values:
																					self.__insert(lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Node | None:
														if self.empty():
																					raise IndexError('''Warning: Tree is empty! please use another.'''       )
														else:
																					lowerCAmelCase__	       =     self.root
																					# use lazy evaluation here to avoid NoneType Attribute error
																					while node is not None and node.value is not value:
																												lowerCAmelCase__	       =     node.left if value < node.value else node.right
																					return node







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ = None       )	->							Node | None:
														if node is None:
																					if self.root is None:
																												return None
																					lowerCAmelCase__	       =     self.root

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







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ = None       )	->							Node | None:
														if node is None:
																					lowerCAmelCase__	       =     self.root
														if self.root is None:
																					return None
														if not self.empty():
																					lowerCAmelCase__	       =     self.root
																					while node.left is not None:
																												lowerCAmelCase__	       =     node.left
														return node







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							None:
														lowerCAmelCase__	       =     self.search(lowerCamelCase_       )  # 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(lowerCamelCase_ ,   lowerCamelCase_       )
																					elif node.left is None:  # Has only right children
																												self.__reassign_nodes(lowerCamelCase_ ,   node.right       )
																					elif node.right is None:  # Has only left children
																												self.__reassign_nodes(lowerCamelCase_ ,   node.left       )
																					else:
																												lowerCAmelCase__	       =     self.get_max(
																												    node.left       )  # Gets the max value of the left branch
																												self.remove(tmp_node.value       )  # type: ignore
																												lowerCAmelCase__	       =     (
																												    tmp_node.value  # type: ignore
																												)  # Assigns the value to the node to delete and keep tree structure







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							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   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_=None       )	->							Any:
														if traversal_function is None:
																					return self.preorder_traverse(self.root       )
														else:
																					return traversal_function(self.root       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							None:
														if node:
																					self.inorder(lowerCamelCase_ ,   node.left       )
																					arr.append(node.value       )
																					self.inorder(lowerCamelCase_ ,   node.right       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							int:
														lowerCAmelCase__	       =     []
														self.inorder(lowerCamelCase_ ,   lowerCamelCase_       )  # append all values to list using inorder traversal
														return arr[k - 1]


def  _snake_case       (					A	)						->   list[Node]:
							lowerCAmelCase__	       =     []
							if curr_node is not None:
														lowerCAmelCase__	       =     postorder(curr_node.left	) + postorder(curr_node.right	) + [curr_node]
							return node_list


def  _snake_case       (					)						->   None:
							lowerCAmelCase__	       =     (8, 3, 6, 1, 10, 14, 13, 4, 7)
							lowerCAmelCase__	       =     BinarySearchTree()
							for i in testlist:
														t.insert(A	)

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

							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(A	)
														print(A	)


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)