





"""simple docstring"""




from __future__ import annotations

from random import random
from typing import Generic, TypeVar

a						: Any     	=			TypeVar('''KT''')
a						: List[str]     	=			TypeVar('''VT''')


class        __UpperCamelCase				(						Generic[KT, VT]						):






							def __init__(   self      ,					lowerCAmelCase__ = "root"      ,					lowerCAmelCase__ = None      )	-> Dict:
								a      :		List[str]	=				key
								a      :		Dict	=				value
								a      :		list[Node[KT, VT]]	=				[]






							def __repr__(   self      )	-> str:
								return f"""Node({self.key}: {self.value})"""






							@property
							def     __a   (   self      )	-> int:
								return len(self.forward      )


class        __UpperCamelCase				(						Generic[KT, VT]						):






							def __init__(   self      ,					lowerCAmelCase__ = 0.5      ,					lowerCAmelCase__ = 16      )	-> Optional[Any]:
								a      :		Node[KT, VT]	=				Node[KT, VT]()
								a      :		Union[str, Any]	=				0
								a      :		str	=				p
								a      :		List[str]	=				max_level






							def __str__(   self      )	-> str:
								a      :		str	=				list(self      )

								if len(lowerCAmelCase__      ) == 0:
									return f"""SkipList(level={self.level})"""

								a      :		List[str]	=				max((len(str(lowerCAmelCase__      )      ) for item in items)      ,					default=4      )
								a      :		Any	=				max(lowerCAmelCase__      ,					4      ) + 4

								a      :		Tuple	=				self.head
								a      :		int	=				[]

								a      :		int	=				node.forward.copy()
								lines.append(f"""[{node.key}]""".ljust(lowerCAmelCase__      ,					"-"      ) + "* " * len(lowerCAmelCase__      )      )
								lines.append(" " * label_size + "| " * len(lowerCAmelCase__      )      )

								while len(node.forward      ) != 0:
									a      :		Optional[int]	=				node.forward[0]

									lines.append(
									    f"""[{node.key}]""".ljust(lowerCAmelCase__      ,					"-"      )
									    + " ".join(str(n.key      ) if n.key == node.key else "|" for n in forwards      )      )
									lines.append(" " * label_size + "| " * len(lowerCAmelCase__      )      )
									a      :		Any	=				node.forward

								lines.append("None".ljust(lowerCAmelCase__      ) + "* " * len(lowerCAmelCase__      )      )
								return f"""SkipList(level={self.level})\n""" + "\n".join(lowerCAmelCase__      )






							def __iter__(   self      )	-> List[str]:
								a      :		str	=				self.head

								while len(node.forward      ) != 0:
									yield node.forward[0].key
									a      :		Optional[int]	=				node.forward[0]






							def     __a   (   self      )	-> int:
								a      :		str	=				1
								while random() < self.p and level < self.max_level:
									level += 1

								return level






							def     __a   (   self      ,					lowerCAmelCase__      )	-> tuple[Node[KT, VT] | None, list[Node[KT, VT]]]:
								a      :		Optional[int]	=				[]

								a      :		List[str]	=				self.head

								for i in reversed(range(self.level      )      ):
									# i < node.level - When node level is lesser than `i` decrement `i`.
									# node.forward[i].key < key - Jumping to node with key value higher
									#                             or equal to searched key would result
									#                             in skipping searched key.
									while i < node.level and node.forward[i].key < key:
										a      :		Union[str, Any]	=				node.forward[i]
									# Each leftmost node (relative to searched node) will potentially have to
									# be updated.
									update_vector.append(lowerCAmelCase__      )

								update_vector.reverse()  # Note that we were inserting values in reverse order.

								# len(node.forward) != 0 - If current node doesn't contain any further
								#                          references then searched key is not present.
								# node.forward[0].key == key - Next node key should be equal to search key
								#                              if key is present.
								if len(node.forward      ) != 0 and node.forward[0].key == key:
									return node.forward[0], update_vector
								else:
									return None, update_vector






							def     __a   (   self      ,					lowerCAmelCase__      )	-> int:
								a,       a      :		str	=				self._locate_node(lowerCAmelCase__      )

								if node is not None:
									for i, update_node in enumerate(lowerCAmelCase__      ):
										# Remove or replace all references to removed node.
										if update_node.level > i and update_node.forward[i].key == key:
											if node.level > i:
												a      :		Any	=				node.forward[i]
											else:
												a      :		List[str]	=				update_node.forward[:i]






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Tuple:
								a,       a      :		Optional[Any]	=				self._locate_node(lowerCAmelCase__      )
								if node is not None:
									a      :		int	=				value
								else:
									a      :		Dict	=				self.random_level()

									if level > self.level:
										# After level increase we have to add additional nodes to head.
										for _ in range(self.level - 1      ,					lowerCAmelCase__      ):
											update_vector.append(self.head      )
										a      :		List[Any]	=				level

									a      :		Tuple	=				Node(lowerCAmelCase__      ,					lowerCAmelCase__      )

									for i, update_node in enumerate(update_vector[:level]      ):
										# Change references to pass through new node.
										if update_node.level > i:
											new_node.forward.append(update_node.forward[i]      )

										if update_node.level < i + 1:
											update_node.forward.append(lowerCAmelCase__      )
										else:
											a      :		Optional[int]	=				new_node






							def     __a   (   self      ,					lowerCAmelCase__      )	-> VT | None:
								a,       a      :		Any	=				self._locate_node(lowerCAmelCase__      )

								if node is not None:
									return node.value

								return None






def       _SCREAMING_SNAKE_CASE		(	) ->Optional[int]:






	'''simple docstring'''






	a      :		Union[str, Any]	=				SkipList()
	skip_list.insert("Key1"    ,       3					)
	skip_list.insert("Key2"    ,       12					)
	skip_list.insert("Key3"    ,       41					)
	skip_list.insert("Key4"    ,       -19					)

	a      :		List[str]	=				skip_list.head
	a      :		int	=				{}
	while node.level != 0:
		a      :		Union[str, Any]	=				node.forward[0]
		a      :		List[Any]	=				node.value

	assert len(_lowercase					) == 4
	assert all_values["Key1"] == 3
	assert all_values["Key2"] == 12
	assert all_values["Key3"] == 41
	assert all_values["Key4"] == -19






def       _SCREAMING_SNAKE_CASE		(	) ->Optional[int]:






	'''simple docstring'''






	a      :		Dict	=				SkipList()
	skip_list.insert("Key1"    ,       10					)
	skip_list.insert("Key1"    ,       12					)

	skip_list.insert("Key5"    ,       7					)
	skip_list.insert("Key7"    ,       10					)
	skip_list.insert("Key10"    ,       5					)

	skip_list.insert("Key7"    ,       7					)
	skip_list.insert("Key5"    ,       5					)
	skip_list.insert("Key10"    ,       10					)

	a      :		int	=				skip_list.head
	a      :		Dict	=				{}
	while node.level != 0:
		a      :		int	=				node.forward[0]
		a      :		List[str]	=				node.value

	if len(_lowercase					) != 4:
		print()
	assert len(_lowercase					) == 4
	assert all_values["Key1"] == 12
	assert all_values["Key7"] == 7
	assert all_values["Key5"] == 5
	assert all_values["Key10"] == 10






def       _SCREAMING_SNAKE_CASE		(	) ->Dict:






	'''simple docstring'''






	a      :		Optional[Any]	=				SkipList()
	assert skip_list.find("Some key"					) is None






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






	'''simple docstring'''






	a      :		List[str]	=				SkipList()

	skip_list.insert("Key2"    ,       20					)
	assert skip_list.find("Key2"					) == 20

	skip_list.insert("Some Key"    ,       10					)
	skip_list.insert("Key2"    ,       8					)
	skip_list.insert("V"    ,       13					)

	assert skip_list.find("Y"					) is None
	assert skip_list.find("Key2"					) == 8
	assert skip_list.find("Some Key"					) == 10
	assert skip_list.find("V"					) == 13






def       _SCREAMING_SNAKE_CASE		(	) ->List[Any]:






	'''simple docstring'''






	a      :		Optional[int]	=				SkipList()
	skip_list.delete("Some key"					)

	assert len(skip_list.head.forward					) == 0






def       _SCREAMING_SNAKE_CASE		(	) ->List[Any]:






	'''simple docstring'''






	a      :		int	=				SkipList()

	skip_list.insert("Key1"    ,       12					)
	skip_list.insert("V"    ,       13					)
	skip_list.insert("X"    ,       14					)
	skip_list.insert("Key2"    ,       15					)

	skip_list.delete("V"					)
	skip_list.delete("Key2"					)

	assert skip_list.find("V"					) is None
	assert skip_list.find("Key2"					) is None






def       _SCREAMING_SNAKE_CASE		(	) ->int:






	'''simple docstring'''






	a      :		List[Any]	=				SkipList()

	skip_list.insert("Key1"    ,       12					)
	skip_list.insert("V"    ,       13					)
	skip_list.insert("X"    ,       14					)
	skip_list.insert("Key2"    ,       15					)

	skip_list.delete("V"					)
	assert skip_list.find("V"					) is None
	assert skip_list.find("X"					) == 14
	assert skip_list.find("Key1"					) == 12
	assert skip_list.find("Key2"					) == 15

	skip_list.delete("X"					)
	assert skip_list.find("V"					) is None
	assert skip_list.find("X"					) is None
	assert skip_list.find("Key1"					) == 12
	assert skip_list.find("Key2"					) == 15

	skip_list.delete("Key1"					)
	assert skip_list.find("V"					) is None
	assert skip_list.find("X"					) is None
	assert skip_list.find("Key1"					) is None
	assert skip_list.find("Key2"					) == 15

	skip_list.delete("Key2"					)
	assert skip_list.find("V"					) is None
	assert skip_list.find("X"					) is None
	assert skip_list.find("Key1"					) is None
	assert skip_list.find("Key2"					) is None






def       _SCREAMING_SNAKE_CASE		(	) ->Optional[int]:






	'''simple docstring'''






	a      :		List[Any]	=				SkipList()

	skip_list.insert("Key1"    ,       12					)
	skip_list.insert("V"    ,       13					)
	skip_list.insert("X"    ,       142					)
	skip_list.insert("Key2"    ,       15					)

	skip_list.delete("X"					)

	def traverse_keys(_lowercase						:    List[str]					):
		yield node.key
		for forward_node in node.forward:
			yield from traverse_keys(_lowercase					)

	assert len(set(traverse_keys(skip_list.head					)					)					) == 4






def       _SCREAMING_SNAKE_CASE		(	) ->Dict:






	'''simple docstring'''






	def is_sorted(_lowercase						:    List[str]					):
		return all(next_item >= item for item, next_item in zip(_lowercase    ,       lst[1:]					)					)

	a      :		Tuple	=				SkipList()
	for i in range(10					):
		skip_list.insert(_lowercase    ,       _lowercase					)
	assert is_sorted(list(_lowercase					)					)
	skip_list.delete(5					)
	skip_list.delete(8					)
	skip_list.delete(2					)
	assert is_sorted(list(_lowercase					)					)
	skip_list.insert(-12    ,       -12					)
	skip_list.insert(77    ,       77					)
	assert is_sorted(list(_lowercase					)					)






def       _SCREAMING_SNAKE_CASE		(	) ->Tuple:






	'''simple docstring'''






	for _ in range(100					):
		# Repeat test 100 times due to the probabilistic nature of skip list
		# random values == random bugs
		test_insert()
		test_insert_overrides_existing_value()

		test_searching_empty_list_returns_none()
		test_search()

		test_deleting_item_from_empty_list_do_nothing()
		test_deleted_items_are_not_founded_by_find_method()
		test_delete_removes_only_given_key()
		test_delete_doesnt_leave_dead_nodes()

		test_iter_always_yields_sorted_values()






def       _SCREAMING_SNAKE_CASE		(	) ->str:






	'''simple docstring'''






	a      :		List[Any]	=				SkipList()
	skip_list.insert(2    ,       "2"					)
	skip_list.insert(4    ,       "4"					)
	skip_list.insert(6    ,       "4"					)
	skip_list.insert(4    ,       "5"					)
	skip_list.insert(8    ,       "4"					)
	skip_list.insert(9    ,       "4"					)

	skip_list.delete(4					)

	print(_lowercase					)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()









"""simple docstring"""




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    List[Any]					) ->str:






	'''simple docstring'''






	a      :		Union[str, Any]	=				os.path.join(args.tf_model_dir    ,       "parameters.json"					)
	a      :		str	=				json.loads(open(_lowercase					).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"					):
		a      :		str	=				args.output + ".pt"
	a      :		Dict	=				OrderedDict()
	with tf.device("/CPU:0"					):
		a      :		Optional[int]	=				tf.train.load_checkpoint(args.tf_model_dir					)
		a      :		Optional[Any]	=				reader.get_variable_to_shape_map()
		for key_name in shapes.keys():
			a      :		Dict	=				reader.get_tensor(_lowercase					).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"					):
					a      :		Union[str, Any]	=				int(key_name[9]					)
				elif key_name.startswith("pasts/out"					):
					a      :		Optional[int]	=				8
				a      :		Dict	=				"model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
				a      :		int	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
				a      :		Dict	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/moe"					):
				a      :		List[str]	=				int(key_name[9:].split("/"					)[0]					)
				if key_name.endswith("/switch_gating/kernel"					):
					a      :		str	=				"model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
					a      :		Tuple	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[str]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/softmlp/kernel"					):
					a      :		Optional[int]	=				"model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
					a      :		Tuple	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/wo/kernel"					) or key_name.endswith("/wi/kernel"					):
					a      :		Any	=				key_name[-9:-7]
					for i in range(16					):
						a      :		List[Any]	=				"model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
						a      :		str	=				(
						    vnp[i].transpose([1, 0]					).copy()
						)  # In Mesh-Tensorflow, it is one array, so it is divided
						a      :		Dict	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/mlp"					):
				a      :		Union[str, Any]	=				int(key_name[9:].split("/"					)[0]					)
				if key_name.endswith("/p1/kernel"					):
					a      :		str	=				"model.blocks.%d.feed_forward.mlp.wi.weight" % player
					a      :		Dict	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		Optional[int]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/p1/bias"					):
					a      :		str	=				"model.blocks.%d.feed_forward.mlp.wi.bias" % player
					a      :		List[Any]	=				vnp.copy()  # same because it is one dimensional
					a      :		Tuple	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/p2/kernel"					):
					a      :		Union[str, Any]	=				"model.blocks.%d.feed_forward.mlp.wo.weight" % player
					a      :		Tuple	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/p2/bias"					):
					a      :		Dict	=				"model.blocks.%d.feed_forward.mlp.wo.bias" % player
					a      :		List[str]	=				vnp.copy()  # same because it is one dimensional
					a      :		str	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/ln"					):
				a      :		List[str]	=				int(key_name[8:].split("/"					)[0]					)
				if key_name.endswith("/b"					):
					a      :		Optional[Any]	=				"model.blocks.%d.feed_forward.norm.bias" % player
					a      :		Tuple	=				vnp.copy()  # same because it is one dimensional
					a      :		int	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/g"					):
					a      :		Optional[Any]	=				"model.blocks.%d.feed_forward.norm.weight" % player
					a      :		List[str]	=				vnp.copy()  # same because it is one dimensional
					a      :		Tuple	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/att"					):
				a      :		Optional[Any]	=				int(key_name[9:].split("/"					)[0]					)
				if key_name.endswith("/qkv/kernel"					):
					a      :		Union[str, Any]	=				vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
					a      :		List[str]	=				state[:, 0, :, :]
					a      :		Dict	=				state[:, 1, :, :]
					a      :		Union[str, Any]	=				state[:, 2, :, :]
					a      :		str	=				(
					    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
					a      :		List[str]	=				(
					    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
					a      :		Dict	=				(
					    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
					a      :		List[Any]	=				"model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
					a      :		Union[str, Any]	=				torch.tensor(_lowercase					)
					a      :		Any	=				"model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
					a      :		List[str]	=				torch.tensor(_lowercase					)
					a      :		Optional[Any]	=				"model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
					a      :		Optional[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/o/kernel"					):
					a      :		Any	=				"model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
					a      :		Optional[int]	=				(
					    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]					).transpose([1, 0]					).copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					a      :		Tuple	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/an"					):
				a      :		List[str]	=				int(key_name[8:].split("/"					)[0]					)
				if key_name.endswith("/b"					):
					a      :		Optional[int]	=				"model.blocks.%d.self_attn.norm.bias" % player
					a      :		Union[str, Any]	=				vnp.copy()  # same because it is one dimensional
					a      :		List[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/g"					):
					a      :		Any	=				"model.blocks.%d.self_attn.norm.weight" % player
					a      :		str	=				vnp.copy()  # same because it is one dimensional
					a      :		Any	=				torch.tensor(_lowercase					)
			elif (
			    key_name.startswith("model/wte"					)
			    or key_name.startswith("model/wpe"					)
			    or key_name.startswith("model/ete"					)
			):
				a      :		Optional[int]	=				{"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
				    key_name[-3:]
				]
				a      :		Tuple	=				"model.%s.weight" % nlayer
				a      :		Any	=				vnp.copy()  # same in embedded
				a      :		Tuple	=				torch.tensor(_lowercase					)
				if key_name.startswith("model/wte"					):
					a      :		Optional[int]	=				"lm_head.weight"
					a      :		Optional[int]	=				vnp.copy()  # same in embedded
					a      :		Optional[int]	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/wob"					):
				a      :		Optional[int]	=				"final_logits_bias"
				a      :		Optional[Any]	=				vnp.copy()  # same in embedded
				a      :		Optional[int]	=				state.reshape((1, -1)					)
				a      :		List[Any]	=				torch.tensor(_lowercase					)
			elif key_name == "model/dense/kernel":
				a      :		Optional[int]	=				"model.last_project.weight"
				a      :		Union[str, Any]	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
				a      :		List[Any]	=				torch.tensor(_lowercase					)
			elif key_name == "model/dense_1/bias":
				a      :		Dict	=				"model.last_project.bias"
				a      :		Optional[Any]	=				vnp.copy()  # same because it is one dimensional
				a      :		Any	=				torch.tensor(_lowercase					)
	torch.save(_lowercase    ,       args.output					)


if __name__ == "__main__":
		a						: Optional[int]     	=			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''')
		a						: Tuple     	=			parser.parse_args()
		convert_tf_gptsan_to_pt(args)


