

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					int:
	__lowerCamelCase    :    int 	=			os.path.join(args.tf_model_dir     ,			'parameters.json'      )
	__lowerCamelCase    :    List[str] 	=			json.loads(open(lowerCamelCase__      ).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    :    str 	=			args.output + '.pt'
	__lowerCamelCase    :    List[Any] 	=			OrderedDict()
	with tf.device('/CPU:0'      ):
		__lowerCamelCase    :    List[Any] 	=			tf.train.load_checkpoint(args.tf_model_dir      )
		__lowerCamelCase    :    Dict 	=			reader.get_variable_to_shape_map()
		for key_name in shapes.keys():
			__lowerCamelCase    :    Any 	=			reader.get_tensor(lowerCamelCase__      ).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    :    Optional[Any] 	=			int(key_name[9]      )
				elif key_name.startswith('pasts/out'      ):
					__lowerCamelCase    :    Dict 	=			8
				__lowerCamelCase    :    Optional[int] 	=			'model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
				__lowerCamelCase    :    Union[str, Any] 	=			vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
				__lowerCamelCase    :    Optional[int] 	=			torch.tensor(lowerCamelCase__      )
			elif key_name.startswith('model/moe'      ):
				__lowerCamelCase    :    Optional[Any] 	=			int(key_name[9:].split('/'      )[0]      )
				if key_name.endswith('/switch_gating/kernel'      ):
					__lowerCamelCase    :    Union[str, Any] 	=			'model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
					__lowerCamelCase    :    Tuple 	=			vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
					__lowerCamelCase    :    Optional[Any] 	=			torch.tensor(lowerCamelCase__      )
				elif key_name.endswith('/softmlp/kernel'      ):
					__lowerCamelCase    :    Optional[Any] 	=			'model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
					__lowerCamelCase    :    Union[str, Any] 	=			vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
					__lowerCamelCase    :    Any 	=			torch.tensor(lowerCamelCase__      )
				elif key_name.endswith('/wo/kernel'      ) or key_name.endswith('/wi/kernel'      ):
					__lowerCamelCase    :    Dict 	=			key_name[-9:-7]
					for i in range(1_6      ):
						__lowerCamelCase    :    Union[str, Any] 	=			'model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
						__lowerCamelCase    :    str 	=			(
						    vnp[i].transpose([1, 0]      ).copy()
						)  # In Mesh-Tensorflow, it is one array, so it is divided
						__lowerCamelCase    :    Optional[int] 	=			torch.tensor(lowerCamelCase__      )
			elif key_name.startswith('model/mlp'      ):
				__lowerCamelCase    :    Tuple 	=			int(key_name[9:].split('/'      )[0]      )
				if key_name.endswith('/p1/kernel'      ):
					__lowerCamelCase    :    Optional[Any] 	=			'model.blocks.%d.feed_forward.mlp.wi.weight' % player
					__lowerCamelCase    :    Any 	=			vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
					__lowerCamelCase    :    int 	=			torch.tensor(lowerCamelCase__      )
				elif key_name.endswith('/p1/bias'      ):
					__lowerCamelCase    :    Tuple 	=			'model.blocks.%d.feed_forward.mlp.wi.bias' % player
					__lowerCamelCase    :    Union[str, Any] 	=			vnp.copy()  # same because it is one dimensional
					__lowerCamelCase    :    Union[str, Any] 	=			torch.tensor(lowerCamelCase__      )
				elif key_name.endswith('/p2/kernel'      ):
					__lowerCamelCase    :    str 	=			'model.blocks.%d.feed_forward.mlp.wo.weight' % player
					__lowerCamelCase    :    Tuple 	=			vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
					__lowerCamelCase    :    str 	=			torch.tensor(lowerCamelCase__      )
				elif key_name.endswith('/p2/bias'      ):
					__lowerCamelCase    :    Any 	=			'model.blocks.%d.feed_forward.mlp.wo.bias' % player
					__lowerCamelCase    :    int 	=			vnp.copy()  # same because it is one dimensional
					__lowerCamelCase    :    List[Any] 	=			torch.tensor(lowerCamelCase__      )
			elif key_name.startswith('model/ln'      ):
				__lowerCamelCase    :    List[Any] 	=			int(key_name[8:].split('/'      )[0]      )
				if key_name.endswith('/b'      ):
					__lowerCamelCase    :    Tuple 	=			'model.blocks.%d.feed_forward.norm.bias' % player
					__lowerCamelCase    :    Any 	=			vnp.copy()  # same because it is one dimensional
					__lowerCamelCase    :    int 	=			torch.tensor(lowerCamelCase__      )
				elif key_name.endswith('/g'      ):
					__lowerCamelCase    :    Dict 	=			'model.blocks.%d.feed_forward.norm.weight' % player
					__lowerCamelCase    :    Tuple 	=			vnp.copy()  # same because it is one dimensional
					__lowerCamelCase    :    Any 	=			torch.tensor(lowerCamelCase__      )
			elif key_name.startswith('model/att'      ):
				__lowerCamelCase    :    List[str] 	=			int(key_name[9:].split('/'      )[0]      )
				if key_name.endswith('/qkv/kernel'      ):
					__lowerCamelCase    :    str 	=			vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
					__lowerCamelCase    :    Tuple 	=			state[:, 0, :, :]
					__lowerCamelCase    :    int 	=			state[:, 1, :, :]
					__lowerCamelCase    :    Optional[Any] 	=			state[:, 2, :, :]
					__lowerCamelCase    :    Union[str, Any] 	=			(
					    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    :    Optional[int] 	=			(
					    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    :    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
					__lowerCamelCase    :    Any 	=			'model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
					__lowerCamelCase    :    Optional[int] 	=			torch.tensor(lowerCamelCase__      )
					__lowerCamelCase    :    int 	=			'model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
					__lowerCamelCase    :    int 	=			torch.tensor(lowerCamelCase__      )
					__lowerCamelCase    :    Tuple 	=			'model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
					__lowerCamelCase    :    List[str] 	=			torch.tensor(lowerCamelCase__      )
				elif key_name.endswith('/o/kernel'      ):
					__lowerCamelCase    :    int 	=			'model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
					__lowerCamelCase    :    int 	=			(
					    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]      ).transpose([1, 0]      ).copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					__lowerCamelCase    :    List[Any] 	=			torch.tensor(lowerCamelCase__      )
			elif key_name.startswith('model/an'      ):
				__lowerCamelCase    :    List[str] 	=			int(key_name[8:].split('/'      )[0]      )
				if key_name.endswith('/b'      ):
					__lowerCamelCase    :    str 	=			'model.blocks.%d.self_attn.norm.bias' % player
					__lowerCamelCase    :    Dict 	=			vnp.copy()  # same because it is one dimensional
					__lowerCamelCase    :    Optional[Any] 	=			torch.tensor(lowerCamelCase__      )
				elif key_name.endswith('/g'      ):
					__lowerCamelCase    :    List[str] 	=			'model.blocks.%d.self_attn.norm.weight' % player
					__lowerCamelCase    :    int 	=			vnp.copy()  # same because it is one dimensional
					__lowerCamelCase    :    str 	=			torch.tensor(lowerCamelCase__      )
			elif (
			    key_name.startswith('model/wte'      )
			    or key_name.startswith('model/wpe'      )
			    or key_name.startswith('model/ete'      )
			):
				__lowerCamelCase    :    Union[str, Any] 	=			{'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
				    key_name[-3:]
				]
				__lowerCamelCase    :    Tuple 	=			'model.%s.weight' % nlayer
				__lowerCamelCase    :    str 	=			vnp.copy()  # same in embedded
				__lowerCamelCase    :    Dict 	=			torch.tensor(lowerCamelCase__      )
				if key_name.startswith('model/wte'      ):
					__lowerCamelCase    :    Optional[int] 	=			'lm_head.weight'
					__lowerCamelCase    :    Optional[int] 	=			vnp.copy()  # same in embedded
					__lowerCamelCase    :    List[str] 	=			torch.tensor(lowerCamelCase__      )
			elif key_name.startswith('model/wob'      ):
				__lowerCamelCase    :    Optional[int] 	=			'final_logits_bias'
				__lowerCamelCase    :    Any 	=			vnp.copy()  # same in embedded
				__lowerCamelCase    :    Any 	=			state.reshape((1, -1)      )
				__lowerCamelCase    :    List[str] 	=			torch.tensor(lowerCamelCase__      )
			elif key_name == "model/dense/kernel":
				__lowerCamelCase    :    Dict 	=			'model.last_project.weight'
				__lowerCamelCase    :    Any 	=			vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
				__lowerCamelCase    :    Optional[Any] 	=			torch.tensor(lowerCamelCase__      )
			elif key_name == "model/dense_1/bias":
				__lowerCamelCase    :    List[Any] 	=			'model.last_project.bias'
				__lowerCamelCase    :    Union[str, Any] 	=			vnp.copy()  # same because it is one dimensional
				__lowerCamelCase    :    Optional[int] 	=			torch.tensor(lowerCamelCase__      )
	torch.save(lowerCamelCase__     ,			args.output      )


if __name__ == "__main__":
					a		=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		=parser.parse_args()
					convert_tf_gptsan_to_pt(args)







from __future__ import annotations

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

a		=TypeVar("""T""")
a		=TypeVar("""U""")






class 	A_		(	Generic[T, U]    ):
							def __init__(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      T | None       ,SCREAMING_SNAKE_CASE__		:      U | None):
								__lowerCamelCase    :    Optional[Any] 	=			key
								__lowerCamelCase    :    Tuple 	=			val
								__lowerCamelCase    :    DoubleLinkedListNode[T, U] | None 	=			None
								__lowerCamelCase    :    DoubleLinkedListNode[T, U] | None 	=			None


							def __repr__(		self		:      Optional[int]):
								return (
								    F"Node: key: {self.key}, val: {self.val}, "
								    F"has next: {bool(self.next)}, has prev: {bool(self.prev)}"
								)






class 	A_		(	Generic[T, U]    ):
							def __init__(		self		:      List[Any]):
								__lowerCamelCase    :    DoubleLinkedListNode[T, U] 	=			DoubleLinkedListNode(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    DoubleLinkedListNode[T, U] 	=			DoubleLinkedListNode(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase	,			__lowerCamelCase    :    List[Any] 	=			self.rear, self.head
							def __repr__(		self		:      Optional[int]):
								__lowerCamelCase    :    int 	=			['DoubleLinkedList']
								__lowerCamelCase    :    Tuple 	=			self.head
								while node.next is not None:
									rep.append(str(SCREAMING_SNAKE_CASE__))
									__lowerCamelCase    :    Union[str, Any] 	=			node.next
								rep.append(str(self.rear))
								return ",\n    ".join(SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      DoubleLinkedListNode[T, U]):
								__lowerCamelCase    :    str 	=			self.rear.prev

								# All nodes other than self.head are guaranteed to have non-None previous
								assert previous is not None

								__lowerCamelCase    :    Any 	=			node
								__lowerCamelCase    :    str 	=			previous
								__lowerCamelCase    :    str 	=			node
								__lowerCamelCase    :    List[str] 	=			self.rear


							def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      DoubleLinkedListNode[T, U]):

								if node.prev is None or node.next is None:
									return None

								__lowerCamelCase    :    Union[str, Any] 	=			node.next
								__lowerCamelCase    :    Dict 	=			node.prev
								__lowerCamelCase    :    Optional[int] 	=			None
								__lowerCamelCase    :    List[str] 	=			None
								return node






class 	A_		(	Generic[T, U]    ):
							_UpperCAmelCase   :  dict[Callable[[T], U], LRUCache[T, U]]										=				{}
							def __init__(		self		:      str       ,SCREAMING_SNAKE_CASE__		:      int):
								__lowerCamelCase    :    DoubleLinkedList[T, U] 	=			DoubleLinkedList()
								__lowerCamelCase    :    List[Any] 	=			capacity
								__lowerCamelCase    :    Optional[int] 	=			0
								__lowerCamelCase    :    int 	=			0
								__lowerCamelCase    :    Optional[Any] 	=			0
								__lowerCamelCase    :    dict[T, DoubleLinkedListNode[T, U]] 	=			{}
							def __repr__(		self		:      Optional[Any]):
								return (
								    F"CacheInfo(hits={self.hits}, misses={self.miss}, "
								    F"capacity={self.capacity}, current size={self.num_keys})"
								)
							def __contains__(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      T):
								return key in self.cache
							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      T):
								# Note: pythonic interface would throw KeyError rather than return None

								if key in self.cache:
									self.hits += 1
									__lowerCamelCase    :    DoubleLinkedListNode[T, U] 	=			self.cache[key]
									__lowerCamelCase    :    List[str] 	=			self.list.remove(self.cache[key])
									assert node == value_node

									# node is guaranteed not None because it is in self.cache
									assert node is not None
									self.list.add(SCREAMING_SNAKE_CASE__)
									return node.val
								self.miss += 1
								return None
							def    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      T       ,SCREAMING_SNAKE_CASE__		:      U):

								if key not in self.cache:
									if self.num_keys >= self.capacity:
										# delete first node (oldest) when over capacity
										__lowerCamelCase    :    Optional[Any] 	=			self.list.head.next

										# guaranteed to have a non-None first node when num_keys > 0
										# explain to type checker via assertions
										assert first_node is not None
										assert first_node.key is not None
										assert (
										    self.list.remove(SCREAMING_SNAKE_CASE__) is not None
										)  # node guaranteed to be in list assert node.key is not None

										del self.cache[first_node.key]
										self.num_keys -= 1
									__lowerCamelCase    :    List[Any] 	=			DoubleLinkedListNode(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
									self.list.add(self.cache[key])
									self.num_keys += 1

								else:
									# bump node to the end of the list, update value
									__lowerCamelCase    :    List[str] 	=			self.list.remove(self.cache[key])
									assert node is not None  # node guaranteed to be in list
									__lowerCamelCase    :    List[Any] 	=			value
									self.list.add(SCREAMING_SNAKE_CASE__)


							@classmethod
							def    lowerCAmelCase					(		cls		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      int = 1_2_8):

								def cache_decorator_inner(SCREAMING_SNAKE_CASE__		:      Callable[[T], U]) -> Callable[..., U]:
									def cache_decorator_wrapper(*SCREAMING_SNAKE_CASE__		:      T) -> U:
										if func not in cls.decorator_function_to_instance_map:
											__lowerCamelCase    :    str 	=			LRUCache(SCREAMING_SNAKE_CASE__)

										__lowerCamelCase    :    Dict 	=			cls.decorator_function_to_instance_map[func].get(args[0])
										if result is None:
											__lowerCamelCase    :    str 	=			func(*SCREAMING_SNAKE_CASE__)
											cls.decorator_function_to_instance_map[func].put(args[0]       ,SCREAMING_SNAKE_CASE__)
										return result

									def cache_info() -> LRUCache[T, U]:
										return cls.decorator_function_to_instance_map[func]

									setattr(SCREAMING_SNAKE_CASE__       ,'cache_info'       ,SCREAMING_SNAKE_CASE__)  # noqa: B010

									return cache_decorator_wrapper

								return cache_decorator_inner


if __name__ == "__main__":
					import doctest

					doctest.testmod()




