




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





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


if __name__ == "__main__":
		lowercase__							:	Optional[Any]											=		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")
		lowercase__							:	Any											=		parser.parse_args()
		convert_tf_gptsan_to_pt(args)






import numpy as np


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	return (2 / (1 + np.exp(-2 * vector				))) - 1


if __name__ == "__main__":
		import doctest

		doctest.testmod()
