


"""simple docstring"""




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch


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




def     lowercase			(SCREAMING_SNAKE_CASE_		:				int = 10_00  )   ->						int:
						SCREAMING_SNAKE_CASE													=					2**power
						SCREAMING_SNAKE_CASE													=					0
						while n:
												SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					r + n % 10, n // 10
						return r


if __name__ == "__main__":
	print(solution(int(str(input()).strip())))


