






import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def 				lowerCamelCase__				(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= os.path.join(args.tf_model_dir							,       'parameters.json')
			SCREAMING_SNAKE_CASE						= json.loads(open(_UpperCAmelCase).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(_UpperCAmelCase).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(_UpperCAmelCase)
									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(_UpperCAmelCase)
												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(_UpperCAmelCase)
												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(_UpperCAmelCase)
									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(_UpperCAmelCase)
												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(_UpperCAmelCase)
												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(_UpperCAmelCase)
												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(_UpperCAmelCase)
									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(_UpperCAmelCase)
												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(_UpperCAmelCase)
									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(_UpperCAmelCase)
															SCREAMING_SNAKE_CASE						= 'model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
															SCREAMING_SNAKE_CASE						= torch.tensor(_UpperCAmelCase)
															SCREAMING_SNAKE_CASE						= 'model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
															SCREAMING_SNAKE_CASE						= torch.tensor(_UpperCAmelCase)
												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(_UpperCAmelCase)
									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(_UpperCAmelCase)
												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(_UpperCAmelCase)
									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(_UpperCAmelCase)
												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(_UpperCAmelCase)
									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(_UpperCAmelCase)
									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(_UpperCAmelCase)
									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(_UpperCAmelCase)
			torch.save(_UpperCAmelCase							,       args.output)


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












import os

# Precomputes a list of the 100 first triangular numbers
a_		:						str             =    [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]






def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= os.path.dirname(os.path.realpath(_UpperCAmelCase))
			SCREAMING_SNAKE_CASE						= os.path.join(_UpperCAmelCase							,       'words.txt')

			SCREAMING_SNAKE_CASE						= ''
			with open(_UpperCAmelCase) as f:
						SCREAMING_SNAKE_CASE						= f.readline()

			SCREAMING_SNAKE_CASE						= [word.strip('"') for word in words.strip('\r\n').split(',')]
			SCREAMING_SNAKE_CASE						= [
			    word
			    for word in [sum(ord(_UpperCAmelCase) - 64 for x in word) for word in words]
			    if word in TRIANGULAR_NUMBERS
			]
			return len(_UpperCAmelCase)


if __name__ == "__main__":
				print(solution())




