



import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch


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


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









def A						(       __UpperCamelCase					)						->      bool:
			return number & 1 == 0


if __name__ == "__main__":
							import doctest

							doctest.testmod()




