






'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


			"""simple docstring"""




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


class        _UpperCAmelCase						:




    def __init__(			self  :       Optional[Any]  ,     a  :       Any			):






       '''simple docstring'''






       lowercase_	:    List[Any]           =     str(id_			)
       lowercase_	:    List[str]           =     None
       lowercase_	:    Tuple           =     None
       lowercase_	:    Dict           =     []
       lowercase_	:    Union[str, Any]           =     {}  # {vertex:distance}




    def __lt__(			self  :       Optional[Any]  ,     a  :       int			):






       '''simple docstring'''






       return self.key < other.key




    def __repr__(			self  :       Union[str, Any]			):






       '''simple docstring'''






       return self.id




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       Optional[int]			):






       '''simple docstring'''






       self.neighbors.append(a			)





    def 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       int  ,     a  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    int           =     weight




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   graph[a - 1].add_neighbor(graph[b - 1]							)
   graph[b - 1].add_neighbor(graph[a - 1]							)
   # add the edges:
   graph[a - 1].add_edge(graph[b - 1]						,		_UpperCamelCase							)
   graph[b - 1].add_edge(graph[a - 1]						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Any           =     []
   for u in graph:
      lowercase_	:    List[Any]           =     math.inf
      lowercase_	:    str           =     None
   lowercase_	:    Tuple           =     0
   lowercase_	:    Tuple           =     graph[:]
   while q:
      lowercase_	:    List[Any]           =     min(_UpperCamelCase							)
      q.remove(_UpperCamelCase							)
      for v in u.neighbors:
         if (v in q) and (u.edges[v.id] < v.key):
            lowercase_	:    Optional[int]           =     u
            lowercase_	:    Union[str, Any]           =     u.edges[v.id]
   for i in range(1						,		len(_UpperCamelCase							)							):
      a.append((int(graph[i].id							) + 1, int(graph[i].pi.id							) + 1)							)
   return a




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   for u in graph:
      lowercase_	:    str           =     math.inf
      lowercase_	:    int           =     None
   lowercase_	:    List[Any]           =     0

   lowercase_	:    str           =     list(_UpperCamelCase							)
   hq.heapify(_UpperCamelCase							)

   while h:
      lowercase_	:    List[Any]           =     hq.heappop(_UpperCamelCase							)
      for v in u.neighbors:
         if (v in h) and (u.edges[v.id] < v.key):
            lowercase_	:    str           =     u
            lowercase_	:    Optional[int]           =     u.edges[v.id]
            hq.heapify(_UpperCamelCase							)

   for i in range(1						,		len(_UpperCamelCase							)							):
      yield (int(graph[i].id							) + 1, int(graph[i].pi.id							) + 1)




def 		__SCREAMING_SNAKE_CASE					(  ):


   """simple docstring"""






if __name__ == "__main__":
 import doctest

 doctest.testmod()




