

"""simple docstring"""





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch




def   _A  (  UpperCamelCase_					:	Dict)     ->						Optional[int]:
   '''simple docstring'''


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


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







"""simple docstring"""





from __future__ import annotations

from typing import Any




class  _lowerCAmelCase  :







 """simple docstring"""







 def __init__(      self							:      Union[str, Any], UpperCAmelCase__							:      int     ):
    __lowercase								=						num_of_nodes
    __lowercase								=						[]
    __lowercase								=						{}


 def  _lowercase			(      self							:      Tuple, UpperCAmelCase__							:      int, UpperCAmelCase__							:      int, UpperCAmelCase__							:      int     ):
    self.m_edges.append([u_node, v_node, weight]     )


 def  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      int     ):

    if self.m_component[u_node] == u_node:
       return u_node
    return self.find_component(self.m_component[u_node]     )


 def  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      int     ):

    if self.m_component[u_node] != u_node:
       for k in self.m_component:
          __lowercase								=						self.find_component(UpperCAmelCase__     )


 def  _lowercase			(      self							:      str, UpperCAmelCase__							:      list[int], UpperCAmelCase__							:      int, UpperCAmelCase__							:      int     ):

    if component_size[u_node] <= component_size[v_node]:
       __lowercase								=						v_node
       component_size[v_node] += component_size[u_node]
       self.set_component(UpperCAmelCase__     )

    elif component_size[u_node] >= component_size[v_node]:
       __lowercase								=						self.find_component(UpperCAmelCase__     )
       component_size[u_node] += component_size[v_node]
       self.set_component(UpperCAmelCase__     )



 def  _lowercase			(      self							:      Tuple     ):
    __lowercase								=						[]
    __lowercase								=						0

    __lowercase								=						[-1] * self.m_num_of_nodes

    # A list of components (initialized to all of the nodes)
    for node in range(self.m_num_of_nodes     ):
       self.m_component.update({node: node}     )
       component_size.append(1     )

    __lowercase								=						self.m_num_of_nodes

    while num_of_components > 1:
       for edge in self.m_edges:
          __lowercase		,__lowercase		,__lowercase								=						edge

          __lowercase								=						self.m_component[u]
          __lowercase								=						self.m_component[v]

          if u_component != v_component:

             for component in (u_component, v_component):
                if (
                    minimum_weight_edge[component] == -1
                    or minimum_weight_edge[component][2] > w
                ):
                   __lowercase								=						[u, v, w]

       for edge in minimum_weight_edge:
          if isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
             __lowercase		,__lowercase		,__lowercase								=						edge

             __lowercase								=						self.m_component[u]
             __lowercase								=						self.m_component[v]

             if u_component != v_component:
                mst_weight += w
                self.union(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__     )
                print(F"""Added edge [{u} - {v}]\nAdded weight: {w}\n"""     )
                num_of_components -= 1

       __lowercase								=						[-1] * self.m_num_of_nodes
    print(F"""The total weight of the minimal spanning tree is: {mst_weight}"""     )







def   _A  (  )     ->						None:
   '''simple docstring'''




if __name__ == "__main__":
   import doctest

   doctest.testmod()




