





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

import numpy as np
import tensorflow as tf
import torch



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


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






"""simple docstring"""
def 					__lowerCamelCase       (							a_					:		str							)      ->				list:
      return [
          txt[:a] + txt[a].upper() + txt[a + 1 :]
          for a in range(len(a_							)							)
          if txt[a].isalpha()
      ]


if __name__ == "__main__":
  __import__("doctest").testmod()