
'''simple docstring'''







import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch





def 					UpperCAmelCase_		(__a      :    Dict ):






   """simple docstring"""

   _a				:	List[Any]			=   os.path.join(args.tf_model_dir ,	'parameters.json' )
   _a				:	List[Any]			=   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' ):
      _a				:	Optional[int]			=   args.output + '.pt'
   _a				:	str			=   OrderedDict()
   with tf.device('/CPU:0' ):
      _a				:	Dict			=   tf.train.load_checkpoint(args.tf_model_dir )
      _a				:	Union[str, Any]			=   reader.get_variable_to_shape_map()
      for key_name in shapes.keys():
         _a				:	Optional[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' ):
               _a				:	int			=   int(key_name[9] )
            elif key_name.startswith('pasts/out' ):
               _a				:	Tuple			=   8
            _a				:	Tuple			=   'model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
            _a				:	Optional[Any]			=   vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
            _a				:	Union[str, Any]			=   torch.tensor(__a )
         elif key_name.startswith('model/moe' ):
            _a				:	str			=   int(key_name[9:].split('/' )[0] )
            if key_name.endswith('/switch_gating/kernel' ):
               _a				:	Union[str, Any]			=   'model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
               _a				:	Optional[int]			=   vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
               _a				:	List[str]			=   torch.tensor(__a )
            elif key_name.endswith('/softmlp/kernel' ):
               _a				:	List[str]			=   'model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
               _a				:	List[str]			=   vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
               _a				:	str			=   torch.tensor(__a )
            elif key_name.endswith('/wo/kernel' ) or key_name.endswith('/wi/kernel' ):
               _a				:	Union[str, Any]			=   key_name[-9:-7]
               for i in range(1_6 ):
                  _a				:	str			=   'model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
                  _a				:	List[Any]			=   (
                      vnp[i].transpose([1, 0] ).copy()
                  )  # In Mesh-Tensorflow, it is one array, so it is divided
                  _a				:	str			=   torch.tensor(__a )
         elif key_name.startswith('model/mlp' ):
            _a				:	Optional[Any]			=   int(key_name[9:].split('/' )[0] )
            if key_name.endswith('/p1/kernel' ):
               _a				:	List[Any]			=   'model.blocks.%d.feed_forward.mlp.wi.weight' % player
               _a				:	List[str]			=   vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
               _a				:	Tuple			=   torch.tensor(__a )
            elif key_name.endswith('/p1/bias' ):
               _a				:	List[str]			=   'model.blocks.%d.feed_forward.mlp.wi.bias' % player
               _a				:	Optional[int]			=   vnp.copy()  # same because it is one dimensional
               _a				:	int			=   torch.tensor(__a )
            elif key_name.endswith('/p2/kernel' ):
               _a				:	str			=   'model.blocks.%d.feed_forward.mlp.wo.weight' % player
               _a				:	Optional[Any]			=   vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
               _a				:	Union[str, Any]			=   torch.tensor(__a )
            elif key_name.endswith('/p2/bias' ):
               _a				:	Union[str, Any]			=   'model.blocks.%d.feed_forward.mlp.wo.bias' % player
               _a				:	Optional[int]			=   vnp.copy()  # same because it is one dimensional
               _a				:	int			=   torch.tensor(__a )
         elif key_name.startswith('model/ln' ):
            _a				:	List[Any]			=   int(key_name[8:].split('/' )[0] )
            if key_name.endswith('/b' ):
               _a				:	List[Any]			=   'model.blocks.%d.feed_forward.norm.bias' % player
               _a				:	Tuple			=   vnp.copy()  # same because it is one dimensional
               _a				:	Union[str, Any]			=   torch.tensor(__a )
            elif key_name.endswith('/g' ):
               _a				:	str			=   'model.blocks.%d.feed_forward.norm.weight' % player
               _a				:	Dict			=   vnp.copy()  # same because it is one dimensional
               _a				:	str			=   torch.tensor(__a )
         elif key_name.startswith('model/att' ):
            _a				:	List[Any]			=   int(key_name[9:].split('/' )[0] )
            if key_name.endswith('/qkv/kernel' ):
               _a				:	Dict			=   vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
               _a				:	str			=   state[:, 0, :, :]
               _a				:	Optional[Any]			=   state[:, 1, :, :]
               _a				:	List[str]			=   state[:, 2, :, :]
               _a				:	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
               _a				:	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
               _a				:	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
               _a				:	List[Any]			=   'model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
               _a				:	Any			=   torch.tensor(__a )
               _a				:	Optional[int]			=   'model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
               _a				:	str			=   torch.tensor(__a )
               _a				:	Optional[int]			=   'model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
               _a				:	List[str]			=   torch.tensor(__a )
            elif key_name.endswith('/o/kernel' ):
               _a				:	List[Any]			=   'model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
               _a				:	Any			=   (
                   vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
               )  # Mesh-Tensorflow is a diagonal matrix
               _a				:	List[str]			=   torch.tensor(__a )
         elif key_name.startswith('model/an' ):
            _a				:	List[Any]			=   int(key_name[8:].split('/' )[0] )
            if key_name.endswith('/b' ):
               _a				:	Optional[int]			=   'model.blocks.%d.self_attn.norm.bias' % player
               _a				:	Optional[int]			=   vnp.copy()  # same because it is one dimensional
               _a				:	Union[str, Any]			=   torch.tensor(__a )
            elif key_name.endswith('/g' ):
               _a				:	int			=   'model.blocks.%d.self_attn.norm.weight' % player
               _a				:	Optional[Any]			=   vnp.copy()  # same because it is one dimensional
               _a				:	Optional[int]			=   torch.tensor(__a )
         elif (
             key_name.startswith('model/wte' )
             or key_name.startswith('model/wpe' )
             or key_name.startswith('model/ete' )
         ):
            _a				:	Optional[Any]			=   {'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
                key_name[-3:]
            ]
            _a				:	List[str]			=   'model.%s.weight' % nlayer
            _a				:	Any			=   vnp.copy()  # same in embedded
            _a				:	int			=   torch.tensor(__a )
            if key_name.startswith('model/wte' ):
               _a				:	Tuple			=   'lm_head.weight'
               _a				:	List[Any]			=   vnp.copy()  # same in embedded
               _a				:	int			=   torch.tensor(__a )
         elif key_name.startswith('model/wob' ):
            _a				:	Union[str, Any]			=   'final_logits_bias'
            _a				:	List[Any]			=   vnp.copy()  # same in embedded
            _a				:	Any			=   state.reshape((1, -1) )
            _a				:	List[Any]			=   torch.tensor(__a )
         elif key_name == "model/dense/kernel":
            _a				:	Optional[Any]			=   'model.last_project.weight'
            _a				:	List[Any]			=   vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
            _a				:	Any			=   torch.tensor(__a )
         elif key_name == "model/dense_1/bias":
            _a				:	Union[str, Any]			=   'model.last_project.bias'
            _a				:	Tuple			=   vnp.copy()  # same because it is one dimensional
            _a				:	str			=   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'''







from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

__lowerCAmelCase         =    TypeVar("""T""")







class UpperCAmelCase__	( Generic[T]	):





  """simple docstring"""

  def __init__(		self      :     Tuple							,_a      :     T       ):


     '''simple docstring'''






     _a				:	List[str]			=   data
     _a				:	Node[T] | None			=   None


  def __str__(		self      :     Dict       ):


     '''simple docstring'''






     return F"""{self.data}"""







class UpperCAmelCase__	( Generic[T]	):





  """simple docstring"""

  def __init__(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Node[T] | None			=   None


  def __iter__(		self      :     str       ):


     '''simple docstring'''






     _a				:	Tuple			=   self.top
     while node:
        yield node.data
        _a				:	int			=   node.next


  def __str__(		self      :     str       ):


     '''simple docstring'''






     return "->".join([str(_a       ) for item in self]       )


  def __len__(		self      :     Optional[Any]       ):


     '''simple docstring'''






     return len(tuple(iter(self       )       )       )


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     return self.top is None


  def   __lowercase		(		self      :     List[Any]							,_a      :     T       ):


     '''simple docstring'''






     _a				:	int			=   Node(_a       )
     if not self.is_empty():
        _a				:	Optional[Any]			=   self.top
     _a				:	List[str]			=   node


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     if self.is_empty():
        raise IndexError('pop from empty stack'       )
     assert isinstance(self.top							,_a       )
     _a				:	List[Any]			=   self.top
     _a				:	int			=   self.top.next
     return pop_node.data


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     if self.is_empty():
        raise IndexError('peek from empty stack'       )

     assert self.top is not None
     return self.top.data


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   None


if __name__ == "__main__":
  from doctest import testmod

  testmod()



