'''simple docstring'''





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch







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


if __name__ == "__main__":
      lowercase_     :   Tuple 				=				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')
      lowercase_     :   str 				=				parser.parse_args()
      convert_tf_gptsan_to_pt(args)


from ..utils import DummyObject, requires_backends






class 		_lowerCamelCase		(   metaclass=UpperCamelCase_    ):
    __a  =      ["torch", "scipy"]
    def __init__(  self , *lowerCAmelCase , **lowerCAmelCase   )       ->					Any:
     requires_backends(self , ['''torch''', '''scipy''']   )
    @classmethod
    def 						UpperCamelCase_				(  cls , *lowerCAmelCase , **lowerCAmelCase   )       ->					List[str]:
     requires_backends(cls , ['''torch''', '''scipy''']   )



    @classmethod
    def 						UpperCamelCase_				(  cls , *lowerCAmelCase , **lowerCAmelCase   )       ->					Tuple:
     requires_backends(cls , ['''torch''', '''scipy''']   )

