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

import numpy as np
import tensorflow as tf
import torch



def  __lowerCamelCase       (      _UpperCamelCase  :  Tuple					):


      '''simple docstring'''


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


if __name__ == "__main__":
      lowercase__  :      int        =					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__  :      List[str]        =					parser.parse_args()
      convert_tf_gptsan_to_pt(args)






'''simple docstring'''
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


lowercase__  :      Dict        =					logging.get_logger(__name__)

lowercase__  :      List[Any]        =					"T5Config"







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	'''mt5'''
  lowerCAmelCase__										=	MTaConfig







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	'''mt5'''
  lowerCAmelCase__										=	MTaConfig







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	'''mt5'''
  lowerCAmelCase__										=	MTaConfig





