


import argparse
import json
import os
from collections import OrderedDict

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


if __name__ == "__main__":
       __UpperCamelCase				:  Any      					=							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')
       __UpperCamelCase				:  Optional[Any]      					=							parser.parse_args()
       convert_tf_gptsan_to_pt(args)



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase				:  Tuple      					=							logging.get_logger(__name__)





class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    A_  :     Union[str, Any]			= 'encoder-decoder'
    A_  :     Optional[int]			= True





    def __init__(	self				:   Dict       ,   **__snake_case				:   Union[str, Any]	):





       '''simple docstring'''
       super().__init__(**__snake_case	)
       assert (
           "encoder" in kwargs and "decoder" in kwargs
       ), "Config has to be initialized with encoder and decoder config"
       UpperCAmelCase_							:      int						=   kwargs.pop('''encoder'''	)
       UpperCAmelCase_							:      List[Any]						=   encoder_config.pop('''model_type'''	)
       UpperCAmelCase_							:      int						=   kwargs.pop('''decoder'''	)
       UpperCAmelCase_							:      Union[str, Any]						=   decoder_config.pop('''model_type'''	)

       from ..auto.configuration_auto import AutoConfig

       UpperCAmelCase_							:      Optional[int]						=   AutoConfig.for_model(__snake_case       ,   **__snake_case	)
       UpperCAmelCase_							:      Any						=   AutoConfig.for_model(__snake_case       ,   **__snake_case	)
       UpperCAmelCase_							:      List[Any]						=   True





    @classmethod
    def 					_lowerCamelCase (	cls				:   Optional[Any]       ,   __snake_case				:   PretrainedConfig       ,   __snake_case				:   PretrainedConfig       ,   **__snake_case				:   Union[str, Any]	):





       '''simple docstring'''
       logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''	)
       UpperCAmelCase_							:      Dict						=   True
       UpperCAmelCase_							:      str						=   True

       return cls(encoder=encoder_config.to_dict()       ,   decoder=decoder_config.to_dict()       ,   **__snake_case	)





    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   copy.deepcopy(self.__dict__	)
       UpperCAmelCase_							:      Tuple						=   self.encoder.to_dict()
       UpperCAmelCase_							:      Tuple						=   self.decoder.to_dict()
       UpperCAmelCase_							:      Tuple						=   self.__class__.model_type
       return output