




'''simple docstring'''





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase_						:      str       =						logging.get_logger(__name__)

UpperCamelCase_						:      Optional[int]       =						{'''vocab_file''': '''spiece.model'''}

UpperCamelCase_						:      List[Any]       =						{
    '''vocab_file''': {
        '''bert_for_seq_generation''': (
            '''https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'''
        ),
    }
}

UpperCamelCase_						:      List[Any]       =						{'''bert_for_seq_generation''': 512}

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   List[str]       =						VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_							:   Union[str, Any]       =						PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_							:   List[str]       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE_							:   List[int]       =						[]
     SCREAMING_SNAKE_CASE_							:   int       =						["""input_ids""", """attention_mask"""]






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE="<s>" ,_SCREAMING_SNAKE_CASE="</s>" ,_SCREAMING_SNAKE_CASE="<unk>" ,_SCREAMING_SNAKE_CASE="<pad>" ,_SCREAMING_SNAKE_CASE="<::::>" ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,)							->      None:
          _snake_case							=				{} if sp_model_kwargs is None else sp_model_kwargs

          # Add extra_ids to the special token list
          super().__init__(
              bos_token=_SCREAMING_SNAKE_CASE ,eos_token=_SCREAMING_SNAKE_CASE ,unk_token=_SCREAMING_SNAKE_CASE ,pad_token=_SCREAMING_SNAKE_CASE ,sep_token=_SCREAMING_SNAKE_CASE ,sp_model_kwargs=self.sp_model_kwargs ,**_SCREAMING_SNAKE_CASE ,)

          _snake_case							=				vocab_file

          _snake_case							=				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
          self.sp_model.Load(_SCREAMING_SNAKE_CASE   )






     @property
     def     _lowercase     (			self   )							->      List[str]:
          return self.sp_model.get_piece_size()






     def     _lowercase     (			self   )							->      int:
          _snake_case							=				{self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE   ): i for i in range(self.vocab_size   )}
          vocab.update(self.added_tokens_encoder   )
          return vocab






     def __getstate__(			self   )							->      Any:
          _snake_case							=				self.__dict__.copy()
          _snake_case							=				None
          return state






     def __setstate__(			self ,_SCREAMING_SNAKE_CASE   )							->      str:
          _snake_case							=				d

          # for backward compatibility
          if not hasattr(self ,"sp_model_kwargs"   ):
               _snake_case							=				{}

          _snake_case							=				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
          self.sp_model.Load(self.vocab_file   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      List[str]:
          return self.sp_model.encode(_SCREAMING_SNAKE_CASE ,out_type=_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      Any:
          return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      Optional[int]:
          _snake_case							=				self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE   )
          return token






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      List[str]:
          _snake_case							=				[]
          _snake_case							=				""
          for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                    out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE   ) + token
                    _snake_case							=				[]
               else:
                    current_sub_tokens.append(_SCREAMING_SNAKE_CASE   )
          out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE   )
          return out_string.strip()






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      Tuple[str]:
          if not os.path.isdir(_SCREAMING_SNAKE_CASE   ):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
               return
          _snake_case							=				os.path.join(
              _SCREAMING_SNAKE_CASE ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

          if os.path.abspath(self.vocab_file   ) != os.path.abspath(_SCREAMING_SNAKE_CASE   ) and os.path.isfile(self.vocab_file   ):
               copyfile(self.vocab_file ,_SCREAMING_SNAKE_CASE   )
          elif not os.path.isfile(self.vocab_file   ):
               with open(_SCREAMING_SNAKE_CASE ,"wb"   ) as fi:
                    _snake_case							=				self.sp_model.serialized_model_proto()
                    fi.write(_SCREAMING_SNAKE_CASE   )

          return (out_vocab_file,)








'''simple docstring'''





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch





def      __a     (       _UpperCamelCase:   Tuple   )						->			Union[str, Any]:

     """simple docstring"""




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


if __name__ == "__main__":
   UpperCamelCase_						:      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''')
   UpperCamelCase_						:      Any       =						parser.parse_args()
   convert_tf_gptsan_to_pt(args)


