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

import numpy as np
import tensorflow as tf
import torch





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


if __name__ == "__main__":
      lowercase_       :						str     					=	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"""
import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer





def    _lowerCAmelCase    (     lowerCamelCase__							:							str,						lowerCamelCase__							:							str,						**lowerCamelCase__							:							Optional[Any]				)  ->    Union[str, Any]:
  _SCREAMING_SNAKE_CASE   :				Dict							    = AutoConfig.from_pretrained(lowerCamelCase__,						**lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = AutoModelForSeqaSeqLM.from_config(lowerCamelCase__				)
  model.save_pretrained(lowerCamelCase__				)
  AutoTokenizer.from_pretrained(lowerCamelCase__				).save_pretrained(lowerCamelCase__				)
  return model


if __name__ == "__main__":
      fire.Fire(save_randomly_initialized_version)



