


'''simple docstring'''







import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch







def _UpperCamelCase       (     __A    )							->		List[str]:


      '''simple docstring'''



      UpperCamelCase__										=				os.path.join(args.tf_model_dir      ,			"parameters.json"    )
      UpperCamelCase__										=				json.loads(open(__A    ).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(__A    ).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(__A    )
                  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(__A    )
                        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(__A    )
                        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(__A    )
                  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(__A    )
                        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(__A    )
                        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(__A    )
                        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(__A    )
                  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(__A    )
                        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(__A    )
                  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(__A    )
                              UpperCamelCase__										=				"model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
                              UpperCamelCase__										=				torch.tensor(__A    )
                              UpperCamelCase__										=				"model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
                              UpperCamelCase__										=				torch.tensor(__A    )
                        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(__A    )
                  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(__A    )
                        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(__A    )
                  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(__A    )
                        if key_name.startswith("model/wte"    ):
                              UpperCamelCase__										=				"lm_head.weight"
                              UpperCamelCase__										=				vnp.copy()  # same in embedded
                              UpperCamelCase__										=				torch.tensor(__A    )
                  elif key_name.startswith("model/wob"    ):
                        UpperCamelCase__										=				"final_logits_bias"
                        UpperCamelCase__										=				vnp.copy()  # same in embedded
                        UpperCamelCase__										=				state.reshape((1, -1)    )
                        UpperCamelCase__										=				torch.tensor(__A    )
                  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(__A    )
                  elif key_name == "model/dense_1/bias":
                        UpperCamelCase__										=				"model.last_project.bias"
                        UpperCamelCase__										=				vnp.copy()  # same because it is one dimensional
                        UpperCamelCase__										=				torch.tensor(__A    )
      torch.save(__A      ,			args.output    )


if __name__ == "__main__":
  a__  :					Dict        =       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')
  a__  :					int        =       parser.parse_args()
  convert_tf_gptsan_to_pt(args)






"""simple docstring"""






def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				if a < 0 or b < 0:
								raise ValueError('the value of both inputs must be positive'       )

				UpperCAmelCase__             =							str(bin(lowerCamelCase       )       )[2:]  # remove the leading "0b"
				UpperCAmelCase__             =							str(bin(lowerCamelCase       )       )[2:]  # remove the leading "0b"

				UpperCAmelCase__             =							max(len(lowerCamelCase       )					,			len(lowerCamelCase       )       )

				return "0b" + "".join(
				    str(int(char_a == '1' and char_b == '1'       )       )
				    for char_a, char_b in zip(a_binary.zfill(lowerCamelCase       )					,			b_binary.zfill(lowerCamelCase       )       )       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



