
import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



def 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[int]  )   ->			List[Any]:






						'''simple docstring'''

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


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






import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union




@dataclass
class   __lowercase						:
	_A       =   None
	_A       =   False
	_A       =   False
	_A       =   False
	_A       =   None
	_A       =   None
	_A       =   False
	_A       =   False
	_A       =   False
	_A       =   True
	_A       =   None
	_A       =   1
	_A       =   None
	_A       =   False
	_A       =   None
	_A       =   None






	def       _a(self				: str    ) ->			"DownloadConfig":
							return self.__class__(**{k: copy.deepcopy(snake_case    ) for k, v in self.__dict__.items()}    )




