




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch

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


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












'''simple docstring'''

from ....utils import logging


a    :							Optional[int]										=    logging.get_logger(__name__)






class 		a							(  _lowerCamelCase	):


				def __init__(						self       :   int			,							lowercase_       :   Tuple			,							lowercase_       :   Any=None			,							lowercase_       :   List[Any]=2048   ):
									snake_case_		     =							config.__dict__
									snake_case_		     =							modal_hidden_size
									if num_labels:
														snake_case_		     =							num_labels

