


'''simple docstring'''

import importlib.metadata
from typing import Union

from packaging.version import Version, parse

from .constants import STR_OPERATION_TO_FUNC


lowerCamelCase				:  List[str]    		=      parse(importlib.metadata.version('torch'))

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			List[str]:






       """simple docstring"""







       if operation not in STR_OPERATION_TO_FUNC.keys():
              raise ValueError(f"`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys()			)}, received {operation}"			)
       lowercase__      =					STR_OPERATION_TO_FUNC[operation]
       if isinstance(A    ,					A			):
              lowercase__      =					parse(importlib.metadata.version(A			)			)
       return operation(A    ,					parse(A			)			)

def _SCREAMING_SNAKE_CASE (A    ,					A			)			->			Optional[int]:






       """simple docstring"""







       return compare_versions(A    ,					A    ,					A			)








'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch

def _SCREAMING_SNAKE_CASE (A			)			->			Dict:






       """simple docstring"""







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


if __name__ == "__main__":
 lowerCamelCase				:  Optional[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')
 lowerCamelCase				:  Dict    		=      parser.parse_args()
 convert_tf_gptsan_to_pt(args)




