
"""simple docstring"""



import argparse
import json
import os
from collections import OrderedDict

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


if __name__ == "__main__":
 UpperCAmelCase__          = 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__          = parser.parse_args()
 convert_tf_gptsan_to_pt(args)





"""simple docstring"""



import os
import time

import numpy as np
import onnxruntime as ort


UpperCAmelCase__          = '1'
UpperCAmelCase__          = '0'
UpperCAmelCase__          = '1'

UpperCAmelCase__          = ort.SessionOptions()
UpperCAmelCase__          = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('Create inference session...')
UpperCAmelCase__          = ['TensorrtExecutionProvider', 'CUDAExecutionProvider']
UpperCAmelCase__          = ort.InferenceSession('model.onnx', sess_options=sess_opt, providers=execution_provider)
UpperCAmelCase__          = ort.RunOptions()

UpperCAmelCase__          = 128
UpperCAmelCase__          = 1
UpperCAmelCase__          = np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase__          = np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase__          = np.ones((batch, sequence), dtype=np.intaa)

print('Warm up phase...')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('Start inference...')
UpperCAmelCase__          = time.time()
UpperCAmelCase__          = 2000
UpperCAmelCase__          = {}
for iter in range(max_iters):
 UpperCAmelCase__          = sess.run(
     None,
     {
         sess.get_inputs()[0].name: input_ids,
         sess.get_inputs()[1].name: attention_mask,
         sess.get_inputs()[2].name: token_type_ids,
     },
     run_options=run_opt,
 )
print('Average Inference Time = {:.3f} ms'.format((time.time() - start_time) * 1000 / max_iters))



