






"""simple docstring"""




from __future__ import annotations






def        lowerCAmelCase (      UpperCamelCase_:							float     , UpperCamelCase_:							float     , UpperCamelCase_:							float     , )       ->							tuple[str, float]:







  '''simple docstring'''







  if (stress, tangential_force, area).count(0					) != 1:
    raise ValueError("You cannot supply more or less than 2 values"					)
  elif stress < 0:
    raise ValueError("Stress cannot be negative"					)
  elif tangential_force < 0:
    raise ValueError("Tangential Force cannot be negative"					)
  elif area < 0:
    raise ValueError("Area cannot be negative"					)
  elif stress == 0:
    return (
        "stress",
        tangential_force / area,
    )
  elif tangential_force == 0:
    return (
        "tangential_force",
        stress * area,
    )
  else:
    return (
        "area",
        tangential_force / stress,
    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()










"""simple docstring"""




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def        lowerCAmelCase (      UpperCamelCase_:							Dict					)       ->							Any:







  '''simple docstring'''







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


