'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch


def __a (	__lowerCamelCase			:						Union[str, Any]				)	->							Tuple:
      '''simple docstring'''


      lowercase_     =      os.path.join(args.tf_model_dir ,		"parameters.json"				)
      lowercase_     =      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"				):
            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(__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"				):
                              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(__lowerCamelCase				)
                  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(__lowerCamelCase				)
                        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(__lowerCamelCase				)
                        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(__lowerCamelCase				)
                  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(__lowerCamelCase				)
                        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(__lowerCamelCase				)
                        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(__lowerCamelCase				)
                        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(__lowerCamelCase				)
                  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(__lowerCamelCase				)
                        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(__lowerCamelCase				)
                  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(__lowerCamelCase				)
                              lowercase_     =      "model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
                              lowercase_     =      torch.tensor(__lowerCamelCase				)
                              lowercase_     =      "model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
                              lowercase_     =      torch.tensor(__lowerCamelCase				)
                        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(__lowerCamelCase				)
                  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(__lowerCamelCase				)
                        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(__lowerCamelCase				)
                  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(__lowerCamelCase				)
                        if key_name.startswith("model/wte"				):
                              lowercase_     =      "lm_head.weight"
                              lowercase_     =      vnp.copy()  # same in embedded
                              lowercase_     =      torch.tensor(__lowerCamelCase				)
                  elif key_name.startswith("model/wob"				):
                        lowercase_     =      "final_logits_bias"
                        lowercase_     =      vnp.copy()  # same in embedded
                        lowercase_     =      state.reshape((1, -1)				)
                        lowercase_     =      torch.tensor(__lowerCamelCase				)
                  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(__lowerCamelCase				)
                  elif key_name == "model/dense_1/bias":
                        lowercase_     =      "model.last_project.bias"
                        lowercase_     =      vnp.copy()  # same because it is one dimensional
                        lowercase_     =      torch.tensor(__lowerCamelCase				)
      torch.save(__lowerCamelCase ,		args.output				)


if __name__ == "__main__":
       lowerCAmelCase_ : List[str]						  =      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_ : str						  =      parser.parse_args()
       convert_tf_gptsan_to_pt(args)







'''simple docstring'''

import json
import sys


def __a (	__lowerCamelCase			:						Optional[int] ,		__lowerCamelCase			:						Union[str, Any]				)	->							Optional[Any]:
      '''simple docstring'''


      with open(__lowerCamelCase ,		encoding="utf-8"				) as f:
            lowercase_     =      json.load(__lowerCamelCase				)

      lowercase_     =      ["<details>", "<summary>Show updated benchmarks!</summary>", " "]

      for benchmark_name in sorted(__lowerCamelCase				):
            lowercase_     =      results[benchmark_name]

            lowercase_     =      benchmark_name.split("/"				)[-1]
            output_md.append(f'### Benchmark: {benchmark_file_name}'				)

            lowercase_     =      "| metric |"
            lowercase_     =      "|--------|"
            lowercase_     =      "| new / old (diff) |"
            for metric_name in sorted(__lowerCamelCase				):
                  lowercase_     =      benchmark_res[metric_name]
                  lowercase_     =      metric_vals["new"]
                  lowercase_     =      metric_vals.get("old" ,		__lowerCamelCase				)
                  lowercase_     =      metric_vals.get("diff" ,		__lowerCamelCase				)

                  lowercase_     =      f' {new_val:f}' if isinstance(__lowerCamelCase ,		(int, float)				) else "None"

                  if old_val is not None:
                        val_str += f' / {old_val:f}' if isinstance(__lowerCamelCase ,		(int, float)				) else "None"
                  if dif_val is not None:
                        val_str += f' ({dif_val:f})' if isinstance(__lowerCamelCase ,		(int, float)				) else "None"

                  title += " " + metric_name + " |"
                  lines += "---|"
                  value += val_str + " |"

            output_md += [title, lines, value, " "]

      output_md.append("</details>"				)

      with open(__lowerCamelCase ,		"w" ,		encoding="utf-8"				) as f:
            f.writelines("\n".join(__lowerCamelCase				)				)


if __name__ == "__main__":
       lowerCAmelCase_ : List[Any]						  =      sys.argv[1]
       lowerCAmelCase_ : Any						  =      sys.argv[2]

       format_json_to_md(input_json_file, output_md_file)






