



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


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









"""simple docstring"""





from __future__ import annotations

from decimal import Decimal

from numpy import array
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							Decimal

      # Check if the provided matrix has 2 rows and 2 columns
      # since this implementation only works for 2x2 matrices
      if len(lowerCAmelCase__   ) == 2 and len(matrix[0]   ) == 2 and len(matrix[1]   ) == 2:
            # Calculate the determinant of the matrix
            UpperCAmelCase_    				=							float(
                d(matrix[0][0]   ) * d(matrix[1][1]   ) - d(matrix[1][0]   ) * d(matrix[0][1]   )   )
            if determinant == 0:
                  raise ValueError("This matrix has no inverse."   )

            # Creates a copy of the matrix with swapped positions of the elements
            UpperCAmelCase_    				=							[[0.0, 0.0], [0.0, 0.0]]
            UpperCAmelCase_				,					UpperCAmelCase_    				=							matrix[1][1], matrix[0][0]
            UpperCAmelCase_				,					UpperCAmelCase_    				=							-matrix[1][0], -matrix[0][1]

            # Calculate the inverse of the matrix
            return [
                [(float(d(lowerCAmelCase__   )   ) / determinant) or 0.0 for n in row] for row in swapped_matrix
            ]
      elif (
          len(lowerCAmelCase__   ) == 3
          and len(matrix[0]   ) == 3
          and len(matrix[1]   ) == 3
          and len(matrix[2]   ) == 3
      ):
            # Calculate the determinant of the matrix using Sarrus rule
            UpperCAmelCase_    				=							float(
                (
                    (d(matrix[0][0]   ) * d(matrix[1][1]   ) * d(matrix[2][2]   ))
                    + (d(matrix[0][1]   ) * d(matrix[1][2]   ) * d(matrix[2][0]   ))
                    + (d(matrix[0][2]   ) * d(matrix[1][0]   ) * d(matrix[2][1]   ))
                )
                - (
                    (d(matrix[0][2]   ) * d(matrix[1][1]   ) * d(matrix[2][0]   ))
                    + (d(matrix[0][1]   ) * d(matrix[1][0]   ) * d(matrix[2][2]   ))
                    + (d(matrix[0][0]   ) * d(matrix[1][2]   ) * d(matrix[2][1]   ))
                )   )
            if determinant == 0:
                  raise ValueError("This matrix has no inverse."   )

            # Creating cofactor matrix
            UpperCAmelCase_    				=							[
                [d(0.0   ), d(0.0   ), d(0.0   )],
                [d(0.0   ), d(0.0   ), d(0.0   )],
                [d(0.0   ), d(0.0   ), d(0.0   )],
            ]
            UpperCAmelCase_    				=							(d(matrix[1][1]   ) * d(matrix[2][2]   )) - (
                d(matrix[1][2]   ) * d(matrix[2][1]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[1][0]   ) * d(matrix[2][2]   )) - (d(matrix[1][2]   ) * d(matrix[2][0]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[1][0]   ) * d(matrix[2][1]   )) - (
                d(matrix[1][1]   ) * d(matrix[2][0]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[0][1]   ) * d(matrix[2][2]   )) - (d(matrix[0][2]   ) * d(matrix[2][1]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[0][0]   ) * d(matrix[2][2]   )) - (
                d(matrix[0][2]   ) * d(matrix[2][0]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[0][0]   ) * d(matrix[2][1]   )) - (d(matrix[0][1]   ) * d(matrix[2][0]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[0][1]   ) * d(matrix[1][2]   )) - (
                d(matrix[0][2]   ) * d(matrix[1][1]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[0][0]   ) * d(matrix[1][2]   )) - (d(matrix[0][2]   ) * d(matrix[1][0]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[0][0]   ) * d(matrix[1][1]   )) - (
                d(matrix[0][1]   ) * d(matrix[1][0]   )
            )

            # Transpose the cofactor matrix (Adjoint matrix)
            UpperCAmelCase_    				=							array(lowerCAmelCase__   )
            for i in range(3   ):
                  for j in range(3   ):
                        UpperCAmelCase_    				=							cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
            UpperCAmelCase_    				=							array(lowerCAmelCase__   )
            for i in range(3   ):
                  for j in range(3   ):
                        inverse_matrix[i][j] /= d(lowerCAmelCase__   )

        # Calculate the inverse of the matrix
            return [[float(d(lowerCAmelCase__   )   ) or 0.0 for n in row] for row in inverse_matrix]
      raise ValueError("Please provide a matrix of size 2x2 or 3x3."   )




