






"""simple docstring"""


import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch




def       snake_case  (A_				:Optional[int]				):







  '''simple docstring'''

  a					:      Any    =       os.path.join(args.tf_model_dir     ,      'parameters.json'				)
  a					:      List[Any]    =       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'				):
    a					:      List[Any]    =       args.output + '.pt'
  a					:      Union[str, Any]    =       OrderedDict()
  with tf.device('/CPU:0'				):
    a					:      int    =       tf.train.load_checkpoint(args.tf_model_dir				)
    a					:      List[str]    =       reader.get_variable_to_shape_map()
    for key_name in shapes.keys():
      a					:      str    =       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'				):
          a					:      Any    =       int(key_name[9]				)
        elif key_name.startswith('pasts/out'				):
          a					:      List[str]    =       8
        a					:      Union[str, Any]    =       'model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
        a					:      List[str]    =       vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
        a					:      Any    =       torch.tensor(A_				)
      elif key_name.startswith('model/moe'				):
        a					:      List[str]    =       int(key_name[9:].split('/'				)[0]				)
        if key_name.endswith('/switch_gating/kernel'				):
          a					:      str    =       'model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
          a					:      str    =       vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
          a					:      List[str]    =       torch.tensor(A_				)
        elif key_name.endswith('/softmlp/kernel'				):
          a					:      Dict    =       'model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
          a					:      Any    =       vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
          a					:      Any    =       torch.tensor(A_				)
        elif key_name.endswith('/wo/kernel'				) or key_name.endswith('/wi/kernel'				):
          a					:      List[Any]    =       key_name[-9:-7]
          for i in range(1_6				):
            a					:      Tuple    =       'model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
            a					:      List[str]    =       (
                vnp[i].transpose([1, 0]				).copy()
            )  # In Mesh-Tensorflow, it is one array, so it is divided
            a					:      Optional[int]    =       torch.tensor(A_				)
      elif key_name.startswith('model/mlp'				):
        a					:      Tuple    =       int(key_name[9:].split('/'				)[0]				)
        if key_name.endswith('/p1/kernel'				):
          a					:      int    =       'model.blocks.%d.feed_forward.mlp.wi.weight' % player
          a					:      Optional[Any]    =       vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
          a					:      Tuple    =       torch.tensor(A_				)
        elif key_name.endswith('/p1/bias'				):
          a					:      Any    =       'model.blocks.%d.feed_forward.mlp.wi.bias' % player
          a					:      Tuple    =       vnp.copy()  # same because it is one dimensional
          a					:      Tuple    =       torch.tensor(A_				)
        elif key_name.endswith('/p2/kernel'				):
          a					:      Any    =       'model.blocks.%d.feed_forward.mlp.wo.weight' % player
          a					:      Optional[Any]    =       vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
          a					:      str    =       torch.tensor(A_				)
        elif key_name.endswith('/p2/bias'				):
          a					:      List[Any]    =       'model.blocks.%d.feed_forward.mlp.wo.bias' % player
          a					:      Union[str, Any]    =       vnp.copy()  # same because it is one dimensional
          a					:      Union[str, Any]    =       torch.tensor(A_				)
      elif key_name.startswith('model/ln'				):
        a					:      Tuple    =       int(key_name[8:].split('/'				)[0]				)
        if key_name.endswith('/b'				):
          a					:      Any    =       'model.blocks.%d.feed_forward.norm.bias' % player
          a					:      Any    =       vnp.copy()  # same because it is one dimensional
          a					:      List[Any]    =       torch.tensor(A_				)
        elif key_name.endswith('/g'				):
          a					:      List[str]    =       'model.blocks.%d.feed_forward.norm.weight' % player
          a					:      Tuple    =       vnp.copy()  # same because it is one dimensional
          a					:      List[Any]    =       torch.tensor(A_				)
      elif key_name.startswith('model/att'				):
        a					:      List[Any]    =       int(key_name[9:].split('/'				)[0]				)
        if key_name.endswith('/qkv/kernel'				):
          a					:      List[Any]    =       vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
          a					:      str    =       state[:, 0, :, :]
          a					:      List[str]    =       state[:, 1, :, :]
          a					:      Dict    =       state[:, 2, :, :]
          a					:      Dict    =       (
              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					:      Dict    =       (
              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					:      Optional[int]    =       (
              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					:      Optional[Any]    =       'model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
          a					:      int    =       torch.tensor(A_				)
          a					:      int    =       'model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
          a					:      str    =       torch.tensor(A_				)
          a					:      List[Any]    =       'model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
          a					:      str    =       torch.tensor(A_				)
        elif key_name.endswith('/o/kernel'				):
          a					:      Optional[int]    =       'model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
          a					:      List[str]    =       (
              vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]				).transpose([1, 0]				).copy()
          )  # Mesh-Tensorflow is a diagonal matrix
          a					:      List[str]    =       torch.tensor(A_				)
      elif key_name.startswith('model/an'				):
        a					:      Dict    =       int(key_name[8:].split('/'				)[0]				)
        if key_name.endswith('/b'				):
          a					:      Any    =       'model.blocks.%d.self_attn.norm.bias' % player
          a					:      int    =       vnp.copy()  # same because it is one dimensional
          a					:      int    =       torch.tensor(A_				)
        elif key_name.endswith('/g'				):
          a					:      Union[str, Any]    =       'model.blocks.%d.self_attn.norm.weight' % player
          a					:      Optional[int]    =       vnp.copy()  # same because it is one dimensional
          a					:      int    =       torch.tensor(A_				)
      elif (
          key_name.startswith('model/wte'				)
          or key_name.startswith('model/wpe'				)
          or key_name.startswith('model/ete'				)
      ):
        a					:      int    =       {'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
            key_name[-3:]
        ]
        a					:      Optional[int]    =       'model.%s.weight' % nlayer
        a					:      Any    =       vnp.copy()  # same in embedded
        a					:      Union[str, Any]    =       torch.tensor(A_				)
        if key_name.startswith('model/wte'				):
          a					:      List[Any]    =       'lm_head.weight'
          a					:      Optional[Any]    =       vnp.copy()  # same in embedded
          a					:      List[Any]    =       torch.tensor(A_				)
      elif key_name.startswith('model/wob'				):
        a					:      Dict    =       'final_logits_bias'
        a					:      Tuple    =       vnp.copy()  # same in embedded
        a					:      Tuple    =       state.reshape((1, -1)				)
        a					:      Union[str, Any]    =       torch.tensor(A_				)
      elif key_name == "model/dense/kernel":
        a					:      List[Any]    =       'model.last_project.weight'
        a					:      List[str]    =       vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
        a					:      Optional[Any]    =       torch.tensor(A_				)
      elif key_name == "model/dense_1/bias":
        a					:      Dict    =       'model.last_project.bias'
        a					:      Dict    =       vnp.copy()  # same because it is one dimensional
        a					:      List[str]    =       torch.tensor(A_				)
  torch.save(A_     ,      args.output				)


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












"""simple docstring"""


_UpperCamelCase    :   List[Any]   			=						8.31_44_62  # Unit - J mol-1 K-1




def       snake_case  (A_				:float     ,      A_				:float     ,      A_				:float				):







  '''simple docstring'''

  if moles < 0 or kelvin < 0 or volume < 0:
    raise ValueError('Invalid inputs. Enter positive value.'				)
  return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume




def       snake_case  (A_				:float     ,      A_				:float     ,      A_				:float				):







  '''simple docstring'''

  if moles < 0 or kelvin < 0 or pressure < 0:
    raise ValueError('Invalid inputs. Enter positive value.'				)
  return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
   from doctest import testmod

   testmod()




