




"""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					( _snake_case							: List[Any]							)   -> int:






      '''simple docstring'''


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


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







"""simple docstring"""

import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation






def 						_snake_case					( _snake_case							: int = 8							)   -> str:






      '''simple docstring'''


      _A        =       ascii_letters + digits + punctuation
      return "".join(secrets.choice(_snake_case							) for _ in range(_snake_case							)							)






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: int							)   -> str:






      '''simple docstring'''


      i -= len(_snake_case							)
      _A        =       i // 3
      _A        =       i % 3
      # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
      #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
      _A        =       (
          chars_incl
          + random(_snake_case    ,  quotient + remainder							)
          + random(_snake_case    ,  _snake_case							)
          + random(_snake_case    ,  _snake_case							)
      )
      _A        =       list(_snake_case							)
      shuffle(_snake_case							)
      return "".join(_snake_case							)

      # random is a generalised function for letters, characters and numbers






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: int							)   -> str:






      '''simple docstring'''


      return "".join(secrets.choice(_snake_case							) for _ in range(_snake_case							)							)






def 						_snake_case					( _snake_case							: Dict    ,  _snake_case							: Optional[int]							)   -> int:






      '''simple docstring'''


      pass  # Put your code here...






def 						_snake_case					( _snake_case							: Any    ,  _snake_case							: str							)   -> Dict:






      '''simple docstring'''


      pass  # Put your code here...






def 						_snake_case					( _snake_case							: Union[str, Any]    ,  _snake_case							: int							)   -> int:






      '''simple docstring'''


      pass  # Put your code here...






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: int = 8							)   -> bool:






      '''simple docstring'''


      if len(_snake_case							) < min_length:
            # Your Password must be at least 8 characters long
            return False

      _A        =       any(char in ascii_uppercase for char in password							)
      _A        =       any(char in ascii_lowercase for char in password							)
      _A        =       any(char in digits for char in password							)
      _A        =       any(char in punctuation for char in password							)

      return upper and lower and num and spec_char
      # Passwords should contain UPPERCASE, lowerase
      # numbers, and special characters






def 						_snake_case					( )   -> Optional[Any]:






      '''simple docstring'''


      _A        =       int(input('Please indicate the max length of your password: '							).strip()							)
      _A        =       input(
          'Please indicate the characters that must be in your password: '							).strip()
      print('Password generated:'    ,  password_generator(_snake_case							)							)
      print(
          'Alternative Password generated:'    ,  alternative_password_generator(_snake_case    ,  _snake_case							)    ,  )
      print('[If you are thinking of using this passsword, You better save it.]'							)


if __name__ == "__main__":
       main()

