"""simple docstring"""



import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()



def 					_lowerCamelCase(					a ,  a ,  a=None							):
       # set parameter of one layer
       assert torch_layer.weight.shape == weight.shape, F"{torch_layer} layer.weight does not match"
       __a     				=  nn.Parameter(a							)
       if bias is not None:
              assert torch_layer.bias.shape == bias.shape, F"{torch_layer} layer.bias does not match"
              __a     				=  nn.Parameter(a							)



def 					_lowerCamelCase(					a ,  a ,  a							):
       # set torch weights for 1-to-1 comparison
       __a     				=  np.asarray(weights[0]							)
       __a     				=  np.asarray(weights[1]							)
       __a     				=  np.asarray(weights[2]							)

       set_param(
           torch_layer.self_attention.query_key ,  torch.tensor(a							).transpose(1 ,  2							).contiguous().view(-1 ,  a							) ,  )
       set_param(
           torch_layer.self_attention.value ,  torch.tensor(a							).transpose(1 ,  2							).contiguous().view(-1 ,  a							) ,  )
       set_param(
           torch_layer.output.dense ,  torch.tensor(a							).view(-1 ,  a							).contiguous().transpose(0 ,  1							) ,  )



def 					_lowerCamelCase(					a ,  a ,  a							):
       # set torch weights for 1-to-1 comparison
       __a     				=  np.asarray(weights[0]							)
       __a     				=  np.asarray(weights[1]							)
       __a     				=  np.asarray(weights[2]							)
       __a     				=  np.asarray(weights[3]							)

       set_param(
           torch_layer.self_attention.query ,  torch.tensor(a							).transpose(1 ,  2							).contiguous().view(-1 ,  a							) ,  )
       set_param(
           torch_layer.self_attention.key ,  torch.tensor(a							).transpose(1 ,  2							).contiguous().view(-1 ,  a							) ,  )
       set_param(
           torch_layer.self_attention.value ,  torch.tensor(a							).transpose(1 ,  2							).contiguous().view(-1 ,  a							) ,  )
       set_param(
           torch_layer.output.dense ,  torch.tensor(a							).view(-1 ,  a							).contiguous().transpose(0 ,  1							) ,  )



def 					_lowerCamelCase(					a ,  a ,  a							):
       # layernorm 1
       __a     				=  weights[0][0][0]
       __a     				=  np.asarray(layer_norm_a[0]							)
       __a     				=  np.asarray(layer_norm_a[1]							)
       set_param(
           torch_block.attention.layer_norm ,  torch.tensor(a							) ,  torch.tensor(a							) ,  )

       # lsh weights + output
       __a     				=  weights[0][1]
       if len(a							) < 4:
              set_layer_weights_in_torch_lsh(a ,  torch_block.attention ,  a							)
       else:
              set_layer_weights_in_torch_local(a ,  torch_block.attention ,  a							)

       # intermediate weighs
       __a     				=  weights[2][0][1][2]

       # Chunked Feed Forward
       if len(a							) == 4:
              __a     				=  intermediate_weights[2]

       # layernorm 2
       __a     				=  np.asarray(intermediate_weights[0][0]							)
       __a     				=  np.asarray(intermediate_weights[0][1]							)
       set_param(
           torch_block.feed_forward.layer_norm ,  torch.tensor(a							) ,  torch.tensor(a							) ,  )

       # intermediate dense
       __a     				=  np.asarray(intermediate_weights[1][0]							)
       __a     				=  np.asarray(intermediate_weights[1][1]							)
       set_param(
           torch_block.feed_forward.dense.dense ,  torch.tensor(a							).transpose(0 ,  1							).contiguous() ,  torch.tensor(a							) ,  )

       # intermediate out
       __a     				=  np.asarray(intermediate_weights[4][0]							)
       __a     				=  np.asarray(intermediate_weights[4][1]							)
       set_param(
           torch_block.feed_forward.output.dense ,  torch.tensor(a							).transpose(0 ,  1							).contiguous() ,  torch.tensor(a							) ,  )



def 					_lowerCamelCase(					a ,  a ,  a							):
       # reformer model
       __a     				=  torch_model.reformer

       # word embeds
       __a     				=  np.asarray(weights[1]							)
       set_param(
           torch_model_reformer.embeddings.word_embeddings ,  torch.tensor(a							) ,  )

       if isinstance(weights[3] ,  a							):
              __a     				=  torch_model_reformer.embeddings.position_embeddings
              for emb_idx in range(len(position_embeddings.weights							)							):
                     __a     				=  np.asarray(weights[3][emb_idx][0]							)
                     assert (
                         position_embeddings.weights[emb_idx].shape == emb_weights.shape
                     ), F"{position_embeddings[emb_idx]} emb does not match"
                     __a     				=  nn.Parameter(torch.tensor(a							)							)

       __a     				=  weights[5]
       assert len(torch_model_reformer.encoder.layers							) * 4 == len(
           a							), "HF and trax model do not have the same number of layers"
       for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers							):
              __a     				=  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
              set_block_weights_in_torch(a ,  a ,  a							)

       # output layer norm
       __a     				=  np.asarray(weights[7][0]							)
       __a     				=  np.asarray(weights[7][1]							)
       set_param(
           torch_model_reformer.encoder.layer_norm ,  torch.tensor(a							) ,  torch.tensor(a							) ,  )

       # output embeddings
       __a     				=  np.asarray(weights[9][0]							)
       __a     				=  np.asarray(weights[9][1]							)
       set_param(
           torch_model.lm_head.decoder ,  torch.tensor(a							).transpose(0 ,  1							).contiguous() ,  torch.tensor(a							) ,  )



def 					_lowerCamelCase(					a ,  a ,  a							):
       # Initialise PyTorch model
       __a     				=  ReformerConfig.from_json_file(a							)
       print(F"Building PyTorch model from configuration: {config}"							)
       __a     				=  ReformerModelWithLMHead(a							)

       with open(a ,  "rb"							) as f:
              __a     				=  pickle.load(a							)["weights"]

       set_model_weights_in_torch(a ,  a ,  config.hidden_size							)

       # Save pytorch-model
       print(F"Save PyTorch model to {pytorch_dump_path}"							)
       torch.save(model.state_dict() ,  a							)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:List[str]			=	argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--trax_model_pkl_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
   )
   parser.add_argument(
       """--config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained Reformer model. \n"""
           """This specifies the model architecture."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   SCREAMING_SNAKE_CASE__:Tuple			=	parser.parse_args()
   convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)

"""simple docstring"""



import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

SCREAMING_SNAKE_CASE__:List[str]			=	3



def 					_lowerCamelCase(					a							):
       print("Generating primitive root of p"							)
       while True:
              __a     				=  random.randrange(3 ,  a							)
              if pow(a ,  2 ,  a							) == 1:
                     continue
              if pow(a ,  a ,  a							) == 1:
                     continue
              return g



def 					_lowerCamelCase(					a							):
       print("Generating prime p..."							)
       __a     				=  rabin_miller.generate_large_prime(a							)  # select large prime number.
       __a     				=  primitive_root(a							)  # one primitive root on modulo p.
       __a     				=  random.randrange(3 ,  a							)  # private_key -> have to be greater than 2 for safety.
       __a     				=  cryptomath.find_mod_inverse(pow(a ,  a ,  a							) ,  a							)

       __a     				=  (key_size, e_a, e_a, p)
       __a     				=  (key_size, d)

       return public_key, private_key



def 					_lowerCamelCase(					a ,  a							):
       if os.path.exists(F"{name}_pubkey.txt"							) or os.path.exists(F"{name}_privkey.txt"							):
              print("\nWARNING:"							)
              print(
                  F"\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"
                  "Use a different name or delete these files and re-run this program."							)
              sys.exit()

       __a				, __a     				=  generate_key(a							)
       print(F"\nWriting public key to file {name}_pubkey.txt..."							)
       with open(F"{name}_pubkey.txt" ,  "w"							) as fo:
              fo.write(F"{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"							)

       print(F"Writing private key to file {name}_privkey.txt..."							)
       with open(F"{name}_privkey.txt" ,  "w"							) as fo:
              fo.write(F"{private_key[0]},{private_key[1]}"							)



def 					_lowerCamelCase(					):
       print("Making key files..."							)
       make_key_files("elgamal" ,  2_0_4_8							)
       print("Key files generation successful"							)


if __name__ == "__main__":
   main()
