'''simple docstring'''

import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

__a:			Tuple	       =       3
def       __UpperCamelCase	(       UpperCAmelCase					):
 print('''Generating primitive root of p'''					)
 while True:
  lowercase__    :   Optional[Any]						= random.randrange(3		,					UpperCAmelCase					)
  if pow(UpperCAmelCase		,					2		,					UpperCAmelCase					) == 1:
   continue
  if pow(UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					) == 1:
   continue
  return g
def       __UpperCamelCase	(       UpperCAmelCase					):
 print('''Generating prime p...'''					)
 lowercase__    :   List[Any]						= rabin_miller.generate_large_prime(UpperCAmelCase					)  # select large prime number.
 lowercase__    :   Optional[int]						= primitive_root(UpperCAmelCase					)  # one primitive root on modulo p.
 lowercase__    :   Dict						= random.randrange(3		,					UpperCAmelCase					)  # private_key -> have to be greater than 2 for safety.
 lowercase__    :   Optional[int]						= cryptomath.find_mod_inverse(pow(UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					)		,					UpperCAmelCase					)

 lowercase__    :   Union[str, Any]						= (key_size, e_a, e_a, p)
 lowercase__    :   List[Any]						= (key_size, d)

 return public_key, private_key
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 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()

 lowercase__			,							lowercase__    :   Union[str, Any]						= generate_key(UpperCAmelCase					)
 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       __UpperCamelCase	(       ):
 print('''Making key files...'''					)
 make_key_files('''elgamal'''		,					2048					)
 print('''Key files generation successful'''					)


if __name__ == "__main__":
  main()


'''simple docstring'''

import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase="attention"					):
 lowercase__    :   Dict						= params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
 lowercase__    :   Dict						= params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
 lowercase__    :   int						= params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
 lowercase__    :   Any						= params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
 return k, o, q, v
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase=False					):
 if split_mlp_wi:
  lowercase__    :   Tuple						= params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
  lowercase__    :   Tuple						= params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
  lowercase__    :   Dict						= (wi_a, wi_a)
 else:
  lowercase__    :   List[Any]						= params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

 lowercase__    :   int						= params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
 return wi, wo
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]
def       __UpperCamelCase	(       UpperCAmelCase		,					*, UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Union[str, Any]						= traverse_util.flatten_dict(variables['''target''']					)
 lowercase__    :   Any						= {'''/'''.join(UpperCAmelCase					): v for k, v in old.items()}

 # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
 lowercase__    :   List[str]						= '''encoder/layers_0/mlp/wi_0/kernel''' in old
 print('''Split MLP:'''		,					UpperCAmelCase					)

 lowercase__    :   Union[str, Any]						= collections.OrderedDict()

 # Shared embeddings.
 lowercase__    :   str						= old['''token_embedder/embedding''']

 # Encoder.
 for i in range(UpperCAmelCase					):
  # Block i, layer 0 (Self Attention).
  lowercase__    :   List[Any]						= tax_layer_norm_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''encoder'''		,					'''pre_attention_layer_norm'''					)
  lowercase__			,							lowercase__			,							lowercase__			,							lowercase__    :   Tuple						= tax_attention_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''encoder'''		,					'''attention'''					)
  lowercase__    :   Dict						= layer_norm
  lowercase__    :   Any						= k.T
  lowercase__    :   Optional[int]						= o.T
  lowercase__    :   Optional[int]						= q.T
  lowercase__    :   Any						= v.T

  # Block i, layer 1 (MLP).
  lowercase__    :   List[str]						= tax_layer_norm_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''encoder'''		,					'''pre_mlp_layer_norm'''					)
  lowercase__			,							lowercase__    :   List[Any]						= tax_mlp_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''encoder'''		,					UpperCAmelCase					)
  lowercase__    :   int						= layer_norm
  if split_mlp_wi:
   lowercase__    :   Union[str, Any]						= wi[0].T
   lowercase__    :   List[str]						= wi[1].T
  else:
   lowercase__    :   int						= wi.T
  lowercase__    :   List[str]						= wo.T

 lowercase__    :   Dict						= old[
     '''encoder/relpos_bias/rel_embedding'''
 ].T
 lowercase__    :   str						= old['''encoder/encoder_norm/scale''']

 if not is_encoder_only:
  # Decoder.
  for i in range(UpperCAmelCase					):
   # Block i, layer 0 (Self Attention).
   lowercase__    :   Optional[Any]						= tax_layer_norm_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''decoder'''		,					'''pre_self_attention_layer_norm'''					)
   lowercase__			,							lowercase__			,							lowercase__			,							lowercase__    :   Optional[Any]						= tax_attention_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''decoder'''		,					'''self_attention'''					)
   lowercase__    :   List[str]						= layer_norm
   lowercase__    :   List[Any]						= k.T
   lowercase__    :   List[Any]						= o.T
   lowercase__    :   List[str]						= q.T
   lowercase__    :   Optional[Any]						= v.T

   # Block i, layer 1 (Cross Attention).
   lowercase__    :   List[Any]						= tax_layer_norm_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''decoder'''		,					'''pre_cross_attention_layer_norm'''					)
   lowercase__			,							lowercase__			,							lowercase__			,							lowercase__    :   Any						= tax_attention_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''decoder'''		,					'''encoder_decoder_attention'''					)
   lowercase__    :   Any						= layer_norm
   lowercase__    :   Optional[int]						= k.T
   lowercase__    :   Optional[Any]						= o.T
   lowercase__    :   Tuple						= q.T
   lowercase__    :   List[Any]						= v.T

   # Block i, layer 2 (MLP).
   lowercase__    :   List[str]						= tax_layer_norm_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''decoder'''		,					'''pre_mlp_layer_norm'''					)
   lowercase__			,							lowercase__    :   List[Any]						= tax_mlp_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''decoder'''		,					UpperCAmelCase					)
   lowercase__    :   Optional[int]						= layer_norm
   if split_mlp_wi:
    lowercase__    :   List[str]						= wi[0].T
    lowercase__    :   str						= wi[1].T
   else:
    lowercase__    :   str						= wi.T
   lowercase__    :   Optional[Any]						= wo.T

  lowercase__    :   Any						= old['''decoder/decoder_norm/scale''']
  lowercase__    :   Optional[Any]						= old[
      '''decoder/relpos_bias/rel_embedding'''
  ].T

  # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
  if "decoder/logits_dense/kernel" in old:
   lowercase__    :   Dict						= old['''decoder/logits_dense/kernel'''].T

 return new
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   int						= collections.OrderedDict([(k, torch.from_numpy(v.copy()					)) for (k, v) in converted_params.items()]					)

 # Add what is missing.
 if "encoder.embed_tokens.weight" not in state_dict:
  lowercase__    :   Optional[int]						= state_dict['''shared.weight''']

 if not is_encoder_only:
  if "decoder.embed_tokens.weight" not in state_dict:
   lowercase__    :   List[Any]						= state_dict['''shared.weight''']

  if "lm_head.weight" not in state_dict:  # For old 1.0 models.
   print('''Using shared word embeddings as lm_head.'''					)
   lowercase__    :   Union[str, Any]						= state_dict['''shared.weight''']

 return state_dict
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   List[str]						= checkpoints.load_tax_checkpoint(UpperCAmelCase					)
 lowercase__    :   str						= convert_tax_to_pytorch(UpperCAmelCase		,					num_layers=config.num_layers		,					is_encoder_only=UpperCAmelCase					)
 lowercase__    :   List[Any]						= make_state_dict(UpperCAmelCase		,					UpperCAmelCase					)
 model.load_state_dict(UpperCAmelCase		,					strict=UpperCAmelCase					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase = False					):
 lowercase__    :   Dict						= TaConfig.from_json_file(UpperCAmelCase					)
 print(F"""Building PyTorch model from configuration: {config}"""					)
 # Non-v1.1 checkpoints could also use T5Model, but this works for all.
 # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
 if is_encoder_only:
  lowercase__    :   List[str]						= TaEncoderModel(UpperCAmelCase					)
 else:
  lowercase__    :   Tuple						= TaForConditionalGeneration(UpperCAmelCase					)

 # Load weights from tf checkpoint
 load_tax_weights_in_ta(UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					)

 # Save pytorch-model
 print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
 model.save_pretrained(UpperCAmelCase					)

 # Verify that we can load the checkpoint.
 model.from_pretrained(UpperCAmelCase					)
 print('''Done'''					)


if __name__ == "__main__":
  __a:			Optional[Any]	       =       argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
  # Required parameters
  parser.add_argument(
      """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  parser.add_argument(
      """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
  )
  __a:			Tuple	       =       parser.parse_args()
  convert_tax_checkpoint_to_pytorch(
      args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
  )

