

"""simple docstring"""





import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   import jax.numpy as jnp

   from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
       FlaxRobertaPreLayerNormForCausalLM,
       FlaxRobertaPreLayerNormForMaskedLM,
       FlaxRobertaPreLayerNormForMultipleChoice,
       FlaxRobertaPreLayerNormForQuestionAnswering,
       FlaxRobertaPreLayerNormForSequenceClassification,
       FlaxRobertaPreLayerNormForTokenClassification,
       FlaxRobertaPreLayerNormModel,
   )




class  _lowerCAmelCase  (     unittest.TestCase       ):







 """simple docstring"""







 def __init__(      self							:      Optional[Any], UpperCAmelCase__							:      List[Any], UpperCAmelCase__							:      List[Any]=1_3, UpperCAmelCase__							:      Tuple=7, UpperCAmelCase__							:      List[Any]=True, UpperCAmelCase__							:      str=True, UpperCAmelCase__							:      Union[str, Any]=True, UpperCAmelCase__							:      Dict=True, UpperCAmelCase__							:      Union[str, Any]=9_9, UpperCAmelCase__							:      Dict=3_2, UpperCAmelCase__							:      Tuple=5, UpperCAmelCase__							:      Union[str, Any]=4, UpperCAmelCase__							:      int=3_7, UpperCAmelCase__							:      Optional[int]="gelu", UpperCAmelCase__							:      Tuple=0.1, UpperCAmelCase__							:      Union[str, Any]=0.1, UpperCAmelCase__							:      Any=5_1_2, UpperCAmelCase__							:      Dict=1_6, UpperCAmelCase__							:      List[Any]=2, UpperCAmelCase__							:      str=0.02, UpperCAmelCase__							:      Tuple=4, ):
    __lowercase								=						parent
    __lowercase								=						batch_size
    __lowercase								=						seq_length
    __lowercase								=						is_training
    __lowercase								=						use_attention_mask
    __lowercase								=						use_token_type_ids
    __lowercase								=						use_labels
    __lowercase								=						vocab_size
    __lowercase								=						hidden_size
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						intermediate_size
    __lowercase								=						hidden_act
    __lowercase								=						hidden_dropout_prob
    __lowercase								=						attention_probs_dropout_prob
    __lowercase								=						max_position_embeddings
    __lowercase								=						type_vocab_size
    __lowercase								=						type_sequence_label_size
    __lowercase								=						initializer_range
    __lowercase								=						num_choices


 def  _lowercase			(      self							:      Tuple     ):
    __lowercase								=						ids_tensor([self.batch_size, self.seq_length], self.vocab_size     )

    __lowercase								=						None
    if self.use_attention_mask:
       __lowercase								=						random_attention_mask([self.batch_size, self.seq_length]     )

    __lowercase								=						None
    if self.use_token_type_ids:
       __lowercase								=						ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size     )

    __lowercase								=						RobertaPreLayerNormConfig(
        vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=_snake_case, initializer_range=self.initializer_range, )

    return config, input_ids, token_type_ids, attention_mask


 def  _lowercase			(      self							:      List[Any]     ):
    __lowercase								=						self.prepare_config_and_inputs()
    __lowercase								=						config_and_inputs
    __lowercase								=						{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
    return config, inputs_dict



 def  _lowercase			(      self							:      Union[str, Any]     ):
    __lowercase								=						self.prepare_config_and_inputs()
    __lowercase								=						config_and_inputs

    __lowercase								=						True
    __lowercase								=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
    __lowercase								=						ids_tensor([self.batch_size, self.seq_length], vocab_size=2     )

    return (
        config,
        input_ids,
        token_type_ids,
        encoder_hidden_states,
        encoder_attention_mask,
    )







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class  _lowerCAmelCase  (     __SCREAMING_SNAKE_CASE					,unittest.TestCase       ):







 """simple docstring"""







 __UpperCAmelCase     : Any       		=     True

 __UpperCAmelCase     : Any       		=     (
     (
         FlaxRobertaPreLayerNormModel,
         FlaxRobertaPreLayerNormForCausalLM,
         FlaxRobertaPreLayerNormForMaskedLM,
         FlaxRobertaPreLayerNormForSequenceClassification,
         FlaxRobertaPreLayerNormForTokenClassification,
         FlaxRobertaPreLayerNormForMultipleChoice,
         FlaxRobertaPreLayerNormForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )


 def  _lowercase			(      self							:      List[Any]     ):
    __lowercase								=						FlaxRobertaPreLayerNormModelTester(self     )



 @slow
 def  _lowercase			(      self							:      Any     ):
    for model_class_name in self.all_model_classes:
       __lowercase								=						model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40", from_pt=_snake_case     )
       __lowercase								=						model(np.ones((1, 1)     )     )
       self.assertIsNotNone(_snake_case     )







@require_flax
class  _lowerCAmelCase  (     unittest.TestCase       ):







 """simple docstring"""







 @slow
 def  _lowercase			(      self							:      int     ):
    __lowercase								=						FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40", from_pt=_snake_case     )

    __lowercase								=						np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]], dtype=jnp.intaa     )
    __lowercase								=						model(_snake_case     )[0]
    __lowercase								=						[1, 1_1, 5_0_2_6_5]
    self.assertEqual(list(output.shape     ), _snake_case     )
    # compare the actual values for a slice.
    __lowercase								=						np.array(
        [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]], dtype=np.floataa     )
    self.assertTrue(np.allclose(output[:, :3, :3], _snake_case, atol=1E-4     )     )



 @slow
 def  _lowercase			(      self							:      int     ):
    __lowercase								=						FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40", from_pt=_snake_case     )

    __lowercase								=						np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]], dtype=jnp.intaa     )
    __lowercase								=						model(_snake_case     )[0]
    # compare the actual values for a slice.
    __lowercase								=						np.array(
        [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]], dtype=np.floataa     )
    self.assertTrue(np.allclose(output[:, :3, :3], _snake_case, atol=1E-4     )     )


"""simple docstring"""





import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812




def   _A  (  )     ->						None:
   '''simple docstring'''


   print("Making key files...")
   make_key_files("rsa",      1024)
   print("Key files generation successful.")




def   _A  (  UpperCamelCase_					:	int)     ->						tuple[tuple[int, int], tuple[int, int]]:
   '''simple docstring'''


   print("Generating prime p...")
   __lowercase								=						rabinMiller.generate_large_prime(UpperCamelCase_)
   print("Generating prime q...")
   __lowercase								=						rabinMiller.generate_large_prime(UpperCamelCase_)
   __lowercase								=						p * q

   print("Generating e that is relatively prime to (p - 1) * (q - 1)...")
   while True:
      __lowercase								=						random.randrange(2 ** (key_size - 1),      2 ** (key_size))
      if cryptoMath.gcd(UpperCamelCase_,      (p - 1) * (q - 1)) == 1:
         break

   print("Calculating d that is mod inverse of e...")
   __lowercase								=						cryptoMath.find_mod_inverse(UpperCamelCase_,      (p - 1) * (q - 1))

   __lowercase								=						(n, e)
   __lowercase								=						(n, d)
   return (public_key, private_key)




def   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	int)     ->						None:
   '''simple docstring'''


   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								=						generate_key(UpperCamelCase_)
   print(F"""\nWriting public key to file {name}_pubkey.txt...""")
   with open(F"""{name}_pubkey.txt""",      "w") as out_file:
      out_file.write(F"""{key_size},{public_key[0]},{public_key[1]}""")

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


if __name__ == "__main__":
   main()




