


'''simple docstring'''





import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
			import jax.numpy as jnp

			from transformers.models.roformer.modeling_flax_roformer import (
			    FlaxRoFormerForMaskedLM,
			    FlaxRoFormerForMultipleChoice,
			    FlaxRoFormerForQuestionAnswering,
			    FlaxRoFormerForSequenceClassification,
			    FlaxRoFormerForTokenClassification,
			    FlaxRoFormerModel,
			)




class    UpperCAmelCase					(       unittest.TestCase    ):




			def __init__(						self							:		Dict       ,       __magic_name__							:		str       ,       __magic_name__							:		List[str]=1_3       ,       __magic_name__							:		int=7       ,       __magic_name__							:		Dict=True       ,       __magic_name__							:		Optional[Any]=True       ,       __magic_name__							:		Optional[Any]=True       ,       __magic_name__							:		int=True       ,       __magic_name__							:		List[str]=9_9       ,       __magic_name__							:		Any=3_2       ,       __magic_name__							:		int=5       ,       __magic_name__							:		List[Any]=4       ,       __magic_name__							:		Optional[Any]=3_7       ,       __magic_name__							:		Dict="gelu"       ,       __magic_name__							:		List[str]=0.1       ,       __magic_name__							:		Optional[int]=0.1       ,       __magic_name__							:		Union[str, Any]=5_1_2       ,       __magic_name__							:		Optional[Any]=1_6       ,       __magic_name__							:		List[str]=2       ,       __magic_name__							:		Dict=0.02       ,       __magic_name__							:		Optional[int]=4       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					parent
						UpperCamelCase         =					batch_size
						UpperCamelCase         =					seq_length
						UpperCamelCase         =					is_training
						UpperCamelCase         =					use_attention_mask
						UpperCamelCase         =					use_token_type_ids
						UpperCamelCase         =					use_labels
						UpperCamelCase         =					vocab_size
						UpperCamelCase         =					hidden_size
						UpperCamelCase         =					num_hidden_layers
						UpperCamelCase         =					num_attention_heads
						UpperCamelCase         =					intermediate_size
						UpperCamelCase         =					hidden_act
						UpperCamelCase         =					hidden_dropout_prob
						UpperCamelCase         =					attention_probs_dropout_prob
						UpperCamelCase         =					max_position_embeddings
						UpperCamelCase         =					type_vocab_size
						UpperCamelCase         =					type_sequence_label_size
						UpperCamelCase         =					initializer_range
						UpperCamelCase         =					num_choices




			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







						UpperCamelCase         =					ids_tensor([self.batch_size, self.seq_length]       ,       self.vocab_size						)

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

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

						UpperCamelCase         =					RoFormerConfig(
						    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=__magic_name__       ,       initializer_range=self.initializer_range       ,       )

						return config, input_ids, token_type_ids, attention_mask





			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					self.prepare_config_and_inputs()
						UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					config_and_inputs
						UpperCamelCase         =					{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
						return config, inputs_dict




@require_flax
class    UpperCAmelCase					(       __snake_case      , unittest.TestCase    ):
			lowercase						= True

			lowercase						= (
			    (
			        FlaxRoFormerModel,
			        FlaxRoFormerForMaskedLM,
			        FlaxRoFormerForSequenceClassification,
			        FlaxRoFormerForTokenClassification,
			        FlaxRoFormerForMultipleChoice,
			        FlaxRoFormerForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)




			def   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					FlaxRoFormerModelTester(self						)





			@slow
			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						for model_class_name in self.all_model_classes:
									UpperCamelCase         =					model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""       ,       from_pt=__magic_name__						)
									UpperCamelCase         =					model(np.ones((1, 1)						)						)
									self.assertIsNotNone(__magic_name__						)




@require_flax
class    UpperCAmelCase					(       unittest.TestCase    ):




			@slow
			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""						)
						UpperCamelCase         =					jnp.array([[0, 1, 2, 3, 4, 5]]						)
						UpperCamelCase         =					model(__magic_name__						)[0]

						UpperCamelCase         =					5_0_0_0_0

						UpperCamelCase         =					(1, 6, vocab_size)
						self.assertEqual(output.shape       ,       __magic_name__						)

						UpperCamelCase         =					jnp.array(
						    [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]						)

						self.assertTrue(jnp.allclose(output[:, :3, :3]       ,       __magic_name__       ,       atol=1e-4						)						)








import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

__snake_case            =   3






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




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

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

			return public_key, private_key






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		int	):





			"""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()

			UpperCamelCase  ,  UpperCamelCase         =					generate_key(SCREAMING_SNAKE_CASE_	)
			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 							_lowercase    (		):





			"""simple docstring"""




			print("""Making key files..."""	)
			make_key_files("""elgamal"""       ,  2_048	)
			print("""Key files generation successful"""	)


if __name__ == "__main__":
			main()




