


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 							A   (	unittest.TestCase							):
					'''simple docstring'''






					def __init__(self   :						str				, _UpperCAmelCase   :						Optional[int]				, _UpperCAmelCase   :						List[Any]=13				, _UpperCAmelCase   :						List[str]=7				, _UpperCAmelCase   :						str=True				, _UpperCAmelCase   :						Optional[Any]=True				, _UpperCAmelCase   :						Dict=True				, _UpperCAmelCase   :						int=True				, _UpperCAmelCase   :						List[str]=99				, _UpperCAmelCase   :						Any=32				, _UpperCAmelCase   :						int=5				, _UpperCAmelCase   :						Optional[Any]=4				, _UpperCAmelCase   :						Tuple=37				, _UpperCAmelCase   :						str="gelu"				, _UpperCAmelCase   :						Dict=0.1				, _UpperCAmelCase   :						int=0.1				, _UpperCAmelCase   :						Union[str, Any]=512				, _UpperCAmelCase   :						str=16				, _UpperCAmelCase   :						Optional[int]=2				, _UpperCAmelCase   :						Optional[Any]=0.02				, _UpperCAmelCase   :						str=4				, )						->       Any:




							"""simple docstring"""


							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   lowerCamelCase__					(self   :						Optional[Any]		)						->       int:




							"""simple docstring"""


							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=_UpperCAmelCase				, initializer_range=self.initializer_range				, )

							return config, input_ids, token_type_ids, attention_mask

					def   lowerCamelCase__					(self   :						List[Any]		)						->       List[Any]:




							"""simple docstring"""


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


					def   lowerCamelCase__					(self   :						str		)						->       List[Any]:




							"""simple docstring"""


							lowercase__					      =		self.prepare_config_and_inputs()
							lowercase__      ,     lowercase__      ,     lowercase__      ,     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 							A   (	UpperCAmelCase__			,  unittest.TestCase							):
					'''simple docstring'''






					A__      =			True

					A__      =			(
					    (
					        FlaxRobertaPreLayerNormModel,
					        FlaxRobertaPreLayerNormForCausalLM,
					        FlaxRobertaPreLayerNormForMaskedLM,
					        FlaxRobertaPreLayerNormForSequenceClassification,
					        FlaxRobertaPreLayerNormForTokenClassification,
					        FlaxRobertaPreLayerNormForMultipleChoice,
					        FlaxRobertaPreLayerNormForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)

					def   lowerCamelCase__					(self   :						Tuple		)						->       int:




							"""simple docstring"""


							lowercase__					      =		FlaxRobertaPreLayerNormModelTester(self		)


					@slow
					def   lowerCamelCase__					(self   :						str		)						->       List[Any]:




							"""simple docstring"""


							for model_class_name in self.all_model_classes:
									lowercase__					      =		model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""				, from_pt=_UpperCAmelCase		)
									lowercase__					      =		model(np.ones((1, 1)		)		)
									self.assertIsNotNone(_UpperCAmelCase		)






@require_flax
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					@slow
					def   lowerCamelCase__					(self   :						Dict		)						->       Union[str, Any]:




							"""simple docstring"""


							lowercase__					      =		FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""				, from_pt=_UpperCAmelCase		)

							lowercase__					      =		np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]				, dtype=jnp.intaa		)
							lowercase__					      =		model(_UpperCAmelCase		)[0]
							lowercase__					      =		[1, 11, 5_0265]
							self.assertEqual(list(output.shape		)				, _UpperCAmelCase		)
							# 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]				, _UpperCAmelCase				, atol=1E-4		)		)


					@slow
					def   lowerCamelCase__					(self   :						int		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""				, from_pt=_UpperCAmelCase		)

							lowercase__					      =		np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]				, dtype=jnp.intaa		)
							lowercase__					      =		model(_UpperCAmelCase		)[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]				, _UpperCAmelCase				, atol=1E-4		)		)





A	:		Tuple    =       'ABCDEFGHIJKLMNOPQRSTUVWXYZ'






def 			UpperCamelCase (     )       ->  None:
		"""simple docstring"""




		lowercase__					      =		input("""Enter message: """					)
		lowercase__					      =		input("""Enter key [alphanumeric]: """					)
		lowercase__					      =		input("""Encrypt/Decrypt [e/d]: """					)

		if mode.lower().startswith("""e"""					):
				lowercase__					      =		"""encrypt"""
				lowercase__					      =		encrypt_message(__magic_name__   ,   __magic_name__					)
		elif mode.lower().startswith("""d"""					):
				lowercase__					      =		"""decrypt"""
				lowercase__					      =		decrypt_message(__magic_name__   ,   __magic_name__					)

		print(f'''\n{mode.title()}ed message:'''					)
		print(__magic_name__					)






def 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							str					)       ->  str:
		"""simple docstring"""




		return translate_message(__magic_name__   ,   __magic_name__   ,   """encrypt"""					)






def 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							str					)       ->  str:
		"""simple docstring"""




		return translate_message(__magic_name__   ,   __magic_name__   ,   """decrypt"""					)






def 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							str   ,   __magic_name__					:							str					)       ->  str:
		"""simple docstring"""




		lowercase__					      =		[]
		lowercase__					      =		0
		lowercase__					      =		key.upper()

		for symbol in message:
				lowercase__					      =		LETTERS.find(symbol.upper()					)
				if num != -1:
						if mode == "encrypt":
								num += LETTERS.find(key[key_index]					)
						elif mode == "decrypt":
								num -= LETTERS.find(key[key_index]					)

						num %= len(__magic_name__					)

						if symbol.isupper():
								translated.append(LETTERS[num]					)
						elif symbol.islower():
								translated.append(LETTERS[num].lower()					)

						key_index += 1
						if key_index == len(__magic_name__					):
								lowercase__					      =		0
				else:
						translated.append(__magic_name__					)
		return "".join(__magic_name__					)


if __name__ == "__main__":
	main()

