
"""simple docstring"""







from __future__ import annotations




class 				A__ :

		def __init__(   self ,	_SCREAMING_SNAKE_CASE = 0				):
			__lowerCAmelCase    :		List[str]		    =   key

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			assert isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) and isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Optional[int]		    =   key or self.__key or 1

			# make sure key is an appropriate size
			key %= 2_55

			return [chr(ord(_SCREAMING_SNAKE_CASE				) ^ key				) for ch in content]

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			assert isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) and isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Union[str, Any]		    =   key or self.__key or 1

			# make sure key is an appropriate size
			key %= 2_55

			return [chr(ord(_SCREAMING_SNAKE_CASE				) ^ key				) for ch in content]

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = 0				):
			assert isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) and isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Union[str, Any]		    =   key or self.__key or 1

			# make sure key can be any size
			while key > 2_55:
				key -= 2_55

			# This will be returned
			__lowerCAmelCase    :		Union[str, Any]		    =   ''

			for ch in content:
				ans += chr(ord(_SCREAMING_SNAKE_CASE				) ^ key				)

			return ans

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = 0				):
			assert isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) and isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Union[str, Any]		    =   key or self.__key or 1

			# make sure key can be any size
			while key > 2_55:
				key -= 2_55

			# This will be returned
			__lowerCAmelCase    :		List[Any]		    =   ''

			for ch in content:
				ans += chr(ord(_SCREAMING_SNAKE_CASE				) ^ key				)

			return ans

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = 0				):
			assert isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) and isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

			try:
				with open(_SCREAMING_SNAKE_CASE				) as fin, open('encrypt.out' ,	'w+'				) as fout:
					# actual encrypt-process
					for line in fin:
						fout.write(self.encrypt_string(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)				)

			except OSError:
				return False

			return True







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			assert isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) and isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

			try:
				with open(_SCREAMING_SNAKE_CASE				) as fin, open('decrypt.out' ,	'w+'				) as fout:
					# actual encrypt-process
					for line in fin:
						fout.write(self.decrypt_string(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)				)

			except OSError:
				return False

			return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")

"""simple docstring"""







import unittest

import numpy as np

from transformers import RobertaConfig, 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():
							from transformers.models.roberta.modeling_flax_roberta import (
							    FlaxRobertaForCausalLM,
							    FlaxRobertaForMaskedLM,
							    FlaxRobertaForMultipleChoice,
							    FlaxRobertaForQuestionAnswering,
							    FlaxRobertaForSequenceClassification,
							    FlaxRobertaForTokenClassification,
							    FlaxRobertaModel,
							)




class 				A__ (			unittest.TestCase):

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=13 ,	_SCREAMING_SNAKE_CASE=7 ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=99 ,	_SCREAMING_SNAKE_CASE=32 ,	_SCREAMING_SNAKE_CASE=5 ,	_SCREAMING_SNAKE_CASE=4 ,	_SCREAMING_SNAKE_CASE=37 ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=5_12 ,	_SCREAMING_SNAKE_CASE=16 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=4 ,	):
			__lowerCAmelCase    :		List[Any]		    =   parent
			__lowerCAmelCase    :		int		    =   batch_size
			__lowerCAmelCase    :		List[str]		    =   seq_length
			__lowerCAmelCase    :		str		    =   is_training
			__lowerCAmelCase    :		Optional[Any]		    =   use_attention_mask
			__lowerCAmelCase    :		Union[str, Any]		    =   use_token_type_ids
			__lowerCAmelCase    :		Dict		    =   use_labels
			__lowerCAmelCase    :		Union[str, Any]		    =   vocab_size
			__lowerCAmelCase    :		List[str]		    =   hidden_size
			__lowerCAmelCase    :		List[str]		    =   num_hidden_layers
			__lowerCAmelCase    :		Optional[int]		    =   num_attention_heads
			__lowerCAmelCase    :		Dict		    =   intermediate_size
			__lowerCAmelCase    :		Dict		    =   hidden_act
			__lowerCAmelCase    :		Optional[int]		    =   hidden_dropout_prob
			__lowerCAmelCase    :		Dict		    =   attention_probs_dropout_prob
			__lowerCAmelCase    :		Union[str, Any]		    =   max_position_embeddings
			__lowerCAmelCase    :		Any		    =   type_vocab_size
			__lowerCAmelCase    :		Tuple		    =   type_sequence_label_size
			__lowerCAmelCase    :		Union[str, Any]		    =   initializer_range
			__lowerCAmelCase    :		int		    =   num_choices

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Union[str, Any]		    =   ids_tensor([self.batch_size, self.seq_length] ,	self.vocab_size				)

			__lowerCAmelCase    :		List[str]		    =   None
			if self.use_attention_mask:
				__lowerCAmelCase    :		str		    =   random_attention_mask([self.batch_size, self.seq_length]				)

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

			__lowerCAmelCase    :		str		    =   RobertaConfig(
			    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=_SCREAMING_SNAKE_CASE ,	initializer_range=self.initializer_range ,	)

			return config, input_ids, token_type_ids, attention_mask

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   self.prepare_config_and_inputs()
			__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		List[Any]		    =   config_and_inputs
			__lowerCAmelCase    :		List[Any]		    =   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
			return config, inputs_dict







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[int]		    =   self.prepare_config_and_inputs()
			__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Any		    =   config_and_inputs

			__lowerCAmelCase    :		int		    =   True
			__lowerCAmelCase    :		Any		    =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
			__lowerCAmelCase    :		int		    =   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
class 				A__ (			_lowerCamelCase ,  unittest.TestCase):
		A_		: List[Any]            =			True

		A_		: Optional[Any]            =			(
		    (
		        FlaxRobertaModel,
		        FlaxRobertaForCausalLM,
		        FlaxRobertaForMaskedLM,
		        FlaxRobertaForSequenceClassification,
		        FlaxRobertaForTokenClassification,
		        FlaxRobertaForMultipleChoice,
		        FlaxRobertaForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Tuple		    =   FlaxRobertaModelTester(self				)







		@slow
		def 							__lowerCamelCase							(   self				):
			for model_class_name in self.all_model_classes:
				__lowerCAmelCase    :		str		    =   model_class_name.from_pretrained('roberta-base' ,	from_pt=_SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase    :		Optional[int]		    =   model(np.ones((1, 1)				)				)
				self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)