






from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFRoFormerForCausalLM,
     TFRoFormerForMaskedLM,
     TFRoFormerForMultipleChoice,
     TFRoFormerForQuestionAnswering,
     TFRoFormerForSequenceClassification,
     TFRoFormerForTokenClassification,
     TFRoFormerModel,
 )
 from transformers.models.roformer.modeling_tf_roformer import (
     TFRoFormerSelfAttention,
     TFRoFormerSinusoidalPositionalEmbedding,
 )



class 							_UpperCamelCase			:

      '''simple docstring'''





      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase=1_3					,						__UpperCamelCase=7					,						__UpperCamelCase=True					,						__UpperCamelCase=True					,						__UpperCamelCase=True					,						__UpperCamelCase=True					,						__UpperCamelCase=9_9					,						__UpperCamelCase=3_2					,						__UpperCamelCase=2					,						__UpperCamelCase=4					,						__UpperCamelCase=3_7					,						__UpperCamelCase="gelu"					,						__UpperCamelCase=0.1					,						__UpperCamelCase=0.1					,						__UpperCamelCase=5_1_2					,						__UpperCamelCase=1_6					,						__UpperCamelCase=2					,						__UpperCamelCase=0.0_2					,						__UpperCamelCase=3					,						__UpperCamelCase=4					,						__UpperCamelCase=None					,						)->	Any:
           __lowerCAmelCase								=					parent
           __lowerCAmelCase								=					1_3
           __lowerCAmelCase								=					7
           __lowerCAmelCase								=					True
           __lowerCAmelCase								=					True
           __lowerCAmelCase								=					True
           __lowerCAmelCase								=					True
           __lowerCAmelCase								=					9_9
           __lowerCAmelCase								=					3_2
           __lowerCAmelCase								=					2
           __lowerCAmelCase								=					4
           __lowerCAmelCase								=					3_7
           __lowerCAmelCase								=					"gelu"
           __lowerCAmelCase								=					0.1
           __lowerCAmelCase								=					0.1
           __lowerCAmelCase								=					5_1_2
           __lowerCAmelCase								=					1_6
           __lowerCAmelCase								=					2
           __lowerCAmelCase								=					0.0_2
           __lowerCAmelCase								=					3
           __lowerCAmelCase								=					4
           __lowerCAmelCase								=					None


      def  __UpperCAmelCase		(	self	)->	List[str]:
           __lowerCAmelCase								=					ids_tensor([self.batch_size, self.seq_length]					,						self.vocab_size	)

           __lowerCAmelCase								=					None
           if self.use_input_mask:
                __lowerCAmelCase								=					random_attention_mask([self.batch_size, self.seq_length]	)

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

           __lowerCAmelCase								=					None
           __lowerCAmelCase								=					None
           __lowerCAmelCase								=					None
           if self.use_labels:
                __lowerCAmelCase								=					ids_tensor([self.batch_size]					,						self.type_sequence_label_size	)
                __lowerCAmelCase								=					ids_tensor([self.batch_size, self.seq_length]					,						self.num_labels	)
                __lowerCAmelCase								=					ids_tensor([self.batch_size]					,						self.num_choices	)

           __lowerCAmelCase								=					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					,						initializer_range=self.initializer_range					,						return_dict=__UpperCamelCase					,						)

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	Optional[Any]:
           __lowerCAmelCase								=					TFRoFormerModel(config=__UpperCamelCase	)
           __lowerCAmelCase								=					{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

           __lowerCAmelCase								=					[input_ids, input_mask]
           __lowerCAmelCase								=					model(__UpperCamelCase	)

           __lowerCAmelCase								=					model(__UpperCamelCase	)

           self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size)	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	Dict:
           __lowerCAmelCase								=					True
           __lowerCAmelCase								=					TFRoFormerForCausalLM(config=__UpperCamelCase	)
           __lowerCAmelCase								=					{
               "input_ids": input_ids,
               "attention_mask": input_mask,
               "token_type_ids": token_type_ids,
           }
           __lowerCAmelCase								=					model(__UpperCamelCase	)["logits"]
           self.parent.assertListEqual(
               list(prediction_scores.numpy().shape	)					,						[self.batch_size, self.seq_length, self.vocab_size]	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	Tuple:
           __lowerCAmelCase								=					TFRoFormerForMaskedLM(config=__UpperCamelCase	)
           __lowerCAmelCase								=					{
               "input_ids": input_ids,
               "attention_mask": input_mask,
               "token_type_ids": token_type_ids,
           }
           __lowerCAmelCase								=					model(__UpperCamelCase	)
           self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.vocab_size)	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	Tuple:
           __lowerCAmelCase								=					self.num_labels
           __lowerCAmelCase								=					TFRoFormerForSequenceClassification(config=__UpperCamelCase	)
           __lowerCAmelCase								=					{
               "input_ids": input_ids,
               "attention_mask": input_mask,
               "token_type_ids": token_type_ids,
           }

           __lowerCAmelCase								=					model(__UpperCamelCase	)
           self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.num_labels)	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	Optional[int]:
           __lowerCAmelCase								=					self.num_choices
           __lowerCAmelCase								=					TFRoFormerForMultipleChoice(config=__UpperCamelCase	)
           __lowerCAmelCase								=					tf.tile(tf.expand_dims(__UpperCamelCase					,						1	)					,						(1, self.num_choices, 1)	)
           __lowerCAmelCase								=					tf.tile(tf.expand_dims(__UpperCamelCase					,						1	)					,						(1, self.num_choices, 1)	)
           __lowerCAmelCase								=					tf.tile(tf.expand_dims(__UpperCamelCase					,						1	)					,						(1, self.num_choices, 1)	)
           __lowerCAmelCase								=					{
               "input_ids": multiple_choice_inputs_ids,
               "attention_mask": multiple_choice_input_mask,
               "token_type_ids": multiple_choice_token_type_ids,
           }
           __lowerCAmelCase								=					model(__UpperCamelCase	)
           self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.num_choices)	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	Tuple:
           __lowerCAmelCase								=					self.num_labels
           __lowerCAmelCase								=					TFRoFormerForTokenClassification(config=__UpperCamelCase	)
           __lowerCAmelCase								=					{
               "input_ids": input_ids,
               "attention_mask": input_mask,
               "token_type_ids": token_type_ids,
           }
           __lowerCAmelCase								=					model(__UpperCamelCase	)
           self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.num_labels)	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	Tuple:
           __lowerCAmelCase								=					TFRoFormerForQuestionAnswering(config=__UpperCamelCase	)
           __lowerCAmelCase								=					{
               "input_ids": input_ids,
               "attention_mask": input_mask,
               "token_type_ids": token_type_ids,
           }

           __lowerCAmelCase								=					model(__UpperCamelCase	)
           self.parent.assertEqual(result.start_logits.shape					,						(self.batch_size, self.seq_length)	)
           self.parent.assertEqual(result.end_logits.shape					,						(self.batch_size, self.seq_length)	)


      def  __UpperCAmelCase		(	self	)->	Any:
           __lowerCAmelCase								=					self.prepare_config_and_inputs()
           (
               (
               __lowerCAmelCase 
           )			, (
               __lowerCAmelCase 
           )			, (
               __lowerCAmelCase 
           )			, (
               __lowerCAmelCase 
           )			, (
               __lowerCAmelCase 
           )			, (
               __lowerCAmelCase 
           )			, (
               __lowerCAmelCase 
           )			,  
           )								=					config_and_inputs
           __lowerCAmelCase								=					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
           return config, inputs_dict



@require_tf
class 							_UpperCamelCase			(a_							,		a_							,		unittest.TestCase			):

      '''simple docstring'''





      snake_case_  =							(
          (
              TFRoFormerModel,
              TFRoFormerForCausalLM,
              TFRoFormerForMaskedLM,
              TFRoFormerForQuestionAnswering,
              TFRoFormerForSequenceClassification,
              TFRoFormerForTokenClassification,
              TFRoFormerForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      snake_case_  =							(
          {
              """feature-extraction""": TFRoFormerModel,
              """fill-mask""": TFRoFormerForMaskedLM,
              """question-answering""": TFRoFormerForQuestionAnswering,
              """text-classification""": TFRoFormerForSequenceClassification,
              """text-generation""": TFRoFormerForCausalLM,
              """token-classification""": TFRoFormerForTokenClassification,
              """zero-shot""": TFRoFormerForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )

      snake_case_  =							False
      snake_case_  =							False


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	str:
           if pipeline_test_casse_name == "TextGenerationPipelineTests":
                return True

           return False


      def  __UpperCAmelCase		(	self	)->	Union[str, Any]:
           __lowerCAmelCase								=					TFRoFormerModelTester(self	)
           __lowerCAmelCase								=					ConfigTester(self					,						config_class=__UpperCamelCase					,						hidden_size=3_7	)


      def  __UpperCAmelCase		(	self	)->	Dict:
           self.config_tester.run_common_tests()


      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           __lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Any:
           __lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Any:
           __lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_lm_head(*__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Tuple:
           __lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	str:
           __lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	str:
           __lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Optional[Any]:
           __lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase	)


      @slow
      def  __UpperCAmelCase		(	self	)->	Dict:
           __lowerCAmelCase								=					TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base"	)
           self.assertIsNotNone(__UpperCamelCase	)



@require_tf
class 							_UpperCamelCase			(unittest.TestCase			):

      '''simple docstring'''





      @slow
      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           __lowerCAmelCase								=					TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"	)
           __lowerCAmelCase								=					tf.constant([[0, 1, 2, 3, 4, 5]]	)
           __lowerCAmelCase								=					model(__UpperCamelCase	)[0]

           # TODO Replace vocab size
           __lowerCAmelCase								=					5_0_0_0_0

           __lowerCAmelCase								=					[1, 6, vocab_size]
           self.assertEqual(output.shape					,						__UpperCamelCase	)

           print(output[:, :3, :3]	)

           # TODO Replace values below with what was printed above.
           __lowerCAmelCase								=					tf.constant(
               [
                   [
                       [-0.1_2_0_5_3_3_4_1, -1.0_2_6_4_9_0_1, 0.2_9_2_2_1_9_4_6],
                       [-1.5_1_3_3_7_8_3, 0.1_9_7_4_3_3, 0.1_5_1_9_0_6_0_7],
                       [-5.0_1_3_5_4_0_3, -3.9_0_0_2_5_6, -0.8_4_0_3_8_7_6_4],
                   ]
               ]	)
           tf.debugging.assert_near(output[:, :3, :3]					,						__UpperCamelCase					,						atol=1e-4	)



@require_tf
class 							_UpperCamelCase			(unittest.TestCase			):

      '''simple docstring'''





      snake_case_  =							1E-4


      def  __UpperCAmelCase		(	self	)->	List[str]:
           __lowerCAmelCase								=					tf.constant([[4, 1_0]]	)
           __lowerCAmelCase								=					TFRoFormerSinusoidalPositionalEmbedding(num_positions=6					,						embedding_dim=6	)

           __lowerCAmelCase								=					emba(input_ids.shape	)
           __lowerCAmelCase								=					tf.constant(
               [[0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 1.0_0_0_0, 1.0_0_0_0, 1.0_0_0_0], [0.8_4_1_5, 0.0_4_6_4, 0.0_0_2_2, 0.5_4_0_3, 0.9_9_8_9, 1.0_0_0_0]]	)

           tf.debugging.assert_near(__UpperCamelCase					,						__UpperCamelCase					,						atol=self.tolerance	)


      def  __UpperCAmelCase		(	self	)->	Optional[Any]:
           __lowerCAmelCase								=					tf.constant(
               [
                   [0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0],
                   [0.8_4_1_5, 0.8_2_1_9, 0.8_0_2_0, 0.7_8_1_9, 0.7_6_1_7],
                   [0.9_0_9_3, 0.9_3_6_4, 0.9_5_8_1, 0.9_7_4_9, 0.9_8_7_0],
               ]	)
           __lowerCAmelCase								=					TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2					,						embedding_dim=5_1_2	)
           emba([2, 1_6, 5_1_2]	)
           __lowerCAmelCase								=					emba.weight[:3, :5]

           tf.debugging.assert_near(__UpperCamelCase					,						__UpperCamelCase					,						atol=self.tolerance	)



@require_tf
class 							_UpperCamelCase			(unittest.TestCase			):

      '''simple docstring'''





      snake_case_  =							1E-4


      def  __UpperCAmelCase		(	self	)->	Optional[Any]:
           # 2,12,16,64
           __lowerCAmelCase								=					tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4					,						dtype=tf.floataa	)					,						shape=(2, 1_2, 1_6, 6_4)	) / 1_0_0

           __lowerCAmelCase								=					-tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4					,						dtype=tf.floataa	)					,						shape=(2, 1_2, 1_6, 6_4)	) / 1_0_0

           __lowerCAmelCase								=					TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2					,						embedding_dim=6_4	)
           __lowerCAmelCase								=					embed_positions([2, 1_6, 7_6_8]	)[None, None, :, :]

           __lowerCAmelCase			, __lowerCAmelCase								=					TFRoFormerSelfAttention.apply_rotary_position_embeddings(
               __UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)

           __lowerCAmelCase								=					tf.constant(
               [
                   [0.0_0_0_0, 0.0_1_0_0, 0.0_2_0_0, 0.0_3_0_0, 0.0_4_0_0, 0.0_5_0_0, 0.0_6_0_0, 0.0_7_0_0],
                   [-0.2_0_1_2, 0.8_8_9_7, 0.0_2_6_3, 0.9_4_0_1, 0.2_0_7_4, 0.9_4_6_3, 0.3_4_8_1, 0.9_3_4_3],
                   [-1.7_0_5_7, 0.6_2_7_1, -1.2_1_4_5, 1.3_8_9_7, -0.6_3_0_3, 1.7_6_4_7, -0.1_1_7_3, 1.8_9_8_5],
                   [-2.1_7_3_1, -1.6_3_9_7, -2.7_3_5_8, 0.2_8_5_4, -2.1_8_4_0, 1.7_1_8_3, -1.3_0_1_8, 2.4_8_7_1],
                   [0.2_7_1_7, -3.6_1_7_3, -2.9_2_0_6, -2.1_9_8_8, -3.6_6_3_8, 0.3_8_5_8, -2.9_1_5_5, 2.2_9_8_0],
                   [3.9_8_5_9, -2.1_5_8_0, -0.7_9_8_4, -4.4_9_0_4, -4.1_1_8_1, -2.0_2_5_2, -4.4_7_8_2, 1.1_2_5_3],
               ]	)
           __lowerCAmelCase								=					tf.constant(
               [
                   [0.0_0_0_0, -0.0_1_0_0, -0.0_2_0_0, -0.0_3_0_0, -0.0_4_0_0, -0.0_5_0_0, -0.0_6_0_0, -0.0_7_0_0],
                   [0.2_0_1_2, -0.8_8_9_7, -0.0_2_6_3, -0.9_4_0_1, -0.2_0_7_4, -0.9_4_6_3, -0.3_4_8_1, -0.9_3_4_3],
                   [1.7_0_5_7, -0.6_2_7_1, 1.2_1_4_5, -1.3_8_9_7, 0.6_3_0_3, -1.7_6_4_7, 0.1_1_7_3, -1.8_9_8_5],
                   [2.1_7_3_1, 1.6_3_9_7, 2.7_3_5_8, -0.2_8_5_4, 2.1_8_4_0, -1.7_1_8_3, 1.3_0_1_8, -2.4_8_7_1],
                   [-0.2_7_1_7, 3.6_1_7_3, 2.9_2_0_6, 2.1_9_8_8, 3.6_6_3_8, -0.3_8_5_8, 2.9_1_5_5, -2.2_9_8_0],
                   [-3.9_8_5_9, 2.1_5_8_0, 0.7_9_8_4, 4.4_9_0_4, 4.1_1_8_1, 2.0_2_5_2, 4.4_7_8_2, -1.1_2_5_3],
               ]	)

           tf.debugging.assert_near(query_layer[0, 0, :6, :8]					,						__UpperCamelCase					,						atol=self.tolerance	)
           tf.debugging.assert_near(key_layer[0, 0, :6, :8]					,						__UpperCamelCase					,						atol=self.tolerance	)














import random



class 							_UpperCamelCase			:


      @staticmethod
      def  __UpperCAmelCase		(	__UpperCamelCase	)->	tuple[list[int], list[int]]:
           __lowerCAmelCase								=					[ord(__UpperCamelCase	) for i in text]
           __lowerCAmelCase								=					[]
           __lowerCAmelCase								=					[]
           for i in plain:
                __lowerCAmelCase								=					random.randint(1					,						3_0_0	)
                __lowerCAmelCase								=					(i + k) * k
                cipher.append(__UpperCamelCase	)
                key.append(__UpperCamelCase	)
           return cipher, key


      @staticmethod
      def  __UpperCAmelCase		(	__UpperCamelCase					,						__UpperCamelCase	)->	str:
           __lowerCAmelCase								=					[]
           for i in range(len(__UpperCamelCase	)	):
                __lowerCAmelCase								=					int((cipher[i] - (key[i]) ** 2) / key[i]	)
                plain.append(chr(__UpperCamelCase	)	)
           return "".join(__UpperCamelCase	)


if __name__ == "__main__":
 lowerCamelCase      ,					lowerCamelCase			:					List[Any]         =	Onepad().encrypt('''Hello''')
 print(c, k)
 print(Onepad().decrypt(c, k))






