





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     __lowerCAmelCase	:







      def __init__(   self		,				lowerCAmelCase__		,				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__=0.02		,				lowerCAmelCase__=3		,				lowerCAmelCase__=4		,				lowerCAmelCase__=None		,				)       ->						str:



        '''simple docstring'''




        a__     : List[Any]  =parent
        a__     : str  =1_3
        a__     : Dict  =7
        a__     : int  =True
        a__     : Union[str, Any]  =True
        a__     : Tuple  =True
        a__     : Optional[int]  =True
        a__     : str  =9_9
        a__     : List[str]  =3_2
        a__     : Dict  =2
        a__     : str  =4
        a__     : Optional[Any]  =3_7
        a__     : Optional[int]  ="gelu"
        a__     : Optional[Any]  =0.1
        a__     : str  =0.1
        a__     : str  =5_1_2
        a__     : Dict  =1_6
        a__     : Tuple  =2
        a__     : Optional[Any]  =0.02
        a__     : Optional[int]  =3
        a__     : Tuple  =4
        a__     : Optional[int]  =None







      def        _lowercase  (   self							)       ->						List[str]:



        '''simple docstring'''




        a__     : Tuple  =ids_tensor([self.batch_size, self.seq_length]		,				self.vocab_size							)

        a__     : Optional[int]  =None
        if self.use_input_mask:
          a__     : Tuple  =random_attention_mask([self.batch_size, self.seq_length]							)

        a__     : List[Any]  =None
        if self.use_token_type_ids:
          a__     : Dict  =ids_tensor([self.batch_size, self.seq_length]		,				self.type_vocab_size							)

        a__     : int  =None
        a__     : Optional[Any]  =None
        a__     : Union[str, Any]  =None
        if self.use_labels:
          a__     : Optional[Any]  =ids_tensor([self.batch_size]		,				self.type_sequence_label_size							)
          a__     : List[Any]  =ids_tensor([self.batch_size, self.seq_length]		,				self.num_labels							)
          a__     : Optional[Any]  =ids_tensor([self.batch_size]		,				self.num_choices							)

        a__     : List[Any]  =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=lowerCAmelCase__		,				)

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







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        a__     : int  =TFRoFormerModel(config=lowerCAmelCase__							)
        a__     : Optional[int]  ={"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

        a__     : Optional[int]  =[input_ids, input_mask]
        a__     : int  =model(lowerCAmelCase__							)

        a__     : Dict  =model(lowerCAmelCase__							)

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







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						str:



        '''simple docstring'''




        a__     : Union[str, Any]  =True
        a__     : List[Any]  =TFRoFormerForCausalLM(config=lowerCAmelCase__							)
        a__     : List[str]  ={
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }
        a__     : Tuple  =model(lowerCAmelCase__							)["logits"]
        self.parent.assertListEqual(
            list(prediction_scores.numpy().shape							)		,				[self.batch_size, self.seq_length, self.vocab_size]							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						List[str]:



        '''simple docstring'''




        a__     : List[str]  =TFRoFormerForMaskedLM(config=lowerCAmelCase__							)
        a__     : int  ={
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }
        a__     : Optional[Any]  =model(lowerCAmelCase__							)
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length, self.vocab_size)							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Tuple:



        '''simple docstring'''




        a__     : Tuple  =self.num_labels
        a__     : int  =TFRoFormerForSequenceClassification(config=lowerCAmelCase__							)
        a__     : Optional[Any]  ={
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }

        a__     : Optional[int]  =model(lowerCAmelCase__							)
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Any:



        '''simple docstring'''




        a__     : int  =self.num_choices
        a__     : int  =TFRoFormerForMultipleChoice(config=lowerCAmelCase__							)
        a__     : int  =tf.tile(tf.expand_dims(lowerCAmelCase__		,				1							)		,				(1, self.num_choices, 1)							)
        a__     : Tuple  =tf.tile(tf.expand_dims(lowerCAmelCase__		,				1							)		,				(1, self.num_choices, 1)							)
        a__     : List[Any]  =tf.tile(tf.expand_dims(lowerCAmelCase__		,				1							)		,				(1, self.num_choices, 1)							)
        a__     : Union[str, Any]  ={
            "input_ids": multiple_choice_inputs_ids,
            "attention_mask": multiple_choice_input_mask,
            "token_type_ids": multiple_choice_token_type_ids,
        }
        a__     : Optional[int]  =model(lowerCAmelCase__							)
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_choices)							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Dict:



        '''simple docstring'''




        a__     : Any  =self.num_labels
        a__     : List[Any]  =TFRoFormerForTokenClassification(config=lowerCAmelCase__							)
        a__     : Any  ={
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }
        a__     : Any  =model(lowerCAmelCase__							)
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length, self.num_labels)							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						List[Any]:



        '''simple docstring'''




        a__     : int  =TFRoFormerForQuestionAnswering(config=lowerCAmelCase__							)
        a__     : Optional[Any]  ={
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }

        a__     : Dict  =model(lowerCAmelCase__							)
        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        _lowercase  (   self							)       ->						List[str]:



        '''simple docstring'''




        a__     : int  =self.prepare_config_and_inputs()
        (
            (
            a__
        ) ,							(
            a__
        ) ,							(
            a__
        ) ,							(
            a__
        ) ,							(
            a__
        ) ,							(
            a__
        ) ,							(
            a__
        ) ,							
        )     : Any  =config_and_inputs
        a__     : Union[str, Any]  ={"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
        return config, inputs_dict







@require_tf
class     __lowerCAmelCase	(   UpperCamelCase__						,    UpperCamelCase__						,    unittest.TestCase):
      _lowercase    : List[str]            =      (
          (
              TFRoFormerModel,
              TFRoFormerForCausalLM,
              TFRoFormerForMaskedLM,
              TFRoFormerForQuestionAnswering,
              TFRoFormerForSequenceClassification,
              TFRoFormerForTokenClassification,
              TFRoFormerForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      _lowercase    : Any            =      (
          {
              """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 {}
      )

      _lowercase    : List[Any]            =      False
      _lowercase    : int            =      False







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Tuple:



        '''simple docstring'''




        if pipeline_test_casse_name == "TextGenerationPipelineTests":
          return True

        return False







      def        _lowercase  (   self							)       ->						Union[str, Any]:



        '''simple docstring'''




        a__     : Optional[Any]  =TFRoFormerModelTester(self							)
        a__     : Tuple  =ConfigTester(self		,				config_class=lowerCAmelCase__		,				hidden_size=3_7							)







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        self.config_tester.run_common_tests()







      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




        a__     : Optional[int]  =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




        a__     : Optional[Any]  =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        a__     : str  =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head(*lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : Union[str, Any]  =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        a__     : Any  =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        a__     : Optional[int]  =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        a__     : Dict  =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__							)







      @slow
      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




        a__     : str  =TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base"							)
        self.assertIsNotNone(lowerCAmelCase__							)







@require_tf
class     __lowerCAmelCase	(   unittest.TestCase):







      @slow
      def        _lowercase  (   self							)       ->						Dict:



        '''simple docstring'''




        a__     : str  =TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"							)
        a__     : int  =tf.constant([[0, 1, 2, 3, 4, 5]]							)
        a__     : Tuple  =model(lowerCAmelCase__							)[0]

        # TODO Replace vocab size
        a__     : Optional[Any]  =5_0_0_0_0

        a__     : List[str]  =[1, 6, vocab_size]
        self.assertEqual(output.shape		,				lowerCAmelCase__							)

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

        # TODO Replace values below with what was printed above.
        a__     : Tuple  =tf.constant(
            [
                [
                    [-0.12_05_33_41, -1.0_26_49_01, 0.29_22_19_46],
                    [-1.5_13_37_83, 0.19_74_33, 0.15_19_06_07],
                    [-5.0_13_54_03, -3.90_02_56, -0.84_03_87_64],
                ]
            ]							)
        tf.debugging.assert_near(output[:, :3, :3]		,				lowerCAmelCase__		,				atol=1E-4							)







@require_tf
class     __lowerCAmelCase	(   unittest.TestCase):
      _lowercase    : Optional[int]            =      1E-4







      def        _lowercase  (   self							)       ->						Union[str, Any]:



        '''simple docstring'''




        a__     : List[str]  =tf.constant([[4, 1_0]]							)
        a__     : str  =TFRoFormerSinusoidalPositionalEmbedding(num_positions=6		,				embedding_dim=6							)

        a__     : Dict  =emba(input_ids.shape							)
        a__     : Optional[int]  =tf.constant(
            [[0.00_00, 0.00_00, 0.00_00, 1.00_00, 1.00_00, 1.00_00], [0.84_15, 0.04_64, 0.00_22, 0.54_03, 0.99_89, 1.00_00]]							)

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







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        a__     : Union[str, Any]  =tf.constant(
            [
                [0.00_00, 0.00_00, 0.00_00, 0.00_00, 0.00_00],
                [0.84_15, 0.82_19, 0.80_20, 0.78_19, 0.76_17],
                [0.90_93, 0.93_64, 0.95_81, 0.97_49, 0.98_70],
            ]							)
        a__     : Any  =TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2		,				embedding_dim=5_1_2							)
        emba([2, 1_6, 5_1_2]							)
        a__     : List[str]  =emba.weight[:3, :5]

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







@require_tf
class     __lowerCAmelCase	(   unittest.TestCase):
      _lowercase    : Any            =      1E-4







      def        _lowercase  (   self							)       ->						Dict:



        '''simple docstring'''




        a__     : List[str]  =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

        a__     : int  =-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

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

        a__ ,							a__     : List[Any]  =TFRoFormerSelfAttention.apply_rotary_position_embeddings(
            lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)

        a__     : List[Any]  =tf.constant(
            [
                [0.00_00, 0.01_00, 0.02_00, 0.03_00, 0.04_00, 0.05_00, 0.06_00, 0.07_00],
                [-0.20_12, 0.88_97, 0.02_63, 0.94_01, 0.20_74, 0.94_63, 0.34_81, 0.93_43],
                [-1.70_57, 0.62_71, -1.21_45, 1.38_97, -0.63_03, 1.76_47, -0.11_73, 1.89_85],
                [-2.17_31, -1.63_97, -2.73_58, 0.28_54, -2.18_40, 1.71_83, -1.30_18, 2.48_71],
                [0.27_17, -3.61_73, -2.92_06, -2.19_88, -3.66_38, 0.38_58, -2.91_55, 2.29_80],
                [3.98_59, -2.15_80, -0.79_84, -4.49_04, -4.11_81, -2.02_52, -4.47_82, 1.12_53],
            ]							)
        a__     : List[Any]  =tf.constant(
            [
                [0.00_00, -0.01_00, -0.02_00, -0.03_00, -0.04_00, -0.05_00, -0.06_00, -0.07_00],
                [0.20_12, -0.88_97, -0.02_63, -0.94_01, -0.20_74, -0.94_63, -0.34_81, -0.93_43],
                [1.70_57, -0.62_71, 1.21_45, -1.38_97, 0.63_03, -1.76_47, 0.11_73, -1.89_85],
                [2.17_31, 1.63_97, 2.73_58, -0.28_54, 2.18_40, -1.71_83, 1.30_18, -2.48_71],
                [-0.27_17, 3.61_73, 2.92_06, 2.19_88, 3.66_38, -0.38_58, 2.91_55, -2.29_80],
                [-3.98_59, 2.15_80, 0.79_84, 4.49_04, 4.11_81, 2.02_52, 4.47_82, -1.12_53],
            ]							)

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













import random
from typing import Any
def 					_A	(		SCREAMING_SNAKE_CASE					:       list						):





  """simple docstring"""



  for _ in range(len(SCREAMING_SNAKE_CASE						)						):
    a__     : Tuple  =random.randint(0	,	len(SCREAMING_SNAKE_CASE						) - 1						)
    a__     : Any  =random.randint(0	,	len(SCREAMING_SNAKE_CASE						) - 1						)
    a__ ,							a__     : Tuple  =data[b], data[a]
  return data


if __name__ == "__main__":
     UpperCAmelCase     :     Dict							=	[0, 1, 2, 3, 4, 5, 6, 7]
     UpperCAmelCase     :     Optional[Any]							=	["""python""", """says""", """hello""", """!"""]
     print("""Fisher-Yates Shuffle:""")
     print("""List""", integers, strings)
     print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))






