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				,				A_				,				A_=13				,				A_=7				,				A_=True				,				A_=True				,				A_=True				,				A_=True				,				A_=99				,				A_=32				,				A_=2				,				A_=4				,				A_=37				,				A_="gelu"				,				A_=0.1				,				A_=0.1				,				A_=5_12				,				A_=16				,				A_=2				,				A_=0.02				,				A_=3				,				A_=4				,				A_=None				,				) ->				Optional[int]:






           """simple docstring"""
           _lowerCamelCase			    =				parent
           _lowerCamelCase			    =				13
           _lowerCamelCase			    =				7
           _lowerCamelCase			    =				True
           _lowerCamelCase			    =				True
           _lowerCamelCase			    =				True
           _lowerCamelCase			    =				True
           _lowerCamelCase			    =				99
           _lowerCamelCase			    =				32
           _lowerCamelCase			    =				2
           _lowerCamelCase			    =				4
           _lowerCamelCase			    =				37
           _lowerCamelCase			    =				'''gelu'''
           _lowerCamelCase			    =				0.1
           _lowerCamelCase			    =				0.1
           _lowerCamelCase			    =				5_12
           _lowerCamelCase			    =				16
           _lowerCamelCase			    =				2
           _lowerCamelCase			    =				0.02
           _lowerCamelCase			    =				3
           _lowerCamelCase			    =				4
           _lowerCamelCase			    =				None



     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           _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=A_				,				)

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



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				TFRoFormerModel(config=A_						)
           _lowerCamelCase			    =				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

           _lowerCamelCase			    =				[input_ids, input_mask]
           _lowerCamelCase			    =				model(A_						)

           _lowerCamelCase			    =				model(A_						)

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



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_						) ->				Optional[int]:






           """simple docstring"""
           _lowerCamelCase			    =				True
           _lowerCamelCase			    =				TFRoFormerForCausalLM(config=A_						)
           _lowerCamelCase			    =				{
               '''input_ids''': input_ids,
               '''attention_mask''': input_mask,
               '''token_type_ids''': token_type_ids,
           }
           _lowerCamelCase			    =				model(A_						)['''logits''']
           self.parent.assertListEqual(
               list(prediction_scores.numpy().shape						)				,				[self.batch_size, self.seq_length, self.vocab_size]						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				TFRoFormerForMaskedLM(config=A_						)
           _lowerCamelCase			    =				{
               '''input_ids''': input_ids,
               '''attention_mask''': input_mask,
               '''token_type_ids''': token_type_ids,
           }
           _lowerCamelCase			    =				model(A_						)
           self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.vocab_size)						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				self.num_labels
           _lowerCamelCase			    =				TFRoFormerForSequenceClassification(config=A_						)
           _lowerCamelCase			    =				{
               '''input_ids''': input_ids,
               '''attention_mask''': input_mask,
               '''token_type_ids''': token_type_ids,
           }

           _lowerCamelCase			    =				model(A_						)
           self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.num_labels)						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				self.num_choices
           _lowerCamelCase			    =				TFRoFormerForMultipleChoice(config=A_						)
           _lowerCamelCase			    =				tf.tile(tf.expand_dims(A_				,				1						)				,				(1, self.num_choices, 1)						)
           _lowerCamelCase			    =				tf.tile(tf.expand_dims(A_				,				1						)				,				(1, self.num_choices, 1)						)
           _lowerCamelCase			    =				tf.tile(tf.expand_dims(A_				,				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(A_						)
           self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.num_choices)						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				self.num_labels
           _lowerCamelCase			    =				TFRoFormerForTokenClassification(config=A_						)
           _lowerCamelCase			    =				{
               '''input_ids''': input_ids,
               '''attention_mask''': input_mask,
               '''token_type_ids''': token_type_ids,
           }
           _lowerCamelCase			    =				model(A_						)
           self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.num_labels)						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase			    =				TFRoFormerForQuestionAnswering(config=A_						)
           _lowerCamelCase			    =				{
               '''input_ids''': input_ids,
               '''attention_mask''': input_mask,
               '''token_type_ids''': token_type_ids,
           }

           _lowerCamelCase			    =				model(A_						)
           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						) ->				Union[str, Any]:






           """simple docstring"""
           _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				( __lowercase   ,  __lowercase   ,  unittest.TestCase ):

     '''simple docstring'''

     A_										=  (
         (
             TFRoFormerModel,
             TFRoFormerForCausalLM,
             TFRoFormerForMaskedLM,
             TFRoFormerForQuestionAnswering,
             TFRoFormerForSequenceClassification,
             TFRoFormerForTokenClassification,
             TFRoFormerForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     A_										=  (
         {
             '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 {}
     )

     A_										=  False
     A_										=  False



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				A_				,				A_						) ->				Optional[int]:






           """simple docstring"""
           if pipeline_test_casse_name == "TextGenerationPipelineTests":
                 return True

           return False



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






           """simple docstring"""
           _lowerCamelCase			    =				TFRoFormerModelTester(self						)
           _lowerCamelCase			    =				ConfigTester(self				,				config_class=A_				,				hidden_size=37						)



     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           self.config_tester.run_common_tests()



     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A_						)



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






           """simple docstring"""
           _lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*A_						)



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_lm_head(*A_						)



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*A_						)



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*A_						)



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*A_						)



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*A_						)





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






           """simple docstring"""
           _lowerCamelCase			    =				TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base'''						)
           self.assertIsNotNone(A_						)





@require_tf
class  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     @slow
     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''						)
           _lowerCamelCase			    =				tf.constant([[0, 1, 2, 3, 4, 5]]						)
           _lowerCamelCase			    =				model(A_						)[0]

           # TODO Replace vocab size
           _lowerCamelCase			    =				5_00_00

           _lowerCamelCase			    =				[1, 6, vocab_size]
           self.assertEqual(output.shape				,				A_						)

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

           # TODO Replace values below with what was printed above.
           _lowerCamelCase			    =				tf.constant(
               [
                   [
                       [-0.12053341, -1.0264901, 0.29221946],
                       [-1.5133783, 0.197433, 0.15190607],
                       [-5.0135403, -3.900256, -0.84038764],
                   ]
               ]						)
           tf.debugging.assert_near(output[:, :3, :3]				,				A_				,				atol=1E-4						)





@require_tf
class  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     A_										=  1E-4



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






           """simple docstring"""
           _lowerCamelCase			    =				tf.constant([[4, 10]]						)
           _lowerCamelCase			    =				TFRoFormerSinusoidalPositionalEmbedding(num_positions=6				,				embedding_dim=6						)

           _lowerCamelCase			    =				emba(input_ids.shape						)
           _lowerCamelCase			    =				tf.constant(
               [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]						)

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





     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				tf.constant(
               [
                   [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
                   [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
                   [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
               ]						)
           _lowerCamelCase			    =				TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12				,				embedding_dim=5_12						)
           emba([2, 16, 5_12]						)
           _lowerCamelCase			    =				emba.weight[:3, :5]

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





@require_tf
class  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     A_										=  1E-4



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           # 2,12,16,64
           _lowerCamelCase			    =				tf.reshape(tf.range(2 * 12 * 16 * 64				,				dtype=tf.floataa						)				,				shape=(2, 12, 16, 64)						) / 1_00

           _lowerCamelCase			    =				-tf.reshape(tf.range(2 * 12 * 16 * 64				,				dtype=tf.floataa						)				,				shape=(2, 12, 16, 64)						) / 1_00

           _lowerCamelCase			    =				TFRoFormerSinusoidalPositionalEmbedding(num_positions=32				,				embedding_dim=64						)
           _lowerCamelCase			    =				embed_positions([2, 16, 7_68]						)[None, None, :, :]

           _lowerCamelCase    ,					_lowerCamelCase			    =				TFRoFormerSelfAttention.apply_rotary_position_embeddings(
               A_				,				A_				,				A_						)

           _lowerCamelCase			    =				tf.constant(
               [
                   [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
                   [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
                   [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
                   [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
                   [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
                   [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
               ]						)
           _lowerCamelCase			    =				tf.constant(
               [
                   [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
                   [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
                   [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
                   [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
                   [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
                   [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
               ]						)

           tf.debugging.assert_near(query_layer[0, 0, :6, :8]				,				A_				,				atol=self.tolerance						)
           tf.debugging.assert_near(key_layer[0, 0, :6, :8]				,				A_				,				atol=self.tolerance						)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    str:



      '''simple docstring'''

      _lowerCamelCase			    =				0
      while b > 0:
            if b & 1:
                  res += a

            a += a
            b >>= 1

      return res
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    Optional[Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				0
      while b > 0:
            if b & 1:
                  _lowerCamelCase			    =				((res % c) + (a % c)) % c

            a += a
            b >>= 1

      return res