




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    :






   '''simple docstring'''





   def __init__( self			:   Any       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   List[str]=13       ,						lowerCamelCase_			:   str=7       ,						lowerCamelCase_			:   List[Any]=True       ,						lowerCamelCase_			:   Optional[Any]=True       ,						lowerCamelCase_			:   Optional[Any]=True       ,						lowerCamelCase_			:   Optional[int]=True       ,						lowerCamelCase_			:   Any=99       ,						lowerCamelCase_			:   List[str]=32       ,						lowerCamelCase_			:   Union[str, Any]=2       ,						lowerCamelCase_			:   Any=4       ,						lowerCamelCase_			:   Dict=37       ,						lowerCamelCase_			:   Optional[Any]="gelu"       ,						lowerCamelCase_			:   Union[str, Any]=0.1       ,						lowerCamelCase_			:   Optional[int]=0.1       ,						lowerCamelCase_			:   List[str]=512       ,						lowerCamelCase_			:   List[Any]=16       ,						lowerCamelCase_			:   str=2       ,						lowerCamelCase_			:   Optional[int]=0.0_2       ,						lowerCamelCase_			:   List[Any]=3       ,						lowerCamelCase_			:   List[Any]=4       ,						lowerCamelCase_			:   str=None       ,						)						->   List[Any]:
        __magic_name__							:	str    							=							parent
        __magic_name__							:	List[Any]    							=							13
        __magic_name__							:	Any    							=							7
        __magic_name__							:	List[Any]    							=							True
        __magic_name__							:	Optional[int]    							=							True
        __magic_name__							:	str    							=							True
        __magic_name__							:	Tuple    							=							True
        __magic_name__							:	Union[str, Any]    							=							99
        __magic_name__							:	List[str]    							=							32
        __magic_name__							:	Tuple    							=							2
        __magic_name__							:	Dict    							=							4
        __magic_name__							:	Union[str, Any]    							=							37
        __magic_name__							:	Optional[int]    							=							'''gelu'''
        __magic_name__							:	Dict    							=							0.1
        __magic_name__							:	Dict    							=							0.1
        __magic_name__							:	Optional[int]    							=							512
        __magic_name__							:	Optional[Any]    							=							16
        __magic_name__							:	Optional[Any]    							=							2
        __magic_name__							:	str    							=							0.0_2
        __magic_name__							:	str    							=							3
        __magic_name__							:	Optional[int]    							=							4
        __magic_name__							:	str    							=							None

   def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   str:
        __magic_name__							:	str    							=							ids_tensor([self.batch_size, self.seq_length]       ,						self.vocab_size  )

        __magic_name__							:	Any    							=							None
        if self.use_input_mask:
             __magic_name__							:	Union[str, Any]    							=							random_attention_mask([self.batch_size, self.seq_length]  )

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

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

        __magic_name__							:	Union[str, 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=__UpperCamelCase       ,						)

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

   def 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   Any       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   Dict  )						->   List[str]:
        __magic_name__							:	Tuple    							=							TFRoFormerModel(config=__UpperCamelCase  )
        __magic_name__							:	Tuple    							=							{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

        __magic_name__							:	str    							=							[input_ids, input_mask]
        __magic_name__							:	Dict    							=							model(__UpperCamelCase  )

        __magic_name__							:	Optional[Any]    							=							model(__UpperCamelCase  )

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

   def 							UpperCAmelCase__       ( self			:   Tuple       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   Union[str, Any]       ,						lowerCamelCase_			:   Any       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   Any       ,						lowerCamelCase_			:   Tuple  )						->   List[Any]:
        __magic_name__							:	str    							=							True
        __magic_name__							:	int    							=							TFRoFormerForCausalLM(config=__UpperCamelCase  )
        __magic_name__							:	Union[str, Any]    							=							{
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
            '''token_type_ids''': token_type_ids,
        }
        __magic_name__							:	Dict    							=							model(__UpperCamelCase  )['''logits''']
        self.parent.assertListEqual(
            list(prediction_scores.numpy().shape  )       ,						[self.batch_size, self.seq_length, self.vocab_size]  )

   def 							UpperCAmelCase__       ( self			:   Optional[Any]       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   Dict       ,						lowerCamelCase_			:   Dict       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   Any       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   Union[str, Any]  )						->   List[str]:
        __magic_name__							:	Any    							=							TFRoFormerForMaskedLM(config=__UpperCamelCase  )
        __magic_name__							:	Optional[int]    							=							{
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
            '''token_type_ids''': token_type_ids,
        }
        __magic_name__							:	Tuple    							=							model(__UpperCamelCase  )
        self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.seq_length, self.vocab_size)  )

   def 							UpperCAmelCase__       ( self			:   List[Any]       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   Dict       ,						lowerCamelCase_			:   str       ,						lowerCamelCase_			:   str       ,						lowerCamelCase_			:   Any       ,						lowerCamelCase_			:   Tuple       ,						lowerCamelCase_			:   Optional[int]  )						->   List[Any]:
        __magic_name__							:	List[str]    							=							self.num_labels
        __magic_name__							:	int    							=							TFRoFormerForSequenceClassification(config=__UpperCamelCase  )
        __magic_name__							:	List[str]    							=							{
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
            '''token_type_ids''': token_type_ids,
        }

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

   def 							UpperCAmelCase__       ( self			:   int       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   Union[str, Any]       ,						lowerCamelCase_			:   Any       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   Tuple       ,						lowerCamelCase_			:   Union[str, Any]  )						->   int:
        __magic_name__							:	Optional[Any]    							=							self.num_choices
        __magic_name__							:	Union[str, Any]    							=							TFRoFormerForMultipleChoice(config=__UpperCamelCase  )
        __magic_name__							:	List[Any]    							=							tf.tile(tf.expand_dims(__UpperCamelCase       ,						1  )       ,						(1, self.num_choices, 1)  )
        __magic_name__							:	Optional[Any]    							=							tf.tile(tf.expand_dims(__UpperCamelCase       ,						1  )       ,						(1, self.num_choices, 1)  )
        __magic_name__							:	str    							=							tf.tile(tf.expand_dims(__UpperCamelCase       ,						1  )       ,						(1, self.num_choices, 1)  )
        __magic_name__							:	Tuple    							=							{
            '''input_ids''': multiple_choice_inputs_ids,
            '''attention_mask''': multiple_choice_input_mask,
            '''token_type_ids''': multiple_choice_token_type_ids,
        }
        __magic_name__							:	List[Any]    							=							model(__UpperCamelCase  )
        self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.num_choices)  )

   def 							UpperCAmelCase__       ( self			:   str       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   Any       ,						lowerCamelCase_			:   Dict       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   str       ,						lowerCamelCase_			:   Tuple  )						->   str:
        __magic_name__							:	str    							=							self.num_labels
        __magic_name__							:	List[Any]    							=							TFRoFormerForTokenClassification(config=__UpperCamelCase  )
        __magic_name__							:	str    							=							{
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
            '''token_type_ids''': token_type_ids,
        }
        __magic_name__							:	Tuple    							=							model(__UpperCamelCase  )
        self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.seq_length, self.num_labels)  )

   def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   Any       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   Any       ,						lowerCamelCase_			:   Optional[int]  )						->   Optional[Any]:
        __magic_name__							:	Tuple    							=							TFRoFormerForQuestionAnswering(config=__UpperCamelCase  )
        __magic_name__							:	int    							=							{
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
            '''token_type_ids''': token_type_ids,
        }

        __magic_name__							:	Any    							=							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			:   int  )						->   Optional[Any]:
        __magic_name__							:	int    							=							self.prepare_config_and_inputs()
        (
            (
            __magic_name__
        )							,    (
            __magic_name__
        )							,    (
            __magic_name__
        )							,    (
            __magic_name__
        )							,    (
            __magic_name__
        )							,    (
            __magic_name__
        )							,    (
            __magic_name__
        )							,    
        )							:	Any    							=							config_and_inputs
        __magic_name__							:	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						):






   '''simple docstring'''





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

   UpperCamelCase__								=False
   UpperCamelCase__								=False

   def 							UpperCAmelCase__       ( self			:   List[str]       ,						lowerCamelCase_			:   Any       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   Any       ,						lowerCamelCase_			:   Union[str, Any]  )						->   List[str]:






        if pipeline_test_casse_name == "TextGenerationPipelineTests":
             return True

        return False

   def 							UpperCAmelCase__       ( self			:   int  )						->   Dict:
        __magic_name__							:	Dict    							=							TFRoFormerModelTester(self  )
        __magic_name__							:	List[Any]    							=							ConfigTester(self       ,						config_class=__UpperCamelCase       ,						hidden_size=37  )

   def 							UpperCAmelCase__       ( self			:   str  )						->   Dict:
        self.config_tester.run_common_tests()

   def 							UpperCAmelCase__       ( self			:   str  )						->   Optional[int]:
        __magic_name__							:	Tuple    							=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase  )

   def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   Optional[int]:
        __magic_name__							:	Union[str, Any]    							=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase  )

   def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Any:
        __magic_name__							:	List[str]    							=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head(*__UpperCamelCase  )

   def 							UpperCAmelCase__       ( self			:   List[str]  )						->   Union[str, Any]:
        __magic_name__							:	List[Any]    							=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*__UpperCamelCase  )

   def 							UpperCAmelCase__       ( self			:   str  )						->   Optional[Any]:
        __magic_name__							:	int    							=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase  )

   def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   List[Any]:
        __magic_name__							:	Tuple    							=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase  )

   def 							UpperCAmelCase__       ( self			:   List[str]  )						->   int:
        __magic_name__							:	List[str]    							=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase  )


   @slow
   def 							UpperCAmelCase__       ( self			:   Dict  )						->   Optional[int]:
        __magic_name__							:	List[Any]    							=							TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base'''  )
        self.assertIsNotNone(__UpperCamelCase  )






@require_tf
class    lowerCamelCase    (		unittest.TestCase						):






   '''simple docstring'''





   @slow
   def 							UpperCAmelCase__       ( self			:   Dict  )						->   List[str]:
        __magic_name__							:	List[Any]    							=							TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''  )
        __magic_name__							:	List[Any]    							=							tf.constant([[0, 1, 2, 3, 4, 5]]  )
        __magic_name__							:	List[str]    							=							model(__UpperCamelCase  )[0]

        # TODO Replace vocab size
        __magic_name__							:	Tuple    							=							50000

        __magic_name__							:	List[Any]    							=							[1, 6, vocab_size]
        self.assertEqual(output.shape       ,						__UpperCamelCase  )

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

        # TODO Replace values below with what was printed above.
        __magic_name__							:	int    							=							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    lowerCamelCase    (		unittest.TestCase						):






   '''simple docstring'''





   UpperCamelCase__								=1e-4

   def 							UpperCAmelCase__       ( self			:   Tuple  )						->   List[Any]:
        __magic_name__							:	str    							=							tf.constant([[4, 10]]  )
        __magic_name__							:	Optional[int]    							=							TFRoFormerSinusoidalPositionalEmbedding(num_positions=6       ,						embedding_dim=6  )

        __magic_name__							:	List[str]    							=							emba(input_ids.shape  )
        __magic_name__							:	List[str]    							=							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			:   Tuple  )						->   int:
        __magic_name__							:	Any    							=							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],
            ]  )
        __magic_name__							:	Tuple    							=							TFRoFormerSinusoidalPositionalEmbedding(num_positions=512       ,						embedding_dim=512  )
        emba([2, 16, 512]  )
        __magic_name__							:	List[str]    							=							emba.weight[:3, :5]

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






@require_tf
class    lowerCamelCase    (		unittest.TestCase						):






   '''simple docstring'''





   UpperCamelCase__								=1e-4

   def 							UpperCAmelCase__       ( self			:   List[str]  )						->   Optional[Any]:
        __magic_name__							:	List[Any]    							=							tf.reshape(tf.range(2 * 12 * 16 * 64       ,						dtype=tf.floataa  )       ,						shape=(2, 12, 16, 64)  ) / 100

        __magic_name__							:	Optional[Any]    							=							-tf.reshape(tf.range(2 * 12 * 16 * 64       ,						dtype=tf.floataa  )       ,						shape=(2, 12, 16, 64)  ) / 100

        __magic_name__							:	List[str]    							=							TFRoFormerSinusoidalPositionalEmbedding(num_positions=32       ,						embedding_dim=64  )
        __magic_name__							:	Optional[int]    							=							embed_positions([2, 16, 768]  )[None, None, :, :]

        __magic_name__							,    __magic_name__							:	Any    							=							TFRoFormerSelfAttention.apply_rotary_position_embeddings(
            __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase  )

        __magic_name__							:	Optional[int]    							=							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],
            ]  )
        __magic_name__							:	Any    							=							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 json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin


class    lowerCamelCase    (		_lowerCamelCase				,unittest.TestCase						):






			'''simple docstring'''





			UpperCamelCase__								=BlenderbotSmallTokenizer
			UpperCamelCase__								=False

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   str:
								super().setUp()

								__magic_name__							:	Union[str, Any]    							=							['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
								__magic_name__							:	Tuple    							=							dict(zip(lowerCamelCase_       ,						range(len(lowerCamelCase_  )  )  )  )

								__magic_name__							:	Tuple    							=							['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
								__magic_name__							:	List[Any]    							=							{'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

								__magic_name__							:	str    							=							os.path.join(self.tmpdirname       ,						VOCAB_FILES_NAMES['''vocab_file''']  )
								__magic_name__							:	List[str]    							=							os.path.join(self.tmpdirname       ,						VOCAB_FILES_NAMES['''merges_file''']  )
								with open(self.vocab_file       ,						'''w'''       ,						encoding='''utf-8'''  ) as fp:
													fp.write(json.dumps(lowerCamelCase_  ) + '''\n'''  )
								with open(self.merges_file       ,						'''w'''       ,						encoding='''utf-8'''  ) as fp:
													fp.write('''\n'''.join(lowerCamelCase_  )  )

			def 							UpperCAmelCase__       ( self			:   List[str]       ,						**lowerCamelCase_			:   Optional[Any]  )						->   List[Any]:
								kwargs.update(self.special_tokens_map  )
								return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Union[str, Any]  )						->   Optional[int]:
								__magic_name__							:	Union[str, Any]    							=							'''adapt act apte'''
								__magic_name__							:	Dict    							=							'''adapt act apte'''
								return input_text, output_text

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Any:
								__magic_name__							:	int    							=							BlenderbotSmallTokenizer(self.vocab_file       ,						self.merges_file       ,						**self.special_tokens_map  )
								__magic_name__							:	str    							=							'''adapt act apte'''
								__magic_name__							:	Any    							=							['''adapt''', '''act''', '''ap@@''', '''te''']
								__magic_name__							:	List[str]    							=							tokenizer.tokenize(lowerCamelCase_  )
								self.assertListEqual(lowerCamelCase_       ,						lowerCamelCase_  )

								__magic_name__							:	Union[str, Any]    							=							[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

								__magic_name__							:	List[Any]    							=							[0, 1, 2, 3, 4, 5]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_  )       ,						lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   int  )						->   int:
								__magic_name__							:	Any    							=							BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )
								assert tok('''sam'''  ).input_ids == [1384]
								__magic_name__							:	Dict    							=							'''I am a small frog.'''
								__magic_name__							:	Tuple    							=							tok([src_text]       ,						padding=lowerCamelCase_       ,						truncation=lowerCamelCase_  )['''input_ids''']
								__magic_name__							:	Tuple    							=							tok.batch_decode(lowerCamelCase_       ,						skip_special_tokens=lowerCamelCase_       ,						clean_up_tokenization_spaces=lowerCamelCase_  )[0]
								assert src_text != decoded  # I wish it did!
								assert decoded == "i am a small frog ."


			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   Dict:
								__magic_name__							:	Tuple    							=							BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )
								__magic_name__							:	Any    							=							'''I am a small frog .'''
								__magic_name__							:	List[str]    							=							'''.'''
								__magic_name__							:	Tuple    							=							tok(lowerCamelCase_  )['''input_ids''']
								__magic_name__							:	Optional[Any]    							=							tok(lowerCamelCase_  )['''input_ids''']

								assert encoded[-1] == encoded_dot[0]



