


'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
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 (
           TF_MODEL_FOR_PRETRAINING_MAPPING,
           TFMobileBertForMaskedLM,
           TFMobileBertForMultipleChoice,
           TFMobileBertForNextSentencePrediction,
           TFMobileBertForPreTraining,
           TFMobileBertForQuestionAnswering,
           TFMobileBertForSequenceClassification,
           TFMobileBertForTokenClassification,
           TFMobileBertModel,
       )

@require_tf
class 						UpperCAmelCase_ (    _lowercase			,      _lowercase			,      unittest.TestCase						):
     lowerCamelCase							:      Tuple            =	(
         (
             TFMobileBertModel,
             TFMobileBertForMaskedLM,
             TFMobileBertForNextSentencePrediction,
             TFMobileBertForPreTraining,
             TFMobileBertForQuestionAnswering,
             TFMobileBertForSequenceClassification,
             TFMobileBertForTokenClassification,
             TFMobileBertForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     lowerCamelCase							:      int            =	(
         {
             'feature-extraction': TFMobileBertModel,
             'fill-mask': TFMobileBertForMaskedLM,
             'question-answering': TFMobileBertForQuestionAnswering,
             'text-classification': TFMobileBertForSequenceClassification,
             'token-classification': TFMobileBertForTokenClassification,
             'zero-shot': TFMobileBertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     lowerCamelCase							:      int            =	False
     lowerCamelCase							:      Any            =	False





     def 				__UpperCAmelCase (					self			: Union[str, Any]       , UpperCAmelCase__			: List[str]       , UpperCAmelCase__			: List[str]       , UpperCAmelCase__			: Optional[int]=False       )							->    int:
        lowerCAmelCase	=     super()._prepare_for_class(__SCREAMING_SNAKE_CASE       , __SCREAMING_SNAKE_CASE       , return_labels=__SCREAMING_SNAKE_CASE       )

        if return_labels:
           if model_class in get_values(__SCREAMING_SNAKE_CASE       ):
              lowerCAmelCase	=     tf.zeros(self.model_tester.batch_size       , dtype=tf.intaa       )

        return inputs_dict

     class 						UpperCAmelCase_ (    _lowercase						):





          def __init__(					self			: Optional[Any]       , UpperCAmelCase__			: int       , UpperCAmelCase__			: Any=1_3       , UpperCAmelCase__			: Optional[int]=7       , UpperCAmelCase__			: Optional[Any]=True       , UpperCAmelCase__			: str=True       , UpperCAmelCase__			: Optional[Any]=True       , UpperCAmelCase__			: Any=True       , UpperCAmelCase__			: int=9_9       , UpperCAmelCase__			: Optional[Any]=3_2       , UpperCAmelCase__			: List[Any]=3_2       , UpperCAmelCase__			: Union[str, Any]=2       , UpperCAmelCase__			: Union[str, Any]=4       , UpperCAmelCase__			: Optional[int]=3_7       , UpperCAmelCase__			: Optional[Any]="gelu"       , UpperCAmelCase__			: Tuple=0.1       , UpperCAmelCase__			: int=0.1       , UpperCAmelCase__			: str=5_1_2       , UpperCAmelCase__			: Union[str, Any]=1_6       , UpperCAmelCase__			: Optional[int]=2       , UpperCAmelCase__			: Union[str, Any]=0.02       , UpperCAmelCase__			: Dict=3       , UpperCAmelCase__			: Union[str, Any]=4       , UpperCAmelCase__			: Dict=None       , )							->    Tuple:
             lowerCAmelCase	=     parent
             lowerCAmelCase	=     batch_size
             lowerCAmelCase	=     seq_length
             lowerCAmelCase	=     is_training
             lowerCAmelCase	=     use_input_mask
             lowerCAmelCase	=     use_token_type_ids
             lowerCAmelCase	=     use_labels
             lowerCAmelCase	=     vocab_size
             lowerCAmelCase	=     hidden_size
             lowerCAmelCase	=     num_hidden_layers
             lowerCAmelCase	=     num_attention_heads
             lowerCAmelCase	=     intermediate_size
             lowerCAmelCase	=     hidden_act
             lowerCAmelCase	=     hidden_dropout_prob
             lowerCAmelCase	=     attention_probs_dropout_prob
             lowerCAmelCase	=     max_position_embeddings
             lowerCAmelCase	=     type_vocab_size
             lowerCAmelCase	=     type_sequence_label_size
             lowerCAmelCase	=     initializer_range
             lowerCAmelCase	=     num_labels
             lowerCAmelCase	=     num_choices
             lowerCAmelCase	=     scope
             lowerCAmelCase	=     embedding_size





          def 				__UpperCAmelCase (					self			: str       )							->    Dict:
             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	=     MobileBertConfig(
                 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       , embedding_size=self.embedding_size       , )

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





          def 				__UpperCAmelCase (					self			: List[str]       , UpperCAmelCase__			: int       , UpperCAmelCase__			: Union[str, Any]       , UpperCAmelCase__			: Optional[int]       , UpperCAmelCase__			: Union[str, Any]       , UpperCAmelCase__			: Optional[Any]       , UpperCAmelCase__			: Union[str, Any]       , UpperCAmelCase__			: Any       )							->    Dict:
             lowerCAmelCase	=     TFMobileBertModel(config=__SCREAMING_SNAKE_CASE       )
             lowerCAmelCase	=     {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
             lowerCAmelCase	=     model(__SCREAMING_SNAKE_CASE       )

             lowerCAmelCase	=     [input_ids, input_mask]
             lowerCAmelCase	=     model(__SCREAMING_SNAKE_CASE       )

             lowerCAmelCase	=     model(__SCREAMING_SNAKE_CASE       )

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





          def 				__UpperCAmelCase (					self			: List[str]       , UpperCAmelCase__			: str       , UpperCAmelCase__			: List[str]       , UpperCAmelCase__			: str       , UpperCAmelCase__			: int       , UpperCAmelCase__			: Dict       , UpperCAmelCase__			: Dict       , UpperCAmelCase__			: str       )							->    Any:
             lowerCAmelCase	=     TFMobileBertForMaskedLM(config=__SCREAMING_SNAKE_CASE       )
             lowerCAmelCase	=     {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
             lowerCAmelCase	=     model(__SCREAMING_SNAKE_CASE       )
             self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.vocab_size)       )





          def 				__UpperCAmelCase (					self			: str       , UpperCAmelCase__			: Optional[int]       , UpperCAmelCase__			: str       , UpperCAmelCase__			: Any       , UpperCAmelCase__			: Optional[int]       , UpperCAmelCase__			: Tuple       , UpperCAmelCase__			: Dict       , UpperCAmelCase__			: Optional[int]       )							->    str:
             lowerCAmelCase	=     TFMobileBertForNextSentencePrediction(config=__SCREAMING_SNAKE_CASE       )
             lowerCAmelCase	=     {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
             lowerCAmelCase	=     model(__SCREAMING_SNAKE_CASE       )
             self.parent.assertEqual(result.logits.shape       , (self.batch_size, 2)       )





          def 				__UpperCAmelCase (					self			: str       , UpperCAmelCase__			: int       , UpperCAmelCase__			: int       , UpperCAmelCase__			: int       , UpperCAmelCase__			: int       , UpperCAmelCase__			: str       , UpperCAmelCase__			: Any       , UpperCAmelCase__			: List[str]       )							->    str:
             lowerCAmelCase	=     TFMobileBertForPreTraining(config=__SCREAMING_SNAKE_CASE       )
             lowerCAmelCase	=     {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
             lowerCAmelCase	=     model(__SCREAMING_SNAKE_CASE       )
             self.parent.assertEqual(
                 result.prediction_logits.shape       , (self.batch_size, self.seq_length, self.vocab_size)       )
             self.parent.assertEqual(result.seq_relationship_logits.shape       , (self.batch_size, 2)       )





          def 				__UpperCAmelCase (					self			: Optional[int]       , UpperCAmelCase__			: Any       , UpperCAmelCase__			: Dict       , UpperCAmelCase__			: Union[str, Any]       , UpperCAmelCase__			: Optional[int]       , UpperCAmelCase__			: Any       , UpperCAmelCase__			: int       , UpperCAmelCase__			: Union[str, Any]       )							->    Tuple:
             lowerCAmelCase	=     self.num_labels
             lowerCAmelCase	=     TFMobileBertForSequenceClassification(config=__SCREAMING_SNAKE_CASE       )
             lowerCAmelCase	=     {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
             lowerCAmelCase	=     model(__SCREAMING_SNAKE_CASE       )
             self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.num_labels)       )





          def 				__UpperCAmelCase (					self			: List[Any]       , UpperCAmelCase__			: int       , UpperCAmelCase__			: Union[str, Any]       , UpperCAmelCase__			: Optional[Any]       , UpperCAmelCase__			: List[Any]       , UpperCAmelCase__			: Any       , UpperCAmelCase__			: str       , UpperCAmelCase__			: Dict       )							->    Dict:
             lowerCAmelCase	=     self.num_choices
             lowerCAmelCase	=     TFMobileBertForMultipleChoice(config=__SCREAMING_SNAKE_CASE       )
             lowerCAmelCase	=     tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE       , 1       )       , (1, self.num_choices, 1)       )
             lowerCAmelCase	=     tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE       , 1       )       , (1, self.num_choices, 1)       )
             lowerCAmelCase	=     tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE       , 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(__SCREAMING_SNAKE_CASE       )
             self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.num_choices)       )





          def 				__UpperCAmelCase (					self			: int       , UpperCAmelCase__			: Tuple       , UpperCAmelCase__			: Optional[Any]       , UpperCAmelCase__			: int       , UpperCAmelCase__			: Dict       , UpperCAmelCase__			: Optional[int]       , UpperCAmelCase__			: Dict       , UpperCAmelCase__			: Any       )							->    Dict:
             lowerCAmelCase	=     self.num_labels
             lowerCAmelCase	=     TFMobileBertForTokenClassification(config=__SCREAMING_SNAKE_CASE       )
             lowerCAmelCase	=     {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
             lowerCAmelCase	=     model(__SCREAMING_SNAKE_CASE       )
             self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.num_labels)       )





          def 				__UpperCAmelCase (					self			: List[Any]       , UpperCAmelCase__			: Optional[int]       , UpperCAmelCase__			: Tuple       , UpperCAmelCase__			: Optional[int]       , UpperCAmelCase__			: str       , UpperCAmelCase__			: int       , UpperCAmelCase__			: Union[str, Any]       , UpperCAmelCase__			: Tuple       )							->    str:
             lowerCAmelCase	=     TFMobileBertForQuestionAnswering(config=__SCREAMING_SNAKE_CASE       )
             lowerCAmelCase	=     {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
             lowerCAmelCase	=     model(__SCREAMING_SNAKE_CASE       )
             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			: Optional[int]       )							->    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





     def 				__UpperCAmelCase (					self			: Any       )							->    Optional[Any]:
        lowerCAmelCase	=     TFMobileBertModelTest.TFMobileBertModelTester(self       )
        lowerCAmelCase	=     ConfigTester(self       , config_class=__SCREAMING_SNAKE_CASE       , hidden_size=3_7       )





     def 				__UpperCAmelCase (					self			: Dict       )							->    Optional[int]:
        self.config_tester.run_common_tests()





     def 				__UpperCAmelCase (					self			: str       )							->    Optional[Any]:
        lowerCAmelCase	=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_model(*__SCREAMING_SNAKE_CASE       )





     def 				__UpperCAmelCase (					self			: Dict       )							->    Tuple:
        lowerCAmelCase	=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_masked_lm(*__SCREAMING_SNAKE_CASE       )





     def 				__UpperCAmelCase (					self			: Optional[Any]       )							->    Any:
        lowerCAmelCase	=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__SCREAMING_SNAKE_CASE       )





     def 				__UpperCAmelCase (					self			: Optional[Any]       )							->    str:
        lowerCAmelCase	=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__SCREAMING_SNAKE_CASE       )





     def 				__UpperCAmelCase (					self			: Dict       )							->    Tuple:
        lowerCAmelCase	=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_pretraining(*__SCREAMING_SNAKE_CASE       )





     def 				__UpperCAmelCase (					self			: Optional[int]       )							->    Any:
        lowerCAmelCase	=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_question_answering(*__SCREAMING_SNAKE_CASE       )





     def 				__UpperCAmelCase (					self			: List[str]       )							->    Union[str, Any]:
        lowerCAmelCase	=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__SCREAMING_SNAKE_CASE       )





     def 				__UpperCAmelCase (					self			: Any       )							->    Optional[int]:
        lowerCAmelCase	=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_token_classification(*__SCREAMING_SNAKE_CASE       )





     @slow
     def 				__UpperCAmelCase (					self			: int       )							->    Optional[int]:



        for model_name in ["google/mobilebert-uncased"]:
           lowerCAmelCase	=     TFMobileBertModel.from_pretrained(__SCREAMING_SNAKE_CASE       )
           self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )




@require_tf
class 						UpperCAmelCase_ (    unittest.TestCase						):





     @slow
     def 				__UpperCAmelCase (					self			: List[Any]       )							->    str:
        lowerCAmelCase	=     TFMobileBertForPreTraining.from_pretrained('google/mobilebert-uncased'       )
        lowerCAmelCase	=     tf.constant([[0, 1, 2, 3, 4, 5]]       )
        lowerCAmelCase	=     model(__SCREAMING_SNAKE_CASE       )[0]

        lowerCAmelCase	=     [1, 6, 3_0_5_2_2]
        self.assertEqual(output.shape       , __SCREAMING_SNAKE_CASE       )

        lowerCAmelCase	=     tf.constant(
            [
                [
                    [-4.5_919_547, -9.248_295, -9.645_256],
                    [-6.7_306_175, -6.440_284, -6.6_052_837],
                    [-7.2_743_506, -6.7_847_915, -6.024_673],
                ]
            ]       )
        tf.debugging.assert_near(output[:, :3, :3]       , __SCREAMING_SNAKE_CASE       , atol=1E-4       )







"""simple docstring"""




from __future__ import annotations



def     __magic_name__		(					UpperCamelCase     :		str							,		UpperCamelCase     :		list[str] | None = None     )     ->						list[list[str]]:
    a__		=			word_bank or []
    # create a table
    a__		=			len(UpperCamelCase     ) + 1

    a__		=			[]
    for _ in range(UpperCamelCase     ):
        table.append([]     )
    # seed value
    a__		=			[[]]  # because empty string has empty combination

    # iterate through the indices
    for i in range(UpperCamelCase     ):
        # condition
        if table[i] != []:
            for word in word_bank:
                # slice condition
                if target[i : i + len(UpperCamelCase     )] == word:
                    a__		=			[
                        [word, *way] for way in table[i]
                    ]
                    # adds the word to every combination the current position holds
                    # now,push that combination to the table[i+len(word)]
                    table[i + len(UpperCamelCase     )] += new_combinations

    # combinations are in reverse order so reverse for better output
    for combination in table[len(UpperCamelCase     )]:
        combination.reverse()

    return table[len(UpperCamelCase     )]


if __name__ == "__main__":
    print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
    print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
    print(
        all_construct(
            'hexagonosaurus',
            ['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
        )
    )


