





from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, 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 (
           TFConvBertForMaskedLM,
           TFConvBertForMultipleChoice,
           TFConvBertForQuestionAnswering,
           TFConvBertForSequenceClassification,
           TFConvBertForTokenClassification,
           TFConvBertModel,
       )


class        __SCREAMING_SNAKE_CASE      :







       def __init__(				self					:     Optional[int]		, A					:     str		, A					:     List[Any]=1_3		, A					:     Tuple=7		, A					:     List[Any]=True		, A					:     Dict=True		, A					:     List[str]=True		, A					:     str=True		, A					:     List[str]=9_9		, A					:     Optional[int]=3_2		, A					:     Union[str, Any]=2		, A					:     Optional[int]=4		, A					:     List[str]=3_7		, A					:     str="gelu"		, A					:     List[Any]=0.1		, A					:     Union[str, Any]=0.1		, A					:     Any=5_1_2		, A					:     Tuple=1_6		, A					:     int=2		, A					:     Any=0.02		, A					:     Any=3		, A					:     int=4		, A					:     List[Any]=None		, )       ->int:
          lowerCamelCase__       :						List[Any]        =							parent
          lowerCamelCase__       :						Optional[int]        =							1_3
          lowerCamelCase__       :						Optional[int]        =							7
          lowerCamelCase__       :						List[str]        =							True
          lowerCamelCase__       :						Any        =							True
          lowerCamelCase__       :						int        =							True
          lowerCamelCase__       :						Union[str, Any]        =							True
          lowerCamelCase__       :						str        =							9_9
          lowerCamelCase__       :						Any        =							3_8_4
          lowerCamelCase__       :						Union[str, Any]        =							2
          lowerCamelCase__       :						List[Any]        =							4
          lowerCamelCase__       :						Optional[int]        =							3_7
          lowerCamelCase__       :						List[str]        =							'''gelu'''
          lowerCamelCase__       :						Union[str, Any]        =							0.1
          lowerCamelCase__       :						Union[str, Any]        =							0.1
          lowerCamelCase__       :						int        =							5_1_2
          lowerCamelCase__       :						Dict        =							1_6
          lowerCamelCase__       :						Optional[int]        =							2
          lowerCamelCase__       :						Tuple        =							0.02
          lowerCamelCase__       :						List[str]        =							3
          lowerCamelCase__       :						int        =							4
          lowerCamelCase__       :						Optional[Any]        =							1_2_8
          lowerCamelCase__       :						str        =							2
          lowerCamelCase__       :						Dict        =							9
          lowerCamelCase__       :						Optional[Any]        =							1
          lowerCamelCase__       :						Dict        =							None







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Optional[Any]:
          lowerCamelCase__       :						Dict        =							ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size      )

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

          lowerCamelCase__       :						Any        =							None
          if self.use_token_type_ids:
             lowerCamelCase__       :						Union[str, Any]        =							ids_tensor([self.batch_size, self.seq_length]		, self.type_vocab_size      )

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

          lowerCamelCase__       :						Optional[int]        =							ConvBertConfig(
              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 						__lowerCamelCase   (				self					:     Dict		, A					:     List[str]		, A					:     Union[str, Any]		, A					:     int		, A					:     Dict		, A					:     Optional[int]		, A					:     Any		, A					:     int      )       ->Dict:
          lowerCamelCase__       :						List[str]        =							TFConvBertModel(config=A      )
          lowerCamelCase__       :						Tuple        =							{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

          lowerCamelCase__       :						Union[str, Any]        =							[input_ids, input_mask]
          lowerCamelCase__       :						Tuple        =							model(A      )

          lowerCamelCase__       :						List[str]        =							model(A      )

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







       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     Optional[Any]		, A					:     str		, A					:     List[str]		, A					:     List[Any]		, A					:     str		, A					:     Any		, A					:     Dict      )       ->Union[str, Any]:
          lowerCamelCase__       :						Optional[Any]        =							TFConvBertForMaskedLM(config=A      )
          lowerCamelCase__       :						List[Any]        =							{
              '''input_ids''': input_ids,
              '''attention_mask''': input_mask,
              '''token_type_ids''': token_type_ids,
          }
          lowerCamelCase__       :						str        =							model(A      )
          self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.seq_length, self.vocab_size)      )







       def 						__lowerCamelCase   (				self					:     str		, A					:     Optional[int]		, A					:     List[str]		, A					:     Any		, A					:     Optional[Any]		, A					:     Any		, A					:     Dict		, A					:     int      )       ->List[str]:
          lowerCamelCase__       :						int        =							self.num_labels
          lowerCamelCase__       :						int        =							TFConvBertForSequenceClassification(config=A      )
          lowerCamelCase__       :						int        =							{
              '''input_ids''': input_ids,
              '''attention_mask''': input_mask,
              '''token_type_ids''': token_type_ids,
          }

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







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







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







       def 						__lowerCamelCase   (				self					:     Optional[Any]		, A					:     List[Any]		, A					:     List[Any]		, A					:     int		, A					:     List[str]		, A					:     Tuple		, A					:     List[Any]		, A					:     Any      )       ->Any:
          lowerCamelCase__       :						Union[str, Any]        =							TFConvBertForQuestionAnswering(config=A      )
          lowerCamelCase__       :						str        =							{
              '''input_ids''': input_ids,
              '''attention_mask''': input_mask,
              '''token_type_ids''': token_type_ids,
          }

          lowerCamelCase__       :						str        =							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 						__lowerCamelCase   (				self					:     Dict      )       ->List[Any]:
          lowerCamelCase__       :						str        =							self.prepare_config_and_inputs()
          (
              (
              lowerCamelCase__
          )       ,							(
              lowerCamelCase__
          )       ,							(
              lowerCamelCase__
          )       ,							(
              lowerCamelCase__
          )       ,							(
              lowerCamelCase__
          )       ,							(
              lowerCamelCase__
          )       ,							(
              lowerCamelCase__
          )       ,							
          )       :						int        =							config_and_inputs
          lowerCamelCase__       :						Dict        =							{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
          return config, inputs_dict


@require_tf
class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  List[str]			   =   (
           (
               TFConvBertModel,
               TFConvBertForMaskedLM,
               TFConvBertForQuestionAnswering,
               TFConvBertForSequenceClassification,
               TFConvBertForTokenClassification,
               TFConvBertForMultipleChoice,
           )
           if is_tf_available()
           else ()
       )
       _UpperCAmelCase				:  Optional[Any]			   =   (
           {
               "feature-extraction": TFConvBertModel,
               "fill-mask": TFConvBertForMaskedLM,
               "question-answering": TFConvBertForQuestionAnswering,
               "text-classification": TFConvBertForSequenceClassification,
               "token-classification": TFConvBertForTokenClassification,
               "zero-shot": TFConvBertForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       _UpperCAmelCase				:  Union[str, Any]			   =   False
       _UpperCAmelCase				:  Any			   =   False
       _UpperCAmelCase				:  Optional[Any]			   =   False







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Any:
          lowerCamelCase__       :						List[str]        =							TFConvBertModelTester(self      )
          lowerCamelCase__       :						List[Any]        =							ConfigTester(self		, config_class=A		, hidden_size=3_7      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->List[str]:
          self.config_tester.run_common_tests()







       def 						__lowerCamelCase   (				self					:     int      )       ->List[str]:
          lowerCamelCase__       :						List[Any]        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A      )







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Union[str, Any]:
          lowerCamelCase__       :						Union[str, Any]        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*A      )







       def 						__lowerCamelCase   (				self					:     int      )       ->Any:
          lowerCamelCase__       :						List[Any]        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*A      )







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->str:
          lowerCamelCase__       :						Dict        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*A      )







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Any:
          lowerCamelCase__       :						List[Any]        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*A      )







       def 						__lowerCamelCase   (				self					:     Dict      )       ->List[Any]:
          lowerCamelCase__       :						Union[str, Any]        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*A      )







       @slow
       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Optional[int]:
          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							self.model_tester.prepare_config_and_inputs_for_common()
          lowerCamelCase__       :						int        =							True
          lowerCamelCase__       :						Any        =							True

          if hasattr(A		, '''use_cache'''      ):
             lowerCamelCase__       :						List[str]        =							True

          lowerCamelCase__       :						List[str]        =							getattr(self.model_tester		, '''encoder_seq_length'''		, self.model_tester.seq_length      )
          lowerCamelCase__       :						Optional[int]        =							getattr(self.model_tester		, '''key_length'''		, A      )

          for model_class in self.all_model_classes:
             lowerCamelCase__       :						Any        =							self._prepare_for_class(A		, A      )
             lowerCamelCase__       :						List[Any]        =							model_class(A      )
             lowerCamelCase__       :						Tuple        =							len(model(A      )      )

             with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(A		, saved_model=A      )
                lowerCamelCase__       :						List[Any]        =							os.path.join(A		, '''saved_model'''		, '''1'''      )
                lowerCamelCase__       :						Optional[int]        =							tf.keras.models.load_model(A      )
                lowerCamelCase__       :						str        =							model(A      )

                if self.is_encoder_decoder:
                   lowerCamelCase__       :						str        =							outputs['''encoder_hidden_states''']
                   lowerCamelCase__       :						str        =							outputs['''encoder_attentions''']
                else:
                   lowerCamelCase__       :						Optional[int]        =							outputs['''hidden_states''']
                   lowerCamelCase__       :						int        =							outputs['''attentions''']

                self.assertEqual(len(A      )		, A      )

                lowerCamelCase__       :						List[Any]        =							getattr(
                    self.model_tester		, '''expected_num_hidden_layers'''		, self.model_tester.num_hidden_layers + 1      )

                self.assertEqual(len(A      )		, A      )
                self.assertListEqual(
                    list(output_hidden_states[0].shape[-2:]      )		, [self.model_tester.seq_length, self.model_tester.hidden_size]		, )

                self.assertEqual(len(A      )		, self.model_tester.num_hidden_layers      )
                self.assertListEqual(
                    list(output_attentions[0].shape[-3:]      )		, [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]		, )







       @slow
       def 						__lowerCamelCase   (				self					:     Tuple      )       ->List[Any]:
          lowerCamelCase__       :						Union[str, Any]        =							TFConvBertModel.from_pretrained('''YituTech/conv-bert-base'''      )
          self.assertIsNotNone(A      )







       def 						__lowerCamelCase   (				self					:     str      )       ->Dict:
          lowerCamelCase__       ,							lowerCamelCase__       :						Optional[int]        =							self.model_tester.prepare_config_and_inputs_for_common()
          lowerCamelCase__       :						int        =							True
          lowerCamelCase__       :						Optional[Any]        =							getattr(self.model_tester		, '''decoder_seq_length'''		, self.model_tester.seq_length      )
          lowerCamelCase__       :						List[str]        =							getattr(self.model_tester		, '''encoder_seq_length'''		, self.model_tester.seq_length      )
          lowerCamelCase__       :						List[Any]        =							getattr(self.model_tester		, '''key_length'''		, A      )
          lowerCamelCase__       :						Dict        =							getattr(self.model_tester		, '''key_length'''		, A      )

          def check_decoder_attentions_output(A					:     List[str]      ):
             lowerCamelCase__       :						Union[str, Any]        =							len(A      )
             self.assertEqual(out_len % 2		, 0      )
             lowerCamelCase__       :						Optional[int]        =							outputs.decoder_attentions
             self.assertEqual(len(A      )		, self.model_tester.num_hidden_layers      )
             self.assertListEqual(
                 list(decoder_attentions[0].shape[-3:]      )		, [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]		, )

          def check_encoder_attentions_output(A					:     Optional[Any]      ):
             lowerCamelCase__       :						int        =							[
                 t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
             ]
             self.assertEqual(len(A      )		, self.model_tester.num_hidden_layers      )
             self.assertListEqual(
                 list(attentions[0].shape[-3:]      )		, [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]		, )

          for model_class in self.all_model_classes:
             lowerCamelCase__       :						int        =							True
             lowerCamelCase__       :						Dict        =							False
             lowerCamelCase__       :						Tuple        =							model_class(A      )
             lowerCamelCase__       :						Dict        =							model(self._prepare_for_class(A		, A      )      )
             lowerCamelCase__       :						Optional[int]        =							len(A      )
             self.assertEqual(config.output_hidden_states		, A      )
             check_encoder_attentions_output(A      )

             if self.is_encoder_decoder:
                lowerCamelCase__       :						List[Any]        =							model_class(A      )
                lowerCamelCase__       :						Tuple        =							model(self._prepare_for_class(A		, A      )      )
                self.assertEqual(config.output_hidden_states		, A      )
                check_decoder_attentions_output(A      )

             # Check that output attentions can also be changed via the config
             del inputs_dict["output_attentions"]
             lowerCamelCase__       :						Optional[int]        =							True
             lowerCamelCase__       :						Union[str, Any]        =							model_class(A      )
             lowerCamelCase__       :						Tuple        =							model(self._prepare_for_class(A		, A      )      )
             self.assertEqual(config.output_hidden_states		, A      )
             check_encoder_attentions_output(A      )

             # Check attention is always last and order is fine
             lowerCamelCase__       :						Optional[Any]        =							True
             lowerCamelCase__       :						Optional[Any]        =							True
             lowerCamelCase__       :						int        =							model_class(A      )
             lowerCamelCase__       :						Tuple        =							model(self._prepare_for_class(A		, A      )      )

             self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)		, len(A      )      )
             self.assertEqual(model.config.output_hidden_states		, A      )
             check_encoder_attentions_output(A      )


@require_tf
class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       @slow
       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Dict:
          lowerCamelCase__       :						Dict        =							TFConvBertModel.from_pretrained('''YituTech/conv-bert-base'''      )
          lowerCamelCase__       :						Dict        =							tf.constant([[0, 1, 2, 3, 4, 5]]      )
          lowerCamelCase__       :						int        =							model(A      )[0]

          lowerCamelCase__       :						int        =							[1, 6, 7_6_8]
          self.assertEqual(output.shape		, A      )

          lowerCamelCase__       :						Optional[Any]        =							tf.constant(
              [
                  [
                      [-0.03_47_54_93, -0.4_68_60_34, -0.30_63_88_32],
                      [0.22_63_72_48, -0.26_98_86_46, -0.7_42_34_24],
                      [0.10_32_48_68, -0.45_01_35_08, -0.58_28_07_84],
                  ]
              ]      )
          tf.debugging.assert_near(output[:, :3, :3]		, A		, atol=1e-4      )










from __future__ import annotations

_A :   List[str]  			=  '#'


class        __SCREAMING_SNAKE_CASE      :







       def __init__(				self					:     List[Any]      )       ->None:
          lowerCamelCase__       :						dict        =							{}







       def 						__lowerCamelCase   (				self					:     Union[str, Any]		, A					:     str      )       ->None:
          lowerCamelCase__       :						Any        =							self._trie
          for char in text:
             if char not in trie:
                lowerCamelCase__       :						Any        =							{}
             lowerCamelCase__       :						Any        =							trie[char]
          lowerCamelCase__       :						List[str]        =							True







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     str      )       ->tuple | list:
          lowerCamelCase__       :						Dict        =							self._trie
          for char in prefix:
             if char in trie:
                lowerCamelCase__       :						List[Any]        =							trie[char]
             else:
                return []
          return self._elements(A      )







       def 						__lowerCamelCase   (				self					:     Dict		, A					:     dict      )       ->tuple:
          lowerCamelCase__       :						Optional[Any]        =							[]
          for c, v in d.items():
             lowerCamelCase__       :						Any        =							[''' '''] if c == END else [(c + s) for s in self._elements(A      )]
             result.extend(A      )
          return tuple(A      )


_A :   str  			=  Trie()
_A :   List[Any]  			=  ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
       trie.insert_word(word)



def 	_a						(   UpperCAmelCase					)    ->    tuple:







   """simple docstring"""
   lowerCamelCase__       :						Optional[int]        =							trie.find_word(UpperCAmelCase					)
   return tuple(string + word for word in suffixes					)



def 	_a						(   )    ->    None:







   """simple docstring"""
   print(autocomplete_using_trie('''de'''					)					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()



