
'''simple docstring'''






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     __snake_case		:
					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=512,    A=16,    A=2,    A=0.02,    A=3,    A=4,    A=None,    ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= parent
									lowerCamelCase					:			Union[str, Any]									= 13
									lowerCamelCase					:			Optional[Any]									= 7
									lowerCamelCase					:			Dict									= True
									lowerCamelCase					:			Optional[int]									= True
									lowerCamelCase					:			Tuple									= True
									lowerCamelCase					:			Dict									= True
									lowerCamelCase					:			Any									= 99
									lowerCamelCase					:			int									= 384
									lowerCamelCase					:			str									= 2
									lowerCamelCase					:			Any									= 4
									lowerCamelCase					:			Union[str, Any]									= 37
									lowerCamelCase					:			int									= 'gelu'
									lowerCamelCase					:			str									= 0.1
									lowerCamelCase					:			Optional[int]									= 0.1
									lowerCamelCase					:			Union[str, Any]									= 512
									lowerCamelCase					:			Optional[Any]									= 16
									lowerCamelCase					:			str									= 2
									lowerCamelCase					:			Union[str, Any]									= 0.02
									lowerCamelCase					:			Optional[Any]									= 3
									lowerCamelCase					:			List[Any]									= 4
									lowerCamelCase					:			str									= 128
									lowerCamelCase					:			str									= 2
									lowerCamelCase					:			int									= 9
									lowerCamelCase					:			int									= 1
									lowerCamelCase					:			Optional[Any]									= None
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= ids_tensor([self.batch_size, self.seq_length],    self.vocab_size      )

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

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

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

									lowerCamelCase					:			List[str]									= 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     UpperCAmelCase_							(     self,    A,    A,    A,    A,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= TFConvBertModel(config=A      )
									lowerCamelCase					:			List[Any]									= {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

									lowerCamelCase					:			List[Any]									= [input_ids, input_mask]
									lowerCamelCase					:			Optional[Any]									= model(A      )

									lowerCamelCase					:			Dict									= 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      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= TFConvBertForMaskedLM(config=A      )
									lowerCamelCase					:			int									= {
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									lowerCamelCase					:			List[Any]									= 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      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.num_labels
									lowerCamelCase					:			Tuple									= TFConvBertForSequenceClassification(config=A      )
									lowerCamelCase					:			str									= {
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}

									lowerCamelCase					:			Any									= model(A      )
									self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.num_labels)      )
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A,    A,    A,    A      ):

									"""simple docstring"""


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

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.num_labels
									lowerCamelCase					:			int									= TFConvBertForTokenClassification(config=A      )
									lowerCamelCase					:			List[str]									= {
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									lowerCamelCase					:			Union[str, Any]									= 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      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= TFConvBertForQuestionAnswering(config=A      )
									lowerCamelCase					:			Union[str, Any]									= {
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}

									lowerCamelCase					:			Optional[int]									= 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      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= self.prepare_config_and_inputs()
									(
									    (
									    lowerCamelCase
									)			, (
									    lowerCamelCase
									)			, (
									    lowerCamelCase
									)			, (
									    lowerCamelCase
									)			, (
									    lowerCamelCase
									)			, (
									    lowerCamelCase
									)			, (
									    lowerCamelCase
									)			, 
									)					:			int									= config_and_inputs
									lowerCamelCase					:			Tuple									= {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
									return config, inputs_dict





@require_tf
class     __snake_case		(   a__    ,   a__    ,   unittest.TestCase):
					_lowerCAmelCase			     =	(
					    (
					        TFConvBertModel,
					        TFConvBertForMaskedLM,
					        TFConvBertForQuestionAnswering,
					        TFConvBertForSequenceClassification,
					        TFConvBertForTokenClassification,
					        TFConvBertForMultipleChoice,
					    )
					    if is_tf_available()
					    else ()
					)
					_lowerCAmelCase			     =	(
					    {
					        '''feature-extraction''': TFConvBertModel,
					        '''fill-mask''': TFConvBertForMaskedLM,
					        '''question-answering''': TFConvBertForQuestionAnswering,
					        '''text-classification''': TFConvBertForSequenceClassification,
					        '''token-classification''': TFConvBertForTokenClassification,
					        '''zero-shot''': TFConvBertForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					_lowerCAmelCase			     =	False
					_lowerCAmelCase			     =	False
					_lowerCAmelCase			     =	False
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= TFConvBertModelTester(self      )
									lowerCamelCase					:			List[Any]									= ConfigTester(self,    config_class=A,    hidden_size=37      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.config_tester.run_common_tests()
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_multiple_choice(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*A      )
					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase			, lowerCamelCase					:			str									= self.model_tester.prepare_config_and_inputs_for_common()
									lowerCamelCase					:			Union[str, Any]									= True
									lowerCamelCase					:			Optional[Any]									= True

									if hasattr(A,    'use_cache'      ):
													lowerCamelCase					:			Optional[Any]									= True

									lowerCamelCase					:			str									= getattr(self.model_tester,    'encoder_seq_length',    self.model_tester.seq_length      )
									lowerCamelCase					:			str									= getattr(self.model_tester,    'key_length',    A      )

									for model_class in self.all_model_classes:
													lowerCamelCase					:			List[str]									= self._prepare_for_class(A,    A      )
													lowerCamelCase					:			int									= model_class(A      )
													lowerCamelCase					:			str									= len(model(A      )      )

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

																	if self.is_encoder_decoder:
																					lowerCamelCase					:			List[str]									= outputs['encoder_hidden_states']
																					lowerCamelCase					:			Union[str, Any]									= outputs['encoder_attentions']
																	else:
																					lowerCamelCase					:			Any									= outputs['hidden_states']
																					lowerCamelCase					:			Optional[Any]									= 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     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= TFConvBertModel.from_pretrained('YituTech/conv-bert-base'      )
									self.assertIsNotNone(A      )




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase			, lowerCamelCase					:			int									= self.model_tester.prepare_config_and_inputs_for_common()
									lowerCamelCase					:			int									= True
									lowerCamelCase					:			Any									= getattr(self.model_tester,    'decoder_seq_length',    self.model_tester.seq_length      )
									lowerCamelCase					:			Tuple									= getattr(self.model_tester,    'encoder_seq_length',    self.model_tester.seq_length      )
									lowerCamelCase					:			Dict									= getattr(self.model_tester,    'key_length',    A      )
									lowerCamelCase					:			int									= getattr(self.model_tester,    'key_length',    A      )

									def check_decoder_attentions_output(A      ):
													lowerCamelCase					:			int									= len(A      )
													self.assertEqual(out_len % 2,    0      )
													lowerCamelCase					:			Tuple									= 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      ):
													lowerCamelCase					:			Dict									= [
													    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					:			Any									= True
													lowerCamelCase					:			Union[str, Any]									= False
													lowerCamelCase					:			Optional[int]									= model_class(A      )
													lowerCamelCase					:			List[str]									= model(self._prepare_for_class(A,    A      )      )
													lowerCamelCase					:			Dict									= len(A      )
													self.assertEqual(config.output_hidden_states,    A      )
													check_encoder_attentions_output(A      )

													if self.is_encoder_decoder:
																	lowerCamelCase					:			Union[str, Any]									= model_class(A      )
																	lowerCamelCase					:			Any									= 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					:			Union[str, Any]									= True
													lowerCamelCase					:			List[str]									= model_class(A      )
													lowerCamelCase					:			str									= 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					:			Tuple									= True
													lowerCamelCase					:			List[Any]									= True
													lowerCamelCase					:			str									= model_class(A      )
													lowerCamelCase					:			int									= 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     __snake_case		(   unittest.TestCase):
					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= TFConvBertModel.from_pretrained('YituTech/conv-bert-base'      )
									lowerCamelCase					:			Optional[int]									= tf.constant([[0, 1, 2, 3, 4, 5]]      )
									lowerCamelCase					:			int									= model(A      )[0]

									lowerCamelCase					:			int									= [1, 6, 768]
									self.assertEqual(output.shape,    A      )

									lowerCamelCase					:			int									= tf.constant(
									    [
									        [
									            [-0.0347_5493, -0.468_6034, -0.3063_8832],
									            [0.2263_7248, -0.2698_8646, -0.742_3424],
									            [0.1032_4868, -0.4501_3508, -0.5828_0784],
									        ]
									    ]      )
									tf.debugging.assert_near(output[:, :3, :3],    A,    atol=1e-4      )


'''simple docstring'''






from __future__ import annotations

A			      =      '#'




class     __snake_case		:
					def __init__(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			dict									= {}
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= self._trie
									for char in text:
													if char not in trie:
																	lowerCamelCase					:			Dict									= {}
													lowerCamelCase					:			Optional[int]									= trie[char]
									lowerCamelCase					:			Optional[Any]									= True
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= self._trie
									for char in prefix:
													if char in trie:
																	lowerCamelCase					:			int									= trie[char]
													else:
																	return []
									return self._elements(A      )




					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= []
									for c, v in d.items():
													lowerCamelCase					:			Optional[Any]									= [' '] if c == END else [(c + s) for s in self._elements(A      )]
													result.extend(A      )
									return tuple(A      )





A			      =      Trie()
A			      =      ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
					trie.insert_word(word)





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			Any									= trie.find_word(UpperCAmelCase__)
				return tuple(string + word for word in suffixes)





def    UpperCAmelCase							(  ):
				print(autocomplete_using_trie('de'))


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()
