



"""simple docstring"""






def 					snake_case__       (		__lowerCamelCase    :   int							,		__lowerCamelCase    :   list[int]							,		__lowerCamelCase    :   int     ):
			"""simple docstring"""

			def count_of_possible_combinations(__lowerCamelCase    :   int     ) -> int:
						if target < 0:
									return 0
						if target == 0:
									return 1
						return sum(count_of_possible_combinations(target - item     ) for item in array     )

			return count_of_possible_combinations(__lowerCamelCase     )






def 					snake_case__       (		__lowerCamelCase    :   int							,		__lowerCamelCase    :   list[int]							,		__lowerCamelCase    :   int     ):
			"""simple docstring"""

			def count_of_possible_combinations_with_dp_array(
			    __lowerCamelCase    :   int							,		__lowerCamelCase    :   list[int]     ) -> int:
						if target < 0:
									return 0
						if target == 0:
									return 1
						if dp_array[target] != -1:
									return dp_array[target]
						lowerCamelCase__		:		List[Any]		     =sum(
						    count_of_possible_combinations_with_dp_array(target - item							,		__lowerCamelCase     )
						    for item in array     )
						lowerCamelCase__		:		List[Any]		     =answer
						return answer

			lowerCamelCase__		:		Any		     =[-1] * (target + 1)
			return count_of_possible_combinations_with_dp_array(__lowerCamelCase							,		__lowerCamelCase     )






def 					snake_case__       (		__lowerCamelCase    :   int							,		__lowerCamelCase    :   list[int]							,		__lowerCamelCase    :   int     ):
			"""simple docstring"""
			lowerCamelCase__		:		Tuple		     =[0] * (target + 1)
			lowerCamelCase__		:		List[Any]		     =1

			for i in range(1							,		target + 1     ):
						for j in range(__lowerCamelCase     ):
									if i - array[j] >= 0:
												dp_array[i] += dp_array[i - array[j]]

			return dp_array[target]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		_lowercase			:			List[str]										=						3
		_lowercase			:			Any										=						5
		_lowercase			:			List[str]										=						[1, 2, 5]
		print(combination_sum_iv(n, array, target))









"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf

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 (
		    TFFunnelBaseModel,
		    TFFunnelForMaskedLM,
		    TFFunnelForMultipleChoice,
		    TFFunnelForPreTraining,
		    TFFunnelForQuestionAnswering,
		    TFFunnelForSequenceClassification,
		    TFFunnelForTokenClassification,
		    TFFunnelModel,
		)

class 	__SCREAMING_SNAKE_CASE     :


					'''simple docstring'''





					def __init__(	self							:  Dict,  lowerCamelCase							:  str,  lowerCamelCase							:  Dict=13,  lowerCamelCase							:  Optional[Any]=7,  lowerCamelCase							:  List[Any]=True,  lowerCamelCase							:  Dict=True,  lowerCamelCase							:  List[Any]=True,  lowerCamelCase							:  Optional[int]=True,  lowerCamelCase							:  int=99,  lowerCamelCase							:  Optional[int]=[1, 1, 2],  lowerCamelCase							:  str=1,  lowerCamelCase							:  List[Any]=32,  lowerCamelCase							:  str=4,  lowerCamelCase							:  Dict=8,  lowerCamelCase							:  List[Any]=37,  lowerCamelCase							:  Optional[int]="gelu_new",  lowerCamelCase							:  Union[str, Any]=0.1,  lowerCamelCase							:  List[Any]=0.1,  lowerCamelCase							:  List[Any]=0.0,  lowerCamelCase							:  Dict=512,  lowerCamelCase							:  Dict=3,  lowerCamelCase							:  str=0.02,  lowerCamelCase							:  str=3,  lowerCamelCase							:  Optional[int]=4,  lowerCamelCase							:  List[str]=None,  lowerCamelCase							:  Tuple=False,  )-> Union[str, Any]:
								lowerCamelCase__		:		int		     =parent
								lowerCamelCase__		:		Dict		     =batch_size
								lowerCamelCase__		:		Dict		     =seq_length
								lowerCamelCase__		:		Any		     =is_training
								lowerCamelCase__		:		int		     =use_input_mask
								lowerCamelCase__		:		Tuple		     =use_token_type_ids
								lowerCamelCase__		:		int		     =use_labels
								lowerCamelCase__		:		Tuple		     =vocab_size
								lowerCamelCase__		:		Union[str, Any]		     =block_sizes
								lowerCamelCase__		:		Any		     =num_decoder_layers
								lowerCamelCase__		:		Optional[Any]		     =d_model
								lowerCamelCase__		:		List[str]		     =n_head
								lowerCamelCase__		:		List[Any]		     =d_head
								lowerCamelCase__		:		Dict		     =d_inner
								lowerCamelCase__		:		Dict		     =hidden_act
								lowerCamelCase__		:		List[str]		     =hidden_dropout
								lowerCamelCase__		:		Union[str, Any]		     =attention_dropout
								lowerCamelCase__		:		Union[str, Any]		     =activation_dropout
								lowerCamelCase__		:		Dict		     =max_position_embeddings
								lowerCamelCase__		:		Dict		     =type_vocab_size
								lowerCamelCase__		:		Union[str, Any]		     =2
								lowerCamelCase__		:		Optional[int]		     =num_labels
								lowerCamelCase__		:		List[str]		     =num_choices
								lowerCamelCase__		:		Tuple		     =scope
								lowerCamelCase__		:		Optional[int]		     =initializer_std

								# Used in the tests to check the size of the first attention layer
								lowerCamelCase__		:		List[str]		     =n_head
								# Used in the tests to check the size of the first hidden state
								lowerCamelCase__		:		Tuple		     =self.d_model
								# Used in the tests to check the number of output hidden states/attentions
								lowerCamelCase__		:		List[Any]		     =sum(self.block_sizes					) + (0 if base else self.num_decoder_layers)
								# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
								# the last hidden state of the first block (which is the first hidden state of the decoder).
								if not base:
											lowerCamelCase__		:		Union[str, Any]		     =self.num_hidden_layers + 2



					def 		snake_case	(	self							:  int					)-> List[Any]:
								lowerCamelCase__		:		Dict		     =ids_tensor([self.batch_size, self.seq_length],  self.vocab_size					)

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

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

								lowerCamelCase__		:		List[str]		     =None
								lowerCamelCase__		:		Union[str, Any]		     =None
								lowerCamelCase__		:		List[str]		     =None
								if self.use_labels:
											lowerCamelCase__		:		List[Any]		     =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]		     =FunnelConfig(
								    vocab_size=self.vocab_size,  block_sizes=self.block_sizes,  num_decoder_layers=self.num_decoder_layers,  d_model=self.d_model,  n_head=self.n_head,  d_head=self.d_head,  d_inner=self.d_inner,  hidden_act=self.hidden_act,  hidden_dropout=self.hidden_dropout,  attention_dropout=self.attention_dropout,  activation_dropout=self.activation_dropout,  max_position_embeddings=self.max_position_embeddings,  type_vocab_size=self.type_vocab_size,  initializer_std=self.initializer_std,  )

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



					def 		snake_case	(	self							:  List[Any],  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  int,  lowerCamelCase							:  int,  lowerCamelCase							:  str,  lowerCamelCase							:  List[str],  lowerCamelCase							:  Dict,  )-> Union[str, Any]:
								lowerCamelCase__		:		Tuple		     =TFFunnelModel(config=lowerCamelCase					)
								lowerCamelCase__		:		Dict		     ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
								lowerCamelCase__		:		Tuple		     =model(lowerCamelCase					)

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

								lowerCamelCase__		:		Any		     =model(lowerCamelCase					)
								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, self.seq_length, self.d_model)					)

								lowerCamelCase__		:		int		     =False
								lowerCamelCase__		:		Any		     =TFFunnelModel(config=lowerCamelCase					)
								lowerCamelCase__		:		str		     =model(lowerCamelCase					)
								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, self.seq_length, self.d_model)					)

								lowerCamelCase__		:		Dict		     =False
								lowerCamelCase__		:		Optional[int]		     =TFFunnelModel(config=lowerCamelCase					)
								lowerCamelCase__		:		Tuple		     =model(lowerCamelCase					)
								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, self.seq_length, self.d_model)					)



					def 		snake_case	(	self							:  Tuple,  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  str,  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  Tuple,  lowerCamelCase							:  Dict,  )-> Optional[Any]:
								lowerCamelCase__		:		List[str]		     =TFFunnelBaseModel(config=lowerCamelCase					)
								lowerCamelCase__		:		str		     ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
								lowerCamelCase__		:		Union[str, Any]		     =model(lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =[input_ids, input_mask]
								lowerCamelCase__		:		Any		     =model(lowerCamelCase					)

								lowerCamelCase__		:		Optional[Any]		     =model(lowerCamelCase					)
								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, 2, self.d_model)					)

								lowerCamelCase__		:		List[Any]		     =False
								lowerCamelCase__		:		Dict		     =TFFunnelBaseModel(config=lowerCamelCase					)
								lowerCamelCase__		:		int		     =model(lowerCamelCase					)
								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, 3, self.d_model)					)

								lowerCamelCase__		:		Union[str, Any]		     =False
								lowerCamelCase__		:		Optional[Any]		     =TFFunnelBaseModel(config=lowerCamelCase					)
								lowerCamelCase__		:		str		     =model(lowerCamelCase					)
								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, 2, self.d_model)					)



					def 		snake_case	(	self							:  str,  lowerCamelCase							:  Dict,  lowerCamelCase							:  Dict,  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Dict,  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  List[Any],  )-> List[Any]:
								lowerCamelCase__		:		List[str]		     =TFFunnelForPreTraining(config=lowerCamelCase					)
								lowerCamelCase__		:		List[Any]		     ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
								lowerCamelCase__		:		Union[str, Any]		     =model(lowerCamelCase					)
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length)					)



					def 		snake_case	(	self							:  str,  lowerCamelCase							:  Tuple,  lowerCamelCase							:  str,  lowerCamelCase							:  List[Any],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  str,  lowerCamelCase							:  Tuple,  lowerCamelCase							:  int,  )-> List[Any]:
								lowerCamelCase__		:		Union[str, Any]		     =TFFunnelForMaskedLM(config=lowerCamelCase					)
								lowerCamelCase__		:		Tuple		     ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
								lowerCamelCase__		:		List[Any]		     =model(lowerCamelCase					)
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.vocab_size)					)



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



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



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



					def 		snake_case	(	self							:  Optional[int],  lowerCamelCase							:  Dict,  lowerCamelCase							:  str,  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  str,  lowerCamelCase							:  Optional[int],  )-> Tuple:
								lowerCamelCase__		:		Tuple		     =TFFunnelForQuestionAnswering(config=lowerCamelCase					)
								lowerCamelCase__		:		Union[str, Any]		     ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
								lowerCamelCase__		:		Optional[int]		     =model(lowerCamelCase					)
								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 		snake_case	(	self							:  int					)-> List[str]:
								lowerCamelCase__		:		List[Any]		     =self.prepare_config_and_inputs()
								(
								    (
								    lowerCamelCase__
								)     ,       (
								    lowerCamelCase__
								)     ,       (
								    lowerCamelCase__
								)     ,       (
								    lowerCamelCase__
								)     ,       (
								    lowerCamelCase__
								)     ,       (
								    lowerCamelCase__
								)     ,       (
								    lowerCamelCase__
								)     ,       
								)		:		Tuple		     =config_and_inputs
								lowerCamelCase__		:		str		     ={'''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       ):


					'''simple docstring'''





					_a             =							(
					    (
					        TFFunnelModel,
					        TFFunnelForMaskedLM,
					        TFFunnelForPreTraining,
					        TFFunnelForQuestionAnswering,
					        TFFunnelForTokenClassification,
					    )
					    if is_tf_available()
					    else ()
					)
					_a             =							(
					    {
					        'feature-extraction': (TFFunnelBaseModel, TFFunnelModel),
					        'fill-mask': TFFunnelForMaskedLM,
					        'question-answering': TFFunnelForQuestionAnswering,
					        'text-classification': TFFunnelForSequenceClassification,
					        'token-classification': TFFunnelForTokenClassification,
					        'zero-shot': TFFunnelForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					_a             =							False
					_a             =							False



					def 		snake_case	(	self							:  str					)-> Tuple:
								lowerCamelCase__		:		Any		     =TFFunnelModelTester(self					)
								lowerCamelCase__		:		Any		     =ConfigTester(self,  config_class=lowerCamelCase					)



					def 		snake_case	(	self							:  List[str]					)-> Tuple:
								self.config_tester.run_common_tests()



					def 		snake_case	(	self							:  str					)-> List[Any]:
								lowerCamelCase__		:		Optional[Any]		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase					)



					def 		snake_case	(	self							:  str					)-> Dict:
								lowerCamelCase__		:		int		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*lowerCamelCase					)



					def 		snake_case	(	self							:  int					)-> List[Any]:
								lowerCamelCase__		:		Tuple		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase					)



					def 		snake_case	(	self							:  Dict					)-> Any:
								lowerCamelCase__		:		str		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*lowerCamelCase					)




					def 		snake_case	(	self							:  Tuple					)-> Optional[Any]:
								lowerCamelCase__		:		Optional[Any]		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*lowerCamelCase					)





@require_tf
class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_		,       unittest.TestCase       ):


					'''simple docstring'''





					_a             =							(
					    (TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
					)
					_a             =							False
					_a             =							False



					def 		snake_case	(	self							:  int					)-> Tuple:
								lowerCamelCase__		:		Union[str, Any]		     =TFFunnelModelTester(self,  base=lowerCamelCase					)
								lowerCamelCase__		:		Tuple		     =ConfigTester(self,  config_class=lowerCamelCase					)



					def 		snake_case	(	self							:  Any					)-> Any:
								self.config_tester.run_common_tests()



					def 		snake_case	(	self							:  Optional[Any]					)-> Optional[Any]:
								lowerCamelCase__		:		Any		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_base_model(*lowerCamelCase					)



					def 		snake_case	(	self							:  Union[str, Any]					)-> int:
								lowerCamelCase__		:		Tuple		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase					)




					def 		snake_case	(	self							:  List[str]					)-> Optional[int]:
								lowerCamelCase__		:		List[Any]		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase					)




