

'''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     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  Union[str, Any]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  List[Any]=1_3      ,lowercase__     :  Union[str, Any]=7      ,lowercase__     :  List[Any]=True      ,lowercase__     :  str=True      ,lowercase__     :  Any=True      ,lowercase__     :  Union[str, Any]=True      ,lowercase__     :  Tuple=9_9      ,lowercase__     :  List[str]=[1, 1, 2]      ,lowercase__     :  List[Any]=1      ,lowercase__     :  List[Any]=3_2      ,lowercase__     :  int=4      ,lowercase__     :  Tuple=8      ,lowercase__     :  Tuple=3_7      ,lowercase__     :  str="gelu_new"      ,lowercase__     :  Dict=0.1      ,lowercase__     :  Optional[int]=0.1      ,lowercase__     :  Optional[Any]=0.0      ,lowercase__     :  Tuple=5_1_2      ,lowercase__     :  Any=3      ,lowercase__     :  List[str]=0.0_2      ,lowercase__     :  List[Any]=3      ,lowercase__     :  List[Any]=4      ,lowercase__     :  Optional[Any]=None      ,lowercase__     :  Tuple=False      ,):
   __lowercase													=				parent
   __lowercase													=				batch_size
   __lowercase													=				seq_length
   __lowercase													=				is_training
   __lowercase													=				use_input_mask
   __lowercase													=				use_token_type_ids
   __lowercase													=				use_labels
   __lowercase													=				vocab_size
   __lowercase													=				block_sizes
   __lowercase													=				num_decoder_layers
   __lowercase													=				d_model
   __lowercase													=				n_head
   __lowercase													=				d_head
   __lowercase													=				d_inner
   __lowercase													=				hidden_act
   __lowercase													=				hidden_dropout
   __lowercase													=				attention_dropout
   __lowercase													=				activation_dropout
   __lowercase													=				max_position_embeddings
   __lowercase													=				type_vocab_size
   __lowercase													=				2
   __lowercase													=				num_labels
   __lowercase													=				num_choices
   __lowercase													=				scope
   __lowercase													=				initializer_std

   # Used in the tests to check the size of the first attention layer
   __lowercase													=				n_head
   # Used in the tests to check the size of the first hidden state
   __lowercase													=				self.d_model
   # Used in the tests to check the number of output hidden states/attentions
   __lowercase													=				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:
    __lowercase													=				self.num_hidden_layers + 2






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase													=				ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size    )

   __lowercase													=				None
   if self.use_input_mask:
    __lowercase													=				random_attention_mask([self.batch_size, self.seq_length]    )

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

   __lowercase													=				None
   __lowercase													=				None
   __lowercase													=				None
   if self.use_labels:
    __lowercase													=				ids_tensor([self.batch_size]      ,self.type_sequence_label_size    )
    __lowercase													=				ids_tensor([self.batch_size, self.seq_length]      ,self.num_labels    )
    __lowercase													=				ids_tensor([self.batch_size]      ,self.num_choices    )

   __lowercase													=				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       SCREAMING_SNAKE_CASE   (   self     :  int      ,lowercase__     :  List[str]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  int      ,lowercase__     :  List[str]      ,lowercase__     :  Optional[int]      ,lowercase__     :  List[Any]      ,lowercase__     :  List[str]      ,):
   __lowercase													=				TFFunnelModel(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )

   __lowercase													=				[input_ids, input_mask]
   __lowercase													=				model(lowercase__    )

   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.d_model)    )

   __lowercase													=				False
   __lowercase													=				TFFunnelModel(config=lowercase__    )
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.d_model)    )

   __lowercase													=				False
   __lowercase													=				TFFunnelModel(config=lowercase__    )
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.d_model)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int      ,lowercase__     :  List[Any]      ,lowercase__     :  Tuple      ,lowercase__     :  Dict      ,lowercase__     :  Any      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  Dict      ,):
   __lowercase													=				TFFunnelBaseModel(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )

   __lowercase													=				[input_ids, input_mask]
   __lowercase													=				model(lowercase__    )

   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, 2, self.d_model)    )

   __lowercase													=				False
   __lowercase													=				TFFunnelBaseModel(config=lowercase__    )
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, 3, self.d_model)    )

   __lowercase													=				False
   __lowercase													=				TFFunnelBaseModel(config=lowercase__    )
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, 2, self.d_model)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  Any      ,lowercase__     :  Any      ,lowercase__     :  List[Any]      ,lowercase__     :  Optional[int]      ,lowercase__     :  Dict      ,lowercase__     :  int      ,lowercase__     :  List[str]      ,):
   __lowercase													=				TFFunnelForPreTraining(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Dict      ,lowercase__     :  List[Any]      ,lowercase__     :  Tuple      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Tuple      ,):
   __lowercase													=				TFFunnelForMaskedLM(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.vocab_size)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  Tuple      ,lowercase__     :  Optional[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  str      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  Tuple      ,):
   __lowercase													=				self.num_labels
   __lowercase													=				TFFunnelForSequenceClassification(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  List[str]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  List[str]      ,lowercase__     :  List[str]      ,lowercase__     :  List[str]      ,lowercase__     :  str      ,lowercase__     :  Tuple      ,):
   __lowercase													=				self.num_choices
   __lowercase													=				TFFunnelForMultipleChoice(config=lowercase__    )
   __lowercase													=				tf.tile(tf.expand_dims(lowercase__      ,1    )      ,(1, self.num_choices, 1)    )
   __lowercase													=				tf.tile(tf.expand_dims(lowercase__      ,1    )      ,(1, self.num_choices, 1)    )
   __lowercase													=				tf.tile(tf.expand_dims(lowercase__      ,1    )      ,(1, self.num_choices, 1)    )
   __lowercase													=				{
       '''input_ids''': multiple_choice_inputs_ids,
       '''attention_mask''': multiple_choice_input_mask,
       '''token_type_ids''': multiple_choice_token_type_ids,
   }
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_choices)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  List[str]      ,lowercase__     :  List[Any]      ,lowercase__     :  Dict      ,lowercase__     :  Any      ,lowercase__     :  Union[str, Any]      ,):
   __lowercase													=				self.num_labels
   __lowercase													=				TFFunnelForTokenClassification(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.num_labels)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Optional[int]      ,lowercase__     :  Dict      ,lowercase__     :  List[str]      ,lowercase__     :  str      ,lowercase__     :  Tuple      ,lowercase__     :  Any      ,):
   __lowercase													=				TFFunnelForQuestionAnswering(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )
   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       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.prepare_config_and_inputs()
   (
       (
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			
   )													=				config_and_inputs
   __lowercase													=				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
   return config, inputs_dict






@require_tf
class     lowercase_			(lowerCamelCase__       , lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						int           =    (
      (
          TFFunnelModel,
          TFFunnelForMaskedLM,
          TFFunnelForPreTraining,
          TFFunnelForQuestionAnswering,
          TFFunnelForTokenClassification,
      )
      if is_tf_available()
      else ()
  )
  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    (
      {
          'feature-extraction': (TFFunnelBaseModel, TFFunnelModel),
          'fill-mask': TFFunnelForMaskedLM,
          'question-answering': TFFunnelForQuestionAnswering,
          'text-classification': TFFunnelForSequenceClassification,
          'token-classification': TFFunnelForTokenClassification,
          'zero-shot': TFFunnelForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  SCREAMING_SNAKE_CASE					:						Tuple           =    False
  SCREAMING_SNAKE_CASE					:						Any           =    False






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				TFFunnelModelTester(self    )
   __lowercase													=				ConfigTester(self      ,config_class=lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   self.config_tester.run_common_tests()






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_pretraining(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*lowercase__    )






@require_tf
class     lowercase_			(lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						List[Any]           =    (
      (TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
  )
  SCREAMING_SNAKE_CASE					:						Dict           =    False
  SCREAMING_SNAKE_CASE					:						List[str]           =    False






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				TFFunnelModelTester(self      ,base=lowercase__    )
   __lowercase													=				ConfigTester(self      ,config_class=lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   self.config_tester.run_common_tests()






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_base_model(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_sequence_classification(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_multiple_choice(*lowercase__    )




'''simple docstring'''






def 	_A  (					A__ = 50   ):






 """simple docstring"""



 __lowercase													=				[1] * (length + 1)

 for row_length in range(3  ,       length + 1   ):
  for block_length in range(3  ,       row_length + 1   ):
   for block_start in range(row_length - block_length   ):
    ways_number[row_length] += ways_number[
        row_length - block_start - block_length - 1
    ]

   ways_number[row_length] += 1

 return ways_number[length]


if __name__ == "__main__":
 print(f'{solution() = }')

