'''simple docstring'''




import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      BioGptForCausalLM,
      BioGptForSequenceClassification,
      BioGptForTokenClassification,
      BioGptModel,
      BioGptTokenizer,
  )
  from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST

class 	lowerCamelCase_	:
      '''simple docstring'''

      def __init__(       self						:				List[Any]						,					A						:				Dict						,					A						:				Optional[Any]=13						,					A						:				Optional[Any]=7						,					A						:				Union[str, Any]=True						,					A						:				Optional[Any]=True						,					A						:				int=False						,					A						:				str=True						,					A						:				Optional[Any]=99						,					A						:				Union[str, Any]=32						,					A						:				int=5						,					A						:				Tuple=4						,					A						:				Union[str, Any]=37						,					A						:				Dict="gelu"						,					A						:				Union[str, Any]=0.1						,					A						:				str=0.1						,					A						:				Union[str, Any]=512						,					A						:				int=16						,					A						:				List[str]=2						,					A						:				Tuple=0.02						,					A						:				int=3						,					A						:				List[str]=4						,					A						:				str=None						,					):
             _UpperCAmelCase     :  List[Any]							      =    parent
             _UpperCAmelCase     :  Any							      =    batch_size
             _UpperCAmelCase     :  int							      =    seq_length
             _UpperCAmelCase     :  Union[str, Any]							      =    is_training
             _UpperCAmelCase     :  Any							      =    use_input_mask
             _UpperCAmelCase     :  Optional[Any]							      =    use_token_type_ids
             _UpperCAmelCase     :  str							      =    use_labels
             _UpperCAmelCase     :  Union[str, Any]							      =    vocab_size
             _UpperCAmelCase     :  Tuple							      =    hidden_size
             _UpperCAmelCase     :  Union[str, Any]							      =    num_hidden_layers
             _UpperCAmelCase     :  Optional[Any]							      =    num_attention_heads
             _UpperCAmelCase     :  Union[str, Any]							      =    intermediate_size
             _UpperCAmelCase     :  Union[str, Any]							      =    hidden_act
             _UpperCAmelCase     :  List[Any]							      =    hidden_dropout_prob
             _UpperCAmelCase     :  List[Any]							      =    attention_probs_dropout_prob
             _UpperCAmelCase     :  Optional[int]							      =    max_position_embeddings
             _UpperCAmelCase     :  str							      =    type_vocab_size
             _UpperCAmelCase     :  str							      =    type_sequence_label_size
             _UpperCAmelCase     :  int							      =    initializer_range
             _UpperCAmelCase     :  Optional[Any]							      =    num_labels
             _UpperCAmelCase     :  List[str]							      =    num_choices
             _UpperCAmelCase     :  List[str]							      =    scope




      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  Dict							      =    ids_tensor([self.batch_size, self.seq_length]						,					self.vocab_size       )

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

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

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

             _UpperCAmelCase     :  List[Any]							      =    self.get_config()

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




      def      _A	(       self						:				Dict       ):
             return BioGptConfig(
                 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						,					is_decoder=A						,					initializer_range=self.initializer_range						,					)




      def      _A	(       self						:				int						,					A						:				List[Any]						,					A						:				Any						,					A						:				int						,					A						:				Union[str, Any]						,					A						:				Dict						,					A						:				List[Any]						,					A						:				Dict       ):
             _UpperCAmelCase     :  List[str]							      =    BioGptModel(config=A       )
             model.to(A       )
             model.eval()
             _UpperCAmelCase     :  Tuple							      =    model(A						,					attention_mask=A       )
             _UpperCAmelCase     :  int							      =    model(A       )
             self.parent.assertEqual(result.last_hidden_state.shape						,					(self.batch_size, self.seq_length, self.hidden_size)       )




      def      _A	(       self						:				List[Any]						,					A						:				str						,					A						:				List[Any]						,					A						:				Dict						,					A						:				List[Any]						,					A						:				List[str]						,					A						:				Union[str, Any]						,					A						:				int						,					A						:				List[str]						,					A						:				Dict						,					):
             _UpperCAmelCase     :  Optional[int]							      =    BioGptForCausalLM(config=A       )
             model.to(A       )
             model.eval()
             _UpperCAmelCase     :  Optional[int]							      =    model(A						,					attention_mask=A						,					token_type_ids=A						,					labels=A       )
             self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.vocab_size)       )




      def      _A	(       self						:				List[Any]						,					A						:				str						,					A						:				str						,					A						:				str						,					A						:				Any						,					A						:				List[str]						,					*A						:				Optional[int]       ):
             _UpperCAmelCase     :  str							      =    BioGptModel(config=A       )
             model.to(A       )
             model.eval()

             # create attention mask
             _UpperCAmelCase     :  List[Any]							      =    torch.ones(input_ids.shape						,					dtype=torch.long						,					device=A       )
             _UpperCAmelCase     :  Optional[int]							      =    self.seq_length // 2
             _UpperCAmelCase     :  List[Any]							      =    0

             # first forward pass
             _UpperCAmelCase      ,  _UpperCAmelCase     :  List[str]							      =    model(A						,					attention_mask=A       ).to_tuple()

             # create hypothetical next token and extent to next_input_ids
             _UpperCAmelCase     :  List[str]							      =    ids_tensor((self.batch_size, 1)						,					config.vocab_size       )

             # change a random masked slice from input_ids
             _UpperCAmelCase     :  List[str]							      =    ids_tensor((1,)						,					A       ).item() + 1
             _UpperCAmelCase     :  str							      =    ids_tensor((self.batch_size, 1)						,					config.vocab_size       ).squeeze(-1       )
             _UpperCAmelCase     :  Any							      =    random_other_next_tokens

             # append to next input_ids and attn_mask
             _UpperCAmelCase     :  List[Any]							      =    torch.cat([input_ids, next_tokens]						,					dim=-1       )
             _UpperCAmelCase     :  Optional[int]							      =    torch.cat(
                 [attn_mask, torch.ones((attn_mask.shape[0], 1)						,					dtype=torch.long						,					device=A       )]						,					dim=1						,					)

             # get two different outputs
             _UpperCAmelCase     :  List[Any]							      =    model(A						,					attention_mask=A       )["last_hidden_state"]
             _UpperCAmelCase     :  Optional[Any]							      =    model(A						,					past_key_values=A						,					attention_mask=A       )["last_hidden_state"]

             # select random slice
             _UpperCAmelCase     :  Any							      =    ids_tensor((1,)						,					output_from_past.shape[-1]       ).item()
             _UpperCAmelCase     :  Optional[Any]							      =    output_from_no_past[:, -1, random_slice_idx].detach()
             _UpperCAmelCase     :  Any							      =    output_from_past[:, 0, random_slice_idx].detach()

             # test that outputs are equal for slice
             self.parent.assertTrue(torch.allclose(A						,					A						,					atol=1E-3       )       )




      def      _A	(       self						:				int						,					A						:				Dict						,					A						:				str						,					A						:				Dict						,					A						:				Union[str, Any]						,					A						:				Any						,					*A						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Optional[Any]							      =    BioGptModel(config=A       ).to(A       ).eval()

             _UpperCAmelCase     :  List[Any]							      =    torch.ones(input_ids.shape						,					dtype=torch.long						,					device=A       )

             # first forward pass
             _UpperCAmelCase     :  Union[str, Any]							      =    model(A						,					attention_mask=A						,					use_cache=A       )

             _UpperCAmelCase      ,  _UpperCAmelCase     :  Dict							      =    outputs.to_tuple()

             # create hypothetical multiple next token and extent to next_input_ids
             _UpperCAmelCase     :  str							      =    ids_tensor((self.batch_size, 3)						,					config.vocab_size       )
             _UpperCAmelCase     :  Any							      =    ids_tensor((self.batch_size, 3)						,					2       )

             # append to next input_ids and
             _UpperCAmelCase     :  Optional[Any]							      =    torch.cat([input_ids, next_tokens]						,					dim=-1       )
             _UpperCAmelCase     :  Dict							      =    torch.cat([attention_mask, next_attn_mask]						,					dim=-1       )

             _UpperCAmelCase     :  Any							      =    model(A						,					attention_mask=A       )["last_hidden_state"]
             _UpperCAmelCase     :  Dict							      =    model(A						,					attention_mask=A						,					past_key_values=A       )[
                 "last_hidden_state"
             ]

             # select random slice
             _UpperCAmelCase     :  Dict							      =    ids_tensor((1,)						,					output_from_past.shape[-1]       ).item()
             _UpperCAmelCase     :  Dict							      =    output_from_no_past[:, -3:, random_slice_idx].detach()
             _UpperCAmelCase     :  Any							      =    output_from_past[:, :, random_slice_idx].detach()

             self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]       )

             # test that outputs are equal for slice
             self.parent.assertTrue(torch.allclose(A						,					A						,					atol=1E-3       )       )




      def      _A	(       self						:				Optional[Any]						,					A						:				Tuple						,					A						:				List[str]						,					A						:				Tuple						,					A						:				Dict						,					A						:				List[Any]						,					*A						:				Tuple						,					A						:				List[str]=False       ):
             _UpperCAmelCase     :  Optional[int]							      =    BioGptForCausalLM(A       )
             model.to(A       )
             if gradient_checkpointing:
                    model.gradient_checkpointing_enable()

             _UpperCAmelCase     :  Union[str, Any]							      =    model(A						,					labels=A       )
             self.parent.assertEqual(result.loss.shape						,					()       )
             self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.vocab_size)       )
             result.loss.backward()




      def      _A	(       self						:				Optional[Any]						,					A						:				Any						,					*A						:				Optional[Any]       ):
             _UpperCAmelCase     :  Tuple							      =    BioGptModel(A       )
             _UpperCAmelCase     :  int							      =    model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers       )
             for key in model.state_dict().keys():
                    if "c_proj" in key and "weight" in key:
                           self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]       ) - model_std       )						,					0.001       )
                           self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]       ) - 0.0       )						,					0.01       )




      def      _A	(       self						:				Optional[int]						,					A						:				Dict						,					A						:				Tuple						,					A						:				Optional[int]						,					A						:				int						,					A						:				List[str]						,					*A						:				Dict       ):
             _UpperCAmelCase     :  Any							      =    self.num_labels
             _UpperCAmelCase     :  Any							      =    BioGptForTokenClassification(A       )
             model.to(A       )
             model.eval()
             _UpperCAmelCase     :  Optional[int]							      =    model(A						,					attention_mask=A						,					token_type_ids=A       )
             self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.num_labels)       )





      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Dict							      =    self.prepare_config_and_inputs()
             (
                 (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  
             )     :  List[str]							      =    config_and_inputs
             _UpperCAmelCase     :  Optional[int]							      =    {"input_ids": input_ids, "attention_mask": input_mask}
             return config, inputs_dict

@require_torch
class 	lowerCamelCase_	(snake_case__							, snake_case__							, snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  List[str]						=						(
          (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
          if is_torch_available()
          else ()
      )
      __UpperCamelCase:  List[str]						=						(BioGptForCausalLM,) if is_torch_available() else ()
      __UpperCamelCase:  str						=						(
          {
              "feature-extraction": BioGptModel,
              "text-classification": BioGptForSequenceClassification,
              "text-generation": BioGptForCausalLM,
              "token-classification": BioGptForTokenClassification,
              "zero-shot": BioGptForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      __UpperCamelCase:  Union[str, Any]						=						False




      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  List[Any]							      =    BioGptModelTester(self       )
             _UpperCAmelCase     :  str							      =    ConfigTester(self						,					config_class=A						,					hidden_size=37       )




      def      _A	(       self						:				Union[str, Any]       ):
             self.config_tester.run_common_tests()




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  Any							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*A       )




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  Optional[int]							      =    self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                    _UpperCAmelCase     :  Tuple							      =    type
                    self.model_tester.create_and_check_model(*A       )




      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Optional[int]							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_model_attention_mask_past(*A       )




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  List[Any]							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_forward_and_backwards(*A						,					gradient_checkpointing=A       )




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Dict							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_model_past_large_inputs(*A       )




      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase     :  Dict							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_weight_initialization(*A       )




      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase     :  Dict							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_for_token_classification(*A       )




      @slow
      def      _A	(       self						:				List[str]       ):
             _UpperCAmelCase     :  Optional[Any]							      =    BioGptForCausalLM.from_pretrained("microsoft/biogpt"       )
             model.to(A       )
             _UpperCAmelCase     :  Tuple							      =    BioGptTokenizer.from_pretrained("microsoft/biogpt"       )

             _UpperCAmelCase     :  str							      =    "left"

             # Define PAD Token = EOS Token = 50256
             _UpperCAmelCase     :  Any							      =    tokenizer.eos_token
             _UpperCAmelCase     :  int							      =    model.config.eos_token_id

             # use different length sentences to test batching
             _UpperCAmelCase     :  Any							      =    [
                 "Hello, my dog is a little",
                 "Today, I",
             ]

             _UpperCAmelCase     :  Tuple							      =    tokenizer(A						,					return_tensors="pt"						,					padding=A       )
             _UpperCAmelCase     :  Optional[Any]							      =    inputs["input_ids"].to(A       )

             _UpperCAmelCase     :  Any							      =    model.generate(
                 input_ids=A						,					attention_mask=inputs["attention_mask"].to(A       )						,					)

             _UpperCAmelCase     :  int							      =    tokenizer(sentences[0]						,					return_tensors="pt"       ).input_ids.to(A       )
             _UpperCAmelCase     :  List[Any]							      =    model.generate(input_ids=A       )

             _UpperCAmelCase     :  List[Any]							      =    inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
             _UpperCAmelCase     :  int							      =    tokenizer(sentences[1]						,					return_tensors="pt"       ).input_ids.to(A       )
             _UpperCAmelCase     :  int							      =    model.generate(input_ids=A						,					max_length=model.config.max_length - num_paddings       )

             _UpperCAmelCase     :  Dict							      =    tokenizer.batch_decode(A						,					skip_special_tokens=A       )
             _UpperCAmelCase     :  Any							      =    tokenizer.decode(output_non_padded[0]						,					skip_special_tokens=A       )
             _UpperCAmelCase     :  Optional[int]							      =    tokenizer.decode(output_padded[0]						,					skip_special_tokens=A       )

             _UpperCAmelCase     :  str							      =    [
                 "Hello, my dog is a little bit bigger than a little bit.",
                 "Today, I have a good idea of how to use the information",
             ]
             self.assertListEqual(A						,					A       )
             self.assertListEqual(A						,					[non_padded_sentence, padded_sentence]       )




      @slow
      def      _A	(       self						:				str       ):
             for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _UpperCAmelCase     :  Optional[Any]							      =    BioGptModel.from_pretrained(A       )
                    self.assertIsNotNone(A       )




      def      _A	(       self						:				List[str]       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  int							      =    self.model_tester.prepare_config_and_inputs_for_common()
             _UpperCAmelCase     :  str							      =    3
             _UpperCAmelCase     :  List[str]							      =    input_dict["input_ids"]
             _UpperCAmelCase     :  Dict							      =    input_ids.ne(1       ).to(A       )
             _UpperCAmelCase     :  List[Any]							      =    ids_tensor([self.model_tester.batch_size]						,					self.model_tester.type_sequence_label_size       )
             _UpperCAmelCase     :  List[str]							      =    BioGptForSequenceClassification(A       )
             model.to(A       )
             model.eval()
             _UpperCAmelCase     :  List[str]							      =    model(A						,					attention_mask=A						,					labels=A       )
             self.assertEqual(result.logits.shape						,					(self.model_tester.batch_size, self.model_tester.num_labels)       )





      def      _A	(       self						:				int       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  Optional[Any]							      =    self.model_tester.prepare_config_and_inputs_for_common()
             _UpperCAmelCase     :  int							      =    3
             _UpperCAmelCase     :  Dict							      =    "multi_label_classification"
             _UpperCAmelCase     :  Optional[Any]							      =    input_dict["input_ids"]
             _UpperCAmelCase     :  Optional[int]							      =    input_ids.ne(1       ).to(A       )
             _UpperCAmelCase     :  Tuple							      =    ids_tensor(
                 [self.model_tester.batch_size, config.num_labels]						,					self.model_tester.type_sequence_label_size       ).to(torch.float       )
             _UpperCAmelCase     :  Optional[Any]							      =    BioGptForSequenceClassification(A       )
             model.to(A       )
             model.eval()
             _UpperCAmelCase     :  Tuple							      =    model(A						,					attention_mask=A						,					labels=A       )
             self.assertEqual(result.logits.shape						,					(self.model_tester.batch_size, self.model_tester.num_labels)       )

@require_torch
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      @slow
      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Optional[Any]							      =    BioGptForCausalLM.from_pretrained("microsoft/biogpt"       )
             _UpperCAmelCase     :  List[str]							      =    torch.tensor([[2, 4805, 9, 656, 21]]       )
             _UpperCAmelCase     :  List[Any]							      =    model(A       )[0]

             _UpperCAmelCase     :  int							      =    42384

             _UpperCAmelCase     :  int							      =    torch.Size((1, 5, vocab_size)       )
             self.assertEqual(output.shape						,					A       )

             _UpperCAmelCase     :  Any							      =    torch.tensor(
                 [[[-9.5_236, -9.8_918, 10.4_557], [-11.0_469, -9.6_423, 8.1_022], [-8.8_664, -7.8_826, 5.5_325]]]       )

             self.assertTrue(torch.allclose(output[:, :3, :3]						,					A						,					atol=1E-4       )       )





      @slow
      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  str							      =    BioGptTokenizer.from_pretrained("microsoft/biogpt"       )
             _UpperCAmelCase     :  Tuple							      =    BioGptForCausalLM.from_pretrained("microsoft/biogpt"       )
             model.to(A       )

             torch.manual_seed(0       )
             _UpperCAmelCase     :  Optional[Any]							      =    tokenizer("COVID-19 is"						,					return_tensors="pt"       ).to(A       )
             _UpperCAmelCase     :  Dict							      =    model.generate(
                 **A						,					min_length=100						,					max_length=1024						,					num_beams=5						,					early_stopping=A						,					)
             _UpperCAmelCase     :  Optional[Any]							      =    tokenizer.decode(output_ids[0]						,					skip_special_tokens=A       )

             _UpperCAmelCase     :  List[str]							      =    (
                 "COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"
                 " causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"
                 " territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"
                 " and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"
                 " more than 800,000 deaths."
             )
             self.assertEqual(A						,					A       )





'''simple docstring'''
def 			a      (			__a   )  ->	"list[int]":



					'''simple docstring'''






					if upper_limit < 0:
										raise ValueError('''Limit for the Catalan sequence must be ≥ 0'''   )

					UpperCamelCase__   :Optional[Any]												= [0] * (upper_limit + 1)

					# Base case: C(0) = C(1) = 1
					UpperCamelCase__   :int												= 1
					if upper_limit > 0:
										UpperCamelCase__   :int												= 1

					# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
					for i in range(2 ,					upper_limit + 1   ):
										for j in range(__a   ):
															catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

					return catalan_list


if __name__ == "__main__":
		print('''\n********* Catalan Numbers Using Dynamic Programming ************\n''')
		print('''\n*** Enter -1 at any time to quit ***''')
		print('''\nEnter the upper limit (≥ 0) for the Catalan number sequence: ''', end='''''')
		try:
				while True:
						__snake_case							      =     int(input().strip())
						if N < 0:
								print('''\n********* Goodbye!! ************''')
								break
						else:
								print(F"""The Catalan numbers from 0 through {N} are:""")
								print(catalan_numbers(N))
								print('''Try another upper limit for the sequence: ''', end='''''')
		except (NameError, ValueError):
				print('''\n********* Invalid input, goodbye! ************\n''')

		import doctest

		doctest.testmod()