





'''simple docstring'''







import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

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


if is_torch_available():
					import torch

					from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel


class 					__a      :

							def __init__(							self							:     Optional[Any]       ,     __magic_name__							:     List[str]       ,     __magic_name__							:     List[Any]=99       ,     __magic_name__							:     Optional[Any]=13       ,     __magic_name__							:     Optional[Any]=7       ,     __magic_name__							:     str=9       ,     __magic_name__							:     Tuple=True       ,     __magic_name__							:     int=True       ,     __magic_name__							:     Tuple=False       ,     __magic_name__							:     int=32       ,     __magic_name__							:     Tuple=5       ,     __magic_name__							:     str=4       ,     __magic_name__							:     List[str]=37       ,     __magic_name__							:     Any=8       ,     __magic_name__							:     List[str]=0.1       ,     __magic_name__							:     Union[str, Any]=0.0_0_2       ,     __magic_name__							:     Any=1       ,     __magic_name__							:     int=0       ,     __magic_name__							:     Optional[int]=0       ,     __magic_name__							:     Optional[int]=None       ,     __magic_name__							:     Any=None       ,     )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  parent
													UpperCAmelCase_			:      str     						=  batch_size
													UpperCAmelCase_			:      Any     						=  encoder_seq_length
													UpperCAmelCase_			:      str     						=  decoder_seq_length
													# For common tests
													UpperCAmelCase_			:      List[str]     						=  self.decoder_seq_length
													UpperCAmelCase_			:      List[str]     						=  is_training
													UpperCAmelCase_			:      Optional[int]     						=  use_attention_mask
													UpperCAmelCase_			:      Optional[Any]     						=  use_labels
													UpperCAmelCase_			:      str     						=  vocab_size
													UpperCAmelCase_			:      int     						=  hidden_size
													UpperCAmelCase_			:      str     						=  num_hidden_layers
													UpperCAmelCase_			:      str     						=  num_attention_heads
													UpperCAmelCase_			:      Dict     						=  d_ff
													UpperCAmelCase_			:      Any     						=  relative_attention_num_buckets
													UpperCAmelCase_			:      List[Any]     						=  dropout_rate
													UpperCAmelCase_			:      int     						=  initializer_factor
													UpperCAmelCase_			:      Dict     						=  eos_token_id
													UpperCAmelCase_			:      Dict     						=  pad_token_id
													UpperCAmelCase_			:      str     						=  decoder_start_token_id
													UpperCAmelCase_			:      List[str]     						=  None
													UpperCAmelCase_			:      Any     						=  decoder_layers

							def     UpperCAmelCase__	(							self							:     str   )					->       Union[str, Any]:







													"""simple docstring"""
													return TaConfig.from_pretrained('''google/umt5-base'''   )

							def     UpperCAmelCase__	(							self							:     str       ,     __magic_name__							:     int       ,     __magic_name__							:     str       ,     __magic_name__							:     List[Any]       ,     __magic_name__							:     int=None       ,     __magic_name__							:     List[str]=None       ,     __magic_name__							:     int=None       ,     __magic_name__							:     Union[str, Any]=None       ,     __magic_name__							:     int=None       ,     )					->       int:







													"""simple docstring"""
													if attention_mask is None:
																			UpperCAmelCase_			:      Any     						=  input_ids.ne(config.pad_token_id   )
													if decoder_attention_mask is None:
																			UpperCAmelCase_			:      Any     						=  decoder_input_ids.ne(config.pad_token_id   )
													if head_mask is None:
																			UpperCAmelCase_			:      List[Any]     						=  torch.ones(config.num_hidden_layers       ,     config.num_attention_heads       ,     device=__magic_name__   )
													if decoder_head_mask is None:
																			UpperCAmelCase_			:      Union[str, Any]     						=  torch.ones(config.num_decoder_layers       ,     config.num_attention_heads       ,     device=__magic_name__   )
													if cross_attn_head_mask is None:
																			UpperCAmelCase_			:      Union[str, Any]     						=  torch.ones(
																			    config.num_decoder_layers       ,     config.num_attention_heads       ,     device=__magic_name__   )
													return {
													    "input_ids": input_ids,
													    "decoder_input_ids": decoder_input_ids,
													    "attention_mask": attention_mask,
													    "decoder_attention_mask": decoder_attention_mask,
													    "head_mask": head_mask,
													    "decoder_head_mask": decoder_head_mask,
													    "cross_attn_head_mask": cross_attn_head_mask,
													}

							def     UpperCAmelCase__	(							self							:     str   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  ids_tensor([self.batch_size, self.encoder_seq_length]       ,     self.vocab_size   )
													UpperCAmelCase_			:      Union[str, Any]     						=  ids_tensor([self.batch_size, self.decoder_seq_length]       ,     self.vocab_size   )

													# we need to clamp the input ids here to avoid having pad token in between
													# this is because for NllbMoe the position_ids are prepared such that
													# all pad tokens have pos id = 2 and rest are between 2..seq_length
													# and the seq_length here is seq_length - num_pad_tokens
													# but when using past, there is no way of knowing if the past input ids had
													# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
													# position_ids being off by num_pad_tokens in past input
													UpperCAmelCase_			:      Tuple     						=  input_ids.clamp(self.pad_token_id + 1   )
													UpperCAmelCase_			:      str     						=  decoder_input_ids.clamp(self.pad_token_id + 1   )

													UpperCAmelCase_			:      Tuple     						=  self.get_config()
													UpperCAmelCase_			:      List[str]     						=  config.num_attention_heads
													UpperCAmelCase_			:      Dict     						=  self.prepare_inputs_dict(__magic_name__       ,     __magic_name__       ,     __magic_name__   )
													return config, input_dict

							def     UpperCAmelCase__	(							self							:     Union[str, Any]   )					->       Any:







													"""simple docstring"""
													UpperCAmelCase_						,       UpperCAmelCase_			:      Any     						=  self.prepare_config_and_inputs()
													return config, inputs_dict

							def     UpperCAmelCase__	(							self							:     Union[str, Any]   )					->       Optional[Any]:







													"""simple docstring"""
													return TaConfig(
													    vocab_size=1_66       ,     d_model=self.hidden_size       ,     d_ff=self.d_ff       ,     d_kv=self.hidden_size // self.num_attention_heads       ,     num_layers=self.num_hidden_layers       ,     num_decoder_layers=self.decoder_layers       ,     num_heads=self.num_attention_heads       ,     relative_attention_num_buckets=self.relative_attention_num_buckets       ,     dropout_rate=self.dropout_rate       ,     initializer_factor=self.initializer_factor       ,     eos_token_id=self.eos_token_id       ,     bos_token_id=self.pad_token_id       ,     pad_token_id=self.pad_token_id       ,     decoder_start_token_id=self.decoder_start_token_id       ,     )

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       Tuple:







													"""simple docstring"""
													return TaConfig(
													    vocab_size=self.vocab_size       ,     d_model=self.hidden_size       ,     d_ff=self.d_ff       ,     d_kv=self.hidden_size // self.num_attention_heads       ,     num_layers=self.num_hidden_layers       ,     num_decoder_layers=self.decoder_layers       ,     num_heads=self.num_attention_heads       ,     relative_attention_num_buckets=self.relative_attention_num_buckets       ,     dropout_rate=self.dropout_rate       ,     initializer_factor=self.initializer_factor       ,     eos_token_id=self.eos_token_id       ,     bos_token_id=self.pad_token_id       ,     pad_token_id=self.pad_token_id       ,     decoder_start_token_id=self.decoder_start_token_id       ,     )

							def     UpperCAmelCase__	(							self							:     Optional[int]       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     List[str]       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     Union[str, Any]       ,     __magic_name__							:     Dict       ,     )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  UMTaModel(config=__magic_name__   )
													model.to(__magic_name__   )
													model.eval()
													UpperCAmelCase_			:      List[str]     						=  model(
													    input_ids=__magic_name__       ,     decoder_input_ids=__magic_name__       ,     attention_mask=__magic_name__       ,     decoder_attention_mask=__magic_name__       ,     )
													UpperCAmelCase_			:      List[Any]     						=  model(input_ids=__magic_name__       ,     decoder_input_ids=__magic_name__   )
													UpperCAmelCase_			:      int     						=  result.last_hidden_state
													UpperCAmelCase_			:      List[str]     						=  result.past_key_values
													UpperCAmelCase_			:      Optional[int]     						=  result.encoder_last_hidden_state

													self.parent.assertEqual(encoder_output.size()       ,     (self.batch_size, self.encoder_seq_length, self.hidden_size)   )
													self.parent.assertEqual(decoder_output.size()       ,     (self.batch_size, self.decoder_seq_length, self.hidden_size)   )
													# There should be `num_layers` key value embeddings stored in decoder_past
													self.parent.assertEqual(len(__magic_name__   )       ,     config.num_layers   )
													# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
													self.parent.assertEqual(len(decoder_past[0]   )       ,     4   )

							def     UpperCAmelCase__	(							self							:     Union[str, Any]       ,     __magic_name__							:     Any       ,     __magic_name__							:     str       ,     __magic_name__							:     List[str]       ,     __magic_name__							:     int       ,     __magic_name__							:     int       ,     __magic_name__							:     Tuple       ,     )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  UMTaModel(config=__magic_name__   ).get_decoder().to(__magic_name__   ).eval()
													# first forward pass
													UpperCAmelCase_			:      int     						=  model(__magic_name__       ,     use_cache=__magic_name__   )
													UpperCAmelCase_			:      List[Any]     						=  model(__magic_name__   )
													UpperCAmelCase_			:      Dict     						=  model(__magic_name__       ,     use_cache=__magic_name__   )

													self.parent.assertTrue(len(__magic_name__   ) == len(__magic_name__   )   )
													self.parent.assertTrue(len(__magic_name__   ) == len(__magic_name__   ) + 1   )

													UpperCAmelCase_						,       UpperCAmelCase_			:      int     						=  outputs.to_tuple()

													# create hypothetical next token and extent to next_input_ids
													UpperCAmelCase_			:      Optional[Any]     						=  ids_tensor((self.batch_size, 1)       ,     config.vocab_size   )

													# append to next input_ids and
													UpperCAmelCase_			:      List[str]     						=  torch.cat([input_ids, next_tokens]       ,     dim=-1   )

													UpperCAmelCase_			:      Tuple     						=  model(__magic_name__   )['''last_hidden_state''']
													UpperCAmelCase_			:      Tuple     						=  model(__magic_name__       ,     past_key_values=__magic_name__   )['''last_hidden_state''']

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

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



							def     UpperCAmelCase__	(							self							:     List[Any]       ,     __magic_name__							:     Dict       ,     __magic_name__							:     List[Any]       ,     )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  UMTaModel(config=__magic_name__   ).to(__magic_name__   ).half().eval()
													UpperCAmelCase_			:      Optional[Any]     						=  model(**__magic_name__   )['''last_hidden_state''']
													self.parent.assertFalse(torch.isnan(__magic_name__   ).any().item()   )





@require_torch
class 					__a      (lowerCamelCase       ,		lowerCamelCase       ,		lowerCamelCase       ,		unittest.TestCase     ):
							__a					:   Tuple									=       (
							    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
							)
							__a					:   Optional[int]									=       (UMTaForConditionalGeneration,) if is_torch_available() else ()
							__a					:   Optional[int]									=       (
							    {
							        "conversational": UMTaForConditionalGeneration,
							        "feature-extraction": UMTaModel,
							        "summarization": UMTaForConditionalGeneration,
							        "text2text-generation": UMTaForConditionalGeneration,
							        "translation": UMTaForConditionalGeneration,
							        "question-answering": UMTaForQuestionAnswering,
							    }
							    if is_torch_available()
							    else {}
							)
							__a					:   List[Any]									=       True
							__a					:   List[str]									=       False
							__a					:   List[str]									=       False
							__a					:   List[Any]									=       True
							__a					:   Optional[Any]									=       True
							# The small UMT5 model needs higher percentages for CPU/MP tests
							__a					:   Union[str, Any]									=       [0.8, 0.9]

							def     UpperCAmelCase__	(							self							:     List[Any]   )					->       Any:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  UMTaModelTester(self   )

							@unittest.skip('''Test has a segmentation fault on torch 1.8.0'''   )
							def     UpperCAmelCase__	(							self							:     Any   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  self.model_tester.prepare_config_and_inputs()
													UpperCAmelCase_			:      Optional[int]     						=  UMTaModel(config_and_inputs[0]   ).to(__magic_name__   )
													with tempfile.TemporaryDirectory() as tmpdirname:
																			torch.onnx.export(
																			    __magic_name__       ,     (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])       ,     F"""{tmpdirname}/t5_test.onnx"""       ,     export_params=__magic_name__       ,     opset_version=9       ,     input_names=['''input_ids''', '''decoder_input_ids''']       ,     )

							@unittest.skipIf(torch_device == '''cpu'''       ,     '''Cant do half precision'''   )
							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       str:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model_fpaa_forward(*__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Dict   )					->       str:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
													UpperCAmelCase_			:      List[Any]     						=  self.model_tester.prepare_config_and_inputs()
													UpperCAmelCase_			:      str     						=  config_and_inputs[0]
													UpperCAmelCase_			:      Tuple     						=  UMTaForConditionalGeneration(__magic_name__   ).eval()
													model.to(__magic_name__   )

													UpperCAmelCase_			:      str     						=  {
													    '''head_mask''': torch.zeros(config.num_layers       ,     config.num_heads       ,     device=__magic_name__   ),
													    '''decoder_head_mask''': torch.zeros(config.num_decoder_layers       ,     config.num_heads       ,     device=__magic_name__   ),
													    '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers       ,     config.num_heads       ,     device=__magic_name__   ),
													}

													for attn_name, (name, mask) in zip(__magic_name__       ,     head_masking.items()   ):
																			UpperCAmelCase_			:      int     						=  {name: mask}
																			# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
																			if name == "head_mask":
																									UpperCAmelCase_			:      Tuple     						=  torch.ones(
																									    config.num_decoder_layers       ,     config.num_heads       ,     device=__magic_name__   )

																			UpperCAmelCase_			:      Tuple     						=  model.generate(
																			    config_and_inputs[1]['''input_ids''']       ,     num_beams=1       ,     max_length=3       ,     output_attentions=__magic_name__       ,     return_dict_in_generate=__magic_name__       ,     **__magic_name__       ,     )
																			# We check the state of decoder_attentions and cross_attentions just from the last step
																			UpperCAmelCase_			:      Union[str, Any]     						=  out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
																			self.assertEqual(sum([w.sum().item() for w in attn_weights]   )       ,     0.0   )



							@unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.'''   )
							def     UpperCAmelCase__	(							self							:     str   )					->       List[Any]:







													"""simple docstring"""
													pass





@require_torch
@require_sentencepiece
@require_tokenizers
class 					__a      (unittest.TestCase     ):

							@slow
							@unittest.skip(
							    '''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'''   )
							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''       ,     return_dict=__magic_name__   ).to(__magic_name__   )
													UpperCAmelCase_			:      Any     						=  AutoTokenizer.from_pretrained('''google/umt5-small'''       ,     use_fast=__magic_name__       ,     legacy=__magic_name__   )
													UpperCAmelCase_			:      Any     						=  [
													    '''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
													    '''No se como puedo <extra_id_0>.''',
													    '''This is the reason why we <extra_id_0> them.''',
													    '''The <extra_id_0> walks in <extra_id_1>, seats''',
													    '''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
													]
													UpperCAmelCase_			:      Tuple     						=  tokenizer(__magic_name__       ,     return_tensors='''pt'''       ,     padding=__magic_name__   ).input_ids
													# fmt: off
													UpperCAmelCase_			:      Union[str, Any]     						=  torch.tensor(
													    [
													        [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
													        [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
													        [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
													        [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
													        [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
													    ]   )
													# fmt: on
													torch.testing.assert_allclose(__magic_name__       ,     __magic_name__   )

													UpperCAmelCase_			:      int     						=  model.generate(input_ids.to(__magic_name__   )   )
													UpperCAmelCase_			:      List[str]     						=  [
													    '''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
													    '''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
													    '''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
													    '''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
													    '''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
													]
													UpperCAmelCase_			:      Union[str, Any]     						=  tokenizer.batch_decode(__magic_name__   )
													self.assertEqual(__magic_name__       ,     __magic_name__   )












'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
					import torch

					from transformers.generation import DisjunctiveConstraint


@require_torch
class 					__a      (unittest.TestCase     ):

							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       List[Any]:







													"""simple docstring"""
													# For consistency across different places the DisjunctiveConstraint is called,
													# dc.token_ids is a list of integers. It is also initialized only by integers.

													UpperCAmelCase_			:      List[str]     						=  [[1, 2, 4], [1, 2, 3, 4]]
													UpperCAmelCase_			:      List[str]     						=  DisjunctiveConstraint(__magic_name__   )
													self.assertTrue(isinstance(dc.token_ids       ,     __magic_name__   )   )

													with self.assertRaises(__magic_name__   ):
																			DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]   )   )

													with self.assertRaises(__magic_name__   ):
																			DisjunctiveConstraint([torch.LongTensor([1, 2, 4]   ), torch.LongTensor([1, 2, 3, 4, 5]   )]   )

							def     UpperCAmelCase__	(							self							:     List[str]   )					->       Dict:







													"""simple docstring"""
													# We can't have constraints that are complete subsets of another. This leads to a preverse
													# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
													# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
													# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
													# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
													UpperCAmelCase_			:      Tuple     						=  [[1, 2], [1, 2, 3, 4]]

													with self.assertRaises(__magic_name__   ):
																			DisjunctiveConstraint(__magic_name__   )  # fails here

							def     UpperCAmelCase__	(							self							:     Optional[int]   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  [[1, 2, 3], [1, 2, 4]]

													UpperCAmelCase_			:      List[str]     						=  DisjunctiveConstraint(__magic_name__   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  dc.update(1   )
													UpperCAmelCase_			:      Dict     						=  stepped is True and completed is False and reset is False
													self.assertTrue(__magic_name__   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      str     						=  dc.update(2   )
													UpperCAmelCase_			:      Optional[Any]     						=  stepped is True and completed is False and reset is False
													self.assertTrue(__magic_name__   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1, 2]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  dc.update(3   )
													UpperCAmelCase_			:      Dict     						=  stepped is True and completed is True and reset is False
													self.assertTrue(__magic_name__   )
													self.assertTrue(dc.completed   )  # Completed!
													self.assertTrue(dc.current_seq == [1, 2, 3]   )



							def     UpperCAmelCase__	(							self							:     int   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

													UpperCAmelCase_			:      Tuple     						=  DisjunctiveConstraint(__magic_name__   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  dc.update(1   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[int]     						=  dc.update(2   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1, 2]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      int     						=  dc.update(4   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1, 2, 4]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      int     						=  dc.update(5   )
													self.assertTrue(dc.completed   )  # Completed!
													self.assertTrue(dc.current_seq == [1, 2, 4, 5]   )

													dc.reset()

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  dc.update(1   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.remaining() == 3   )
													self.assertTrue(dc.current_seq == [1]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[Any]     						=  dc.update(2   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.remaining() == 2   )
													self.assertTrue(dc.current_seq == [1, 2]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[int]     						=  dc.update(5   )
													self.assertTrue(dc.completed   )  # Completed!
													self.assertTrue(dc.remaining() == 0   )
													self.assertTrue(dc.current_seq == [1, 2, 5]   )





