






"""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     UpperCamelCase_ :





	def __init__(   self      :				List[Any]			,			lowerCAmelCase_      :				Any			,			lowerCAmelCase_      :				Optional[Any]=99			,			lowerCAmelCase_      :				Optional[Any]=13			,			lowerCAmelCase_      :				str=7			,			lowerCAmelCase_      :				Tuple=9			,			lowerCAmelCase_      :				Any=True			,			lowerCAmelCase_      :				Union[str, Any]=True			,			lowerCAmelCase_      :				Union[str, Any]=False			,			lowerCAmelCase_      :				Optional[Any]=32			,			lowerCAmelCase_      :				Dict=5			,			lowerCAmelCase_      :				List[Any]=4			,			lowerCAmelCase_      :				Dict=37			,			lowerCAmelCase_      :				Any=8			,			lowerCAmelCase_      :				Any=0.1			,			lowerCAmelCase_      :				Dict=0.0_0_2			,			lowerCAmelCase_      :				Any=1			,			lowerCAmelCase_      :				Union[str, Any]=0			,			lowerCAmelCase_      :				List[str]=0			,			lowerCAmelCase_      :				Optional[int]=None			,			lowerCAmelCase_      :				Optional[int]=None			,			)		->    Dict:
				UpperCAmelCase_  :				Any      =      parent
				UpperCAmelCase_  :				Tuple      =      batch_size
				UpperCAmelCase_  :				Tuple      =      encoder_seq_length
				UpperCAmelCase_  :				List[str]      =      decoder_seq_length
				# For common tests
				UpperCAmelCase_  :				Dict      =      self.decoder_seq_length
				UpperCAmelCase_  :				str      =      is_training
				UpperCAmelCase_  :				Union[str, Any]      =      use_attention_mask
				UpperCAmelCase_  :				Any      =      use_labels
				UpperCAmelCase_  :				Optional[int]      =      vocab_size
				UpperCAmelCase_  :				Tuple      =      hidden_size
				UpperCAmelCase_  :				Any      =      num_hidden_layers
				UpperCAmelCase_  :				Dict      =      num_attention_heads
				UpperCAmelCase_  :				str      =      d_ff
				UpperCAmelCase_  :				Union[str, Any]      =      relative_attention_num_buckets
				UpperCAmelCase_  :				Union[str, Any]      =      dropout_rate
				UpperCAmelCase_  :				str      =      initializer_factor
				UpperCAmelCase_  :				List[Any]      =      eos_token_id
				UpperCAmelCase_  :				List[str]      =      pad_token_id
				UpperCAmelCase_  :				Union[str, Any]      =      decoder_start_token_id
				UpperCAmelCase_  :				Any      =      None
				UpperCAmelCase_  :				Optional[Any]      =      decoder_layers





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Union[str, Any]:
				return TaConfig.from_pretrained("google/umt5-base"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str			,			lowerCAmelCase_      :				List[str]			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Optional[int]			,			lowerCAmelCase_      :				str=None			,			lowerCAmelCase_      :				Union[str, Any]=None			,			lowerCAmelCase_      :				Tuple=None			,			lowerCAmelCase_      :				Optional[Any]=None			,			lowerCAmelCase_      :				Any=None			,			)		->    Dict:
				if attention_mask is None:
							UpperCAmelCase_  :				Union[str, Any]      =      input_ids.ne(config.pad_token_id		)
				if decoder_attention_mask is None:
							UpperCAmelCase_  :				Optional[int]      =      decoder_input_ids.ne(config.pad_token_id		)
				if head_mask is None:
							UpperCAmelCase_  :				str      =      torch.ones(config.num_hidden_layers			,			config.num_attention_heads			,			device=lowerCAmelCase_		)
				if decoder_head_mask is None:
							UpperCAmelCase_  :				List[str]      =      torch.ones(config.num_decoder_layers			,			config.num_attention_heads			,			device=lowerCAmelCase_		)
				if cross_attn_head_mask is None:
							UpperCAmelCase_  :				Optional[int]      =      torch.ones(
							    config.num_decoder_layers			,			config.num_attention_heads			,			device=lowerCAmelCase_		)
				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   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    Optional[int]:
				UpperCAmelCase_  :				Optional[Any]      =      ids_tensor([self.batch_size, self.encoder_seq_length]			,			self.vocab_size		)
				UpperCAmelCase_  :				Tuple      =      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_  :				int      =      input_ids.clamp(self.pad_token_id + 1		)
				UpperCAmelCase_  :				Union[str, Any]      =      decoder_input_ids.clamp(self.pad_token_id + 1		)

				UpperCAmelCase_  :				int      =      self.get_config()
				UpperCAmelCase_  :				str      =      config.num_attention_heads
				UpperCAmelCase_  :				List[str]      =      self.prepare_inputs_dict(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		)
				return config, input_dict





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Union[str, Any]:
				UpperCAmelCase_  ,			UpperCAmelCase_  :				Dict      =      self.prepare_config_and_inputs()
				return config, inputs_dict





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Union[str, Any]:
				return TaConfig(
				    vocab_size=166			,			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   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Union[str, Any]:
				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   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				Optional[Any]			,			lowerCAmelCase_      :				Optional[Any]			,			lowerCAmelCase_      :				Tuple			,			lowerCAmelCase_      :				Tuple			,			lowerCAmelCase_      :				List[Any]			,			lowerCAmelCase_      :				int			,			)		->    Tuple:
				UpperCAmelCase_  :				Any      =      UMTaModel(config=lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()
				UpperCAmelCase_  :				List[Any]      =      model(
				    input_ids=lowerCAmelCase_			,			decoder_input_ids=lowerCAmelCase_			,			attention_mask=lowerCAmelCase_			,			decoder_attention_mask=lowerCAmelCase_			,			)
				UpperCAmelCase_  :				Union[str, Any]      =      model(input_ids=lowerCAmelCase_			,			decoder_input_ids=lowerCAmelCase_		)
				UpperCAmelCase_  :				int      =      result.last_hidden_state
				UpperCAmelCase_  :				Tuple      =      result.past_key_values
				UpperCAmelCase_  :				List[str]      =      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(lowerCAmelCase_		)			,			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   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				Dict			,			lowerCAmelCase_      :				List[Any]			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				Dict			,			lowerCAmelCase_      :				Optional[int]			,			lowerCAmelCase_      :				Union[str, Any]			,			)		->    List[str]:
				UpperCAmelCase_  :				Tuple      =      UMTaModel(config=lowerCAmelCase_		).get_decoder().to(lowerCAmelCase_		).eval()
				# first forward pass
				UpperCAmelCase_  :				str      =      model(lowerCAmelCase_			,			use_cache=lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[int]      =      model(lowerCAmelCase_		)
				UpperCAmelCase_  :				List[Any]      =      model(lowerCAmelCase_			,			use_cache=lowerCAmelCase_		)

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

				UpperCAmelCase_  ,			UpperCAmelCase_  :				Tuple      =      outputs.to_tuple()

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

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

				UpperCAmelCase_  :				List[Any]      =      model(lowerCAmelCase_		)["last_hidden_state"]
				UpperCAmelCase_  :				Union[str, Any]      =      model(lowerCAmelCase_			,			past_key_values=lowerCAmelCase_		)["last_hidden_state"]

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

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				List[Any]			,			)		->    Tuple:
				UpperCAmelCase_  :				Tuple      =      UMTaModel(config=lowerCAmelCase_		).to(lowerCAmelCase_		).half().eval()
				UpperCAmelCase_  :				Any      =      model(**lowerCAmelCase_		)["last_hidden_state"]
				self.parent.assertFalse(torch.isnan(lowerCAmelCase_		).any().item()		)







@require_torch
class     UpperCamelCase_ (__A       ,		__A       ,		__A       ,		unittest.TestCase  ):
	__magic_name__			   =       (
	    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
	)
	__magic_name__			   =       (UMTaForConditionalGeneration,) if is_torch_available() else ()
	__magic_name__			   =       (
	    {
	        '''conversational''': UMTaForConditionalGeneration,
	        '''feature-extraction''': UMTaModel,
	        '''summarization''': UMTaForConditionalGeneration,
	        '''text2text-generation''': UMTaForConditionalGeneration,
	        '''translation''': UMTaForConditionalGeneration,
	        '''question-answering''': UMTaForQuestionAnswering,
	    }
	    if is_torch_available()
	    else {}
	)
	__magic_name__			   =       True
	__magic_name__			   =       False
	__magic_name__			   =       False
	__magic_name__			   =       True
	__magic_name__			   =       True
	# The small UMT5 model needs higher percentages for CPU/MP tests
	__magic_name__			   =       [0.8, 0.9]





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    Optional[Any]:
				UpperCAmelCase_  :				Union[str, Any]      =      UMTaModelTester(self		)





	@unittest.skip("Test has a segmentation fault on torch 1.8.0"		)
	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    Optional[Any]:
				UpperCAmelCase_  :				Optional[Any]      =      self.model_tester.prepare_config_and_inputs()
				UpperCAmelCase_  :				List[str]      =      UMTaModel(config_and_inputs[0]		).to(lowerCAmelCase_		)
				with tempfile.TemporaryDirectory() as tmpdirname:
							torch.onnx.export(
							    lowerCAmelCase_			,			(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])			,			f"""{tmpdirname}/t5_test.onnx"""			,			export_params=lowerCAmelCase_			,			opset_version=9			,			input_names=["input_ids", "decoder_input_ids"]			,			)





	@unittest.skipIf(torch_device == "cpu"			,			"Cant do half precision"		)
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Tuple:
				UpperCAmelCase_  :				str      =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model_fpaa_forward(*lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    str:
				UpperCAmelCase_  :				int      =      ["encoder_attentions", "decoder_attentions", "cross_attentions"]
				UpperCAmelCase_  :				Dict      =      self.model_tester.prepare_config_and_inputs()
				UpperCAmelCase_  :				Dict      =      config_and_inputs[0]
				UpperCAmelCase_  :				int      =      UMTaForConditionalGeneration(lowerCAmelCase_		).eval()
				model.to(lowerCAmelCase_		)

				UpperCAmelCase_  :				Optional[Any]      =      {
				    "head_mask": torch.zeros(config.num_layers			,			config.num_heads			,			device=lowerCAmelCase_		),
				    "decoder_head_mask": torch.zeros(config.num_decoder_layers			,			config.num_heads			,			device=lowerCAmelCase_		),
				    "cross_attn_head_mask": torch.zeros(config.num_decoder_layers			,			config.num_heads			,			device=lowerCAmelCase_		),
				}

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

							UpperCAmelCase_  :				Any      =      model.generate(
							    config_and_inputs[1]["input_ids"]			,			num_beams=1			,			max_length=3			,			output_attentions=lowerCAmelCase_			,			return_dict_in_generate=lowerCAmelCase_			,			**lowerCAmelCase_			,			)
							# We check the state of decoder_attentions and cross_attentions just from the last step
							UpperCAmelCase_  :				str      =      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   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    List[Any]:
				pass







@require_torch
@require_sentencepiece
@require_tokenizers
class     UpperCamelCase_ (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   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Dict:
				UpperCAmelCase_  :				Optional[int]      =      UMTaForConditionalGeneration.from_pretrained("google/umt5-small"			,			return_dict=lowerCAmelCase_		).to(lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      AutoTokenizer.from_pretrained("google/umt5-small"			,			use_fast=lowerCAmelCase_			,			legacy=lowerCAmelCase_		)
				UpperCAmelCase_  :				Union[str, 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_  :				Optional[Any]      =      tokenizer(lowerCAmelCase_			,			return_tensors="pt"			,			padding=lowerCAmelCase_		).input_ids
				# fmt: off
				UpperCAmelCase_  :				List[Any]      =      torch.tensor(
				    [
				        [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
				        [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
				        [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
				        [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
				        [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
				    ]		)
				# fmt: on
				torch.testing.assert_allclose(lowerCAmelCase_			,			lowerCAmelCase_		)

				UpperCAmelCase_  :				Optional[int]      =      model.generate(input_ids.to(lowerCAmelCase_		)		)
				UpperCAmelCase_  :				Tuple      =      [
				    "<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_  :				List[Any]      =      tokenizer.batch_decode(lowerCAmelCase_		)
				self.assertEqual(lowerCAmelCase_			,			lowerCAmelCase_		)











"""simple docstring"""
class     UpperCamelCase_ :





	def __init__(   self      :				List[str]			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				int=None			,			lowerCAmelCase_      :				List[Any]=None		)		->    int:
				UpperCAmelCase_  :				int      =      data
				UpperCAmelCase_  :				Optional[int]      =      previous
				UpperCAmelCase_  :				int      =      next_node





	def __str__(   self      :				Dict		)		->    str:
				return f"""{self.data}"""





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    int:
				return self.data





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    Union[str, Any]:
				return self.next





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Optional[int]:
				return self.previous







class     UpperCamelCase_ :





	def __init__(   self      :				Any			,			lowerCAmelCase_      :				str		)		->    List[str]:
				UpperCAmelCase_  :				Union[str, Any]      =      head





	def __iter__(   self      :				Any		)		->    List[str]:
				return self





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    str:
				if not self.current:
							raise StopIteration
				else:
							UpperCAmelCase_  :				Optional[int]      =      self.current.get_data()
							UpperCAmelCase_  :				Dict      =      self.current.get_next()
							return value







class     UpperCamelCase_ :





	def __init__(   self      :				List[str]		)		->    Tuple:
				UpperCAmelCase_  :				Tuple      =      None  # First node in list
				UpperCAmelCase_  :				Union[str, Any]      =      None  # Last node in list





	def __str__(   self      :				List[Any]		)		->    Optional[int]:
				UpperCAmelCase_  :				List[str]      =      self.head
				UpperCAmelCase_  :				int      =      []
				while current is not None:
							nodes.append(current.get_data()		)
							UpperCAmelCase_  :				Optional[Any]      =      current.get_next()
				return " ".join(str(lowerCAmelCase_		) for node in nodes		)





	def __contains__(   self      :				int			,			lowerCAmelCase_      :				int		)		->    List[str]:
				UpperCAmelCase_  :				Optional[int]      =      self.head
				while current:
							if current.get_data() == value:
										return True
							UpperCAmelCase_  :				Union[str, Any]      =      current.get_next()
				return False





	def __iter__(   self      :				int		)		->    Tuple:
				return LinkedListIterator(self.head		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Optional[int]:
				if self.head:
							return self.head.get_data()
				return None





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Optional[Any]:
				if self.tail:
							return self.tail.get_data()
				return None





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				Node		)		->    None:
				if self.head is None:
							UpperCAmelCase_  :				Optional[int]      =      node
							UpperCAmelCase_  :				Union[str, Any]      =      node
				else:
							self.insert_before_node(self.head			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				Node		)		->    None:
				if self.head is None:
							self.set_head(lowerCAmelCase_		)
				else:
							self.insert_after_node(self.tail			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				int			,			lowerCAmelCase_      :				int		)		->    None:
				UpperCAmelCase_  :				Optional[Any]      =      Node(lowerCAmelCase_		)
				if self.head is None:
							self.set_head(lowerCAmelCase_		)
				else:
							self.set_tail(lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			lowerCAmelCase_      :				Node			,			lowerCAmelCase_      :				Node		)		->    None:
				UpperCAmelCase_  :				Any      =      node
				UpperCAmelCase_  :				Tuple      =      node.previous

				if node.get_previous() is None:
							UpperCAmelCase_  :				List[Any]      =      node_to_insert
				else:
							UpperCAmelCase_  :				Dict      =      node_to_insert

				UpperCAmelCase_  :				Dict      =      node_to_insert





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				Node			,			lowerCAmelCase_      :				Node		)		->    None:
				UpperCAmelCase_  :				Dict      =      node
				UpperCAmelCase_  :				int      =      node.next

				if node.get_next() is None:
							UpperCAmelCase_  :				int      =      node_to_insert
				else:
							UpperCAmelCase_  :				Optional[Any]      =      node_to_insert

				UpperCAmelCase_  :				Any      =      node_to_insert





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				int		)		->    None:
				UpperCAmelCase_  :				int      =      1
				UpperCAmelCase_  :				List[str]      =      Node(lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[Any]      =      self.head
				while node:
							if current_position == position:
										self.insert_before_node(lowerCAmelCase_			,			lowerCAmelCase_		)
										return
							current_position += 1
							UpperCAmelCase_  :				Optional[Any]      =      node.next
				self.insert_after_node(self.tail			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]			,			lowerCAmelCase_      :				int		)		->    Node:
				UpperCAmelCase_  :				List[Any]      =      self.head
				while node:
							if node.get_data() == item:
										return node
							UpperCAmelCase_  :				Optional[int]      =      node.get_next()
				raise Exception("Node not found"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str			,			lowerCAmelCase_      :				List[str]		)		->    Union[str, Any]:
				if (node := self.get_node(lowerCAmelCase_		)) is not None:
							if node == self.head:
										UpperCAmelCase_  :				Tuple      =      self.head.get_next()

							if node == self.tail:
										UpperCAmelCase_  :				Optional[int]      =      self.tail.get_previous()

							self.remove_node_pointers(lowerCAmelCase_		)





	@staticmethod
	def   _SCREAMING_SNAKE_CASE   (   lowerCAmelCase_      :				Node		)		->    None:
				if node.get_next():
							UpperCAmelCase_  :				int      =      node.previous

				if node.get_previous():
							UpperCAmelCase_  :				Optional[Any]      =      node.next

				UpperCAmelCase_  :				Optional[int]      =      None
				UpperCAmelCase_  :				Dict      =      None





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    str:
				return self.head is None





def        snake_case   (     ):
			pass


if __name__ == "__main__":
			import doctest

			doctest.testmod()



