
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_  :




	'''simple docstring'''


	def __init__(			self						,       snake_case						,       snake_case=99						,       snake_case=13						,       snake_case=7						,       snake_case=9						,       snake_case=True						,       snake_case=True						,       snake_case=False						,       snake_case=32						,       snake_case=5						,       snake_case=4						,       snake_case=37						,       snake_case=8						,       snake_case=0.1						,       snake_case=0.002						,       snake_case=1						,       snake_case=0						,       snake_case=0						,       snake_case=None						,       snake_case=None						,       ):
		lowercase	    =       parent
		lowercase	    =       batch_size
		lowercase	    =       encoder_seq_length
		lowercase	    =       decoder_seq_length
		# For common tests
		lowercase	    =       self.decoder_seq_length
		lowercase	    =       is_training
		lowercase	    =       use_attention_mask
		lowercase	    =       use_labels
		lowercase	    =       vocab_size
		lowercase	    =       hidden_size
		lowercase	    =       num_hidden_layers
		lowercase	    =       num_attention_heads
		lowercase	    =       d_ff
		lowercase	    =       relative_attention_num_buckets
		lowercase	    =       dropout_rate
		lowercase	    =       initializer_factor
		lowercase	    =       eos_token_id
		lowercase	    =       pad_token_id
		lowercase	    =       decoder_start_token_id
		lowercase	    =       None
		lowercase	    =       decoder_layers





	def       SCREAMING_SNAKE_CASE__		(			self			):
		return TaConfig.from_pretrained('google/umt5-base'			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case=None						,       snake_case=None						,       snake_case=None						,       snake_case=None						,       snake_case=None						,       ):
		if attention_mask is None:
			lowercase	    =       input_ids.ne(config.pad_token_id			)
		if decoder_attention_mask is None:
			lowercase	    =       decoder_input_ids.ne(config.pad_token_id			)
		if head_mask is None:
			lowercase	    =       torch.ones(config.num_hidden_layers						,       config.num_attention_heads						,       device=snake_case			)
		if decoder_head_mask is None:
			lowercase	    =       torch.ones(config.num_decoder_layers						,       config.num_attention_heads						,       device=snake_case			)
		if cross_attn_head_mask is None:
			lowercase	    =       torch.ones(
			    config.num_decoder_layers						,       config.num_attention_heads						,       device=snake_case			)
		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			):
		lowercase	    =       ids_tensor([self.batch_size, self.encoder_seq_length]						,       self.vocab_size			)
		lowercase	    =       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
		lowercase	    =       input_ids.clamp(self.pad_token_id + 1			)
		lowercase	    =       decoder_input_ids.clamp(self.pad_token_id + 1			)

		lowercase	    =       self.get_config()
		lowercase	    =       config.num_attention_heads
		lowercase	    =       self.prepare_inputs_dict(snake_case						,       snake_case						,       snake_case			)
		return config, input_dict





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase    ,   lowercase	    =       self.prepare_config_and_inputs()
		return config, inputs_dict





	def       SCREAMING_SNAKE_CASE__		(			self			):
		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			):
		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						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       ):
		lowercase	    =       UMTaModel(config=snake_case			)
		model.to(snake_case			)
		model.eval()
		lowercase	    =       model(
		    input_ids=snake_case						,       decoder_input_ids=snake_case						,       attention_mask=snake_case						,       decoder_attention_mask=snake_case						,       )
		lowercase	    =       model(input_ids=snake_case						,       decoder_input_ids=snake_case			)
		lowercase	    =       result.last_hidden_state
		lowercase	    =       result.past_key_values
		lowercase	    =       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(snake_case			)						,       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						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       ):
		lowercase	    =       UMTaModel(config=snake_case			).get_decoder().to(snake_case			).eval()
		# first forward pass
		lowercase	    =       model(snake_case						,       use_cache=snake_case			)
		lowercase	    =       model(snake_case			)
		lowercase	    =       model(snake_case						,       use_cache=snake_case			)

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

		lowercase    ,   lowercase	    =       outputs.to_tuple()

		# create hypothetical next token and extent to next_input_ids
		lowercase	    =       ids_tensor((self.batch_size, 1)						,       config.vocab_size			)

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

		lowercase	    =       model(snake_case			)['last_hidden_state']
		lowercase	    =       model(snake_case						,       past_key_values=snake_case			)['last_hidden_state']

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

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





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       ):
		lowercase	    =       UMTaModel(config=snake_case			).to(snake_case			).half().eval()
		lowercase	    =       model(**snake_case			)['last_hidden_state']
		self.parent.assertFalse(torch.isnan(snake_case			).any().item()			)







@require_torch
class 			A_  (     __lowerCamelCase ,      __lowerCamelCase ,      __lowerCamelCase ,      unittest.TestCase  ):




	'''simple docstring'''


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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       UMTaModelTester(self			)





	@unittest.skip('Test has a segmentation fault on torch 1.8.0'			)
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       self.model_tester.prepare_config_and_inputs()
		lowercase	    =       UMTaModel(config_and_inputs[0]			).to(snake_case			)
		with tempfile.TemporaryDirectory() as tmpdirname:
			torch.onnx.export(
			    snake_case						,       (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])						,       F'''{tmpdirname}/t5_test.onnx'''						,       export_params=snake_case						,       opset_version=9						,       input_names=['input_ids', 'decoder_input_ids']						,       )





	@unittest.skipIf(torch_device == 'cpu'						,       'Cant do half precision'			)
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model_fpaa_forward(*snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
		lowercase	    =       self.model_tester.prepare_config_and_inputs()
		lowercase	    =       config_and_inputs[0]
		lowercase	    =       UMTaForConditionalGeneration(snake_case			).eval()
		model.to(snake_case			)

		lowercase	    =       {
		    'head_mask': torch.zeros(config.num_layers						,       config.num_heads						,       device=snake_case			),
		    'decoder_head_mask': torch.zeros(config.num_decoder_layers						,       config.num_heads						,       device=snake_case			),
		    'cross_attn_head_mask': torch.zeros(config.num_decoder_layers						,       config.num_heads						,       device=snake_case			),
		}

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

			lowercase	    =       model.generate(
			    config_and_inputs[1]['input_ids']						,       num_beams=1						,       max_length=3						,       output_attentions=snake_case						,       return_dict_in_generate=snake_case						,       **snake_case						,       )
			# We check the state of decoder_attentions and cross_attentions just from the last step
			lowercase	    =       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			):
		pass







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




	'''simple docstring'''


	@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			):
		lowercase	    =       UMTaForConditionalGeneration.from_pretrained('google/umt5-small'						,       return_dict=snake_case			).to(snake_case			)
		lowercase	    =       AutoTokenizer.from_pretrained('google/umt5-small'						,       use_fast=snake_case						,       legacy=snake_case			)
		lowercase	    =       [
		    '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>.',
		]
		lowercase	    =       tokenizer(snake_case						,       return_tensors='pt'						,       padding=snake_case			).input_ids
		# fmt: off
		lowercase	    =       torch.tensor(
		    [
		        [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
		        [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
		        [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
		        [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
		        [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
		    ]			)
		# fmt: on
		torch.testing.assert_allclose(snake_case						,       snake_case			)

		lowercase	    =       model.generate(input_ids.to(snake_case			)			)
		lowercase	    =       [
		    '<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>',
		]
		lowercase	    =       tokenizer.batch_decode(snake_case			)
		self.assertEqual(snake_case						,       snake_case			)




from typing import Any






class 						__magic_name__					:






     def __init__(				self		,						_lowercase	)->     List[str]:
         UpperCamelCase_			  =						data
         UpperCamelCase_			  =						None






     def __repr__(				self	)->     str:
         return F"Node({self.data})"






class 						__magic_name__					:






     def __init__(				self	)->     Optional[Any]:
         UpperCamelCase_			  =						None






     def __iter__(				self	)->     Any:
         UpperCamelCase_			  =						self.head
         while node:
             yield node.data
             UpperCamelCase_			  =						node.next






     def __len__(				self	)->     int:
         return sum(1 for _ in self	)






     def __repr__(				self	)->     str:
         return "->".join([str(_lowercase	) for item in self]	)






     def __getitem__(				self		,						_lowercase	)->     Any:
         if not 0 <= index < len(self	):
             raise ValueError("list index out of range."	)
         for i, node in enumerate(self	):
             if i == index:
                 return node
         return None






     def __setitem__(				self		,						_lowercase		,						_lowercase	)->     None:
         if not 0 <= index < len(self	):
             raise ValueError("list index out of range."	)
         UpperCamelCase_			  =						self.head
         for _ in range(_lowercase	):
             UpperCamelCase_			  =						current.next
         UpperCamelCase_			  =						data






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     None:
         self.insert_nth(len(self	)		,						_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     None:
         self.insert_nth(0		,						_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase	)->     None:
         if not 0 <= index <= len(self	):
             raise IndexError("list index out of range"	)
         UpperCamelCase_			  =						Node(_lowercase	)
         if self.head is None:
             UpperCamelCase_			  =						new_node
         elif index == 0:
             UpperCamelCase_			  =						self.head  # link new_node to head
             UpperCamelCase_			  =						new_node
         else:
             UpperCamelCase_			  =						self.head
             for _ in range(index - 1	):
                 UpperCamelCase_			  =						temp.next
             UpperCamelCase_			  =						temp.next
             UpperCamelCase_			  =						new_node






     def 	UpperCAmelCase_				(				self	)->     None:  # print every node data
         print(self	)






     def 	UpperCAmelCase_				(				self	)->     Any:
         return self.delete_nth(0	)






     def 	UpperCAmelCase_				(				self	)->     Any:  # delete from tail
         return self.delete_nth(len(self	) - 1	)






     def 	UpperCAmelCase_				(				self		,						_lowercase = 0	)->     Any:
         if not 0 <= index <= len(self	) - 1:  # test if index is valid
             raise IndexError("List index out of range."	)
         UpperCamelCase_			  =						self.head  # default first node
         if index == 0:
             UpperCamelCase_			  =						self.head.next
         else:
             UpperCamelCase_			  =						self.head
             for _ in range(index - 1	):
                 UpperCamelCase_			  =						temp.next
             UpperCamelCase_			  =						temp.next
             UpperCamelCase_			  =						temp.next.next
         return delete_node.data






     def 	UpperCAmelCase_				(				self	)->     bool:
         return self.head is None






     def 	UpperCAmelCase_				(				self	)->     None:
         UpperCamelCase_			  =						None
         UpperCamelCase_			  =						self.head

         while current:
             # Store the current node's next node.
             UpperCamelCase_			  =						current.next
             # Make the current node's next point backwards
             UpperCamelCase_			  =						prev
             # Make the previous node be the current node
             UpperCamelCase_			  =						current
             # Make the current node the next node (to progress iteration)
             UpperCamelCase_			  =						next_node
         # Return prev in order to put the head at the end
         UpperCamelCase_			  =						prev




def 			lowerCAmelCase(      )->						None:



    """simple docstring"""






    UpperCamelCase_			  =						LinkedList()
    assert linked_list.is_empty() is True
    assert str(SCREAMING_SNAKE_CASE_			) == ""

    try:
        linked_list.delete_head()
        raise AssertionError  # This should not happen.
    except IndexError:
        assert True  # This should happen.

    try:
        linked_list.delete_tail()
        raise AssertionError  # This should not happen.
    except IndexError:
        assert True  # This should happen.

    for i in range(1_0			):
        assert len(SCREAMING_SNAKE_CASE_			) == i
        linked_list.insert_nth(SCREAMING_SNAKE_CASE_			,						i + 1			)
    assert str(SCREAMING_SNAKE_CASE_			) == "->".join(str(SCREAMING_SNAKE_CASE_			) for i in range(1			,						1_1			)			)

    linked_list.insert_head(0			)
    linked_list.insert_tail(1_1			)
    assert str(SCREAMING_SNAKE_CASE_			) == "->".join(str(SCREAMING_SNAKE_CASE_			) for i in range(0			,						1_2			)			)

    assert linked_list.delete_head() == 0
    assert linked_list.delete_nth(9			) == 1_0
    assert linked_list.delete_tail() == 1_1
    assert len(SCREAMING_SNAKE_CASE_			) == 9
    assert str(SCREAMING_SNAKE_CASE_			) == "->".join(str(SCREAMING_SNAKE_CASE_			) for i in range(1			,						1_0			)			)

    assert all(linked_list[i] == i + 1 for i in range(0			,						9			)			) is True

    for i in range(0			,						9			):
        UpperCamelCase_			  =						-i
    assert all(linked_list[i] == -i for i in range(0			,						9			)			) is True

    linked_list.reverse()
    assert str(SCREAMING_SNAKE_CASE_			) == "->".join(str(SCREAMING_SNAKE_CASE_			) for i in range(-8			,						1			)			)




def 			lowerCAmelCase(      )->						None:



    """simple docstring"""






    UpperCamelCase_			  =						[
        -9,
        1_0_0,
        Node(7_7_3_4_5_1_1_2			),
        "dlrow olleH",
        7,
        5_5_5_5,
        0,
        -192.5_5555,
        "Hello, world!",
        77.9,
        Node(1_0			),
        None,
        None,
        12.20,
    ]
    UpperCamelCase_			  =						LinkedList()

    for i in test_input:
        linked_list.insert_tail(SCREAMING_SNAKE_CASE_			)

    # Check if it's empty or not
    assert linked_list.is_empty() is False
    assert (
        str(SCREAMING_SNAKE_CASE_			) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
        "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
    )

    # Delete the head
    UpperCamelCase_			  =						linked_list.delete_head()
    assert result == -9
    assert (
        str(SCREAMING_SNAKE_CASE_			) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
        "Hello, world!->77.9->Node(10)->None->None->12.2"
    )

    # Delete the tail
    UpperCamelCase_			  =						linked_list.delete_tail()
    assert result == 12.2
    assert (
        str(SCREAMING_SNAKE_CASE_			) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
        "Hello, world!->77.9->Node(10)->None->None"
    )

    # Delete a node in specific location in linked list
    UpperCamelCase_			  =						linked_list.delete_nth(1_0			)
    assert result is None
    assert (
        str(SCREAMING_SNAKE_CASE_			) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
        "Hello, world!->77.9->Node(10)->None"
    )

    # Add a Node instance to its head
    linked_list.insert_head(Node("Hello again, world!"			)			)
    assert (
        str(SCREAMING_SNAKE_CASE_			)
        == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
        "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
    )

    # Add None to its tail
    linked_list.insert_tail(SCREAMING_SNAKE_CASE_			)
    assert (
        str(SCREAMING_SNAKE_CASE_			)
        == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
        "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
    )

    # Reverse the linked list
    linked_list.reverse()
    assert (
        str(SCREAMING_SNAKE_CASE_			)
        == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
        "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
    )




def 			lowerCAmelCase(      )->						List[Any]:



    """simple docstring"""






    from doctest import testmod

    testmod()

    UpperCamelCase_			  =						LinkedList()
    linked_list.insert_head(input("Inserting 1st at head "			).strip()			)
    linked_list.insert_head(input("Inserting 2nd at head "			).strip()			)
    print("\nPrint list:"			)
    linked_list.print_list()
    linked_list.insert_tail(input("\nInserting 1st at tail "			).strip()			)
    linked_list.insert_tail(input("Inserting 2nd at tail "			).strip()			)
    print("\nPrint list:"			)
    linked_list.print_list()
    print("\nDelete head"			)
    linked_list.delete_head()
    print("Delete tail"			)
    linked_list.delete_tail()
    print("\nPrint list:"			)
    linked_list.print_list()
    print("\nReverse linked list"			)
    linked_list.reverse()
    print("\nPrint list:"			)
    linked_list.print_list()
    print("\nString representation of linked list:"			)
    print(SCREAMING_SNAKE_CASE_			)
    print("\nReading/changing Node data using indexing:"			)
    print(f"Element at Position 1: {linked_list[1]}"			)
    UpperCamelCase_			  =						input("Enter New Value: "			).strip()
    print("New list:"			)
    print(SCREAMING_SNAKE_CASE_			)
    print(f"length of linked_list is : {len(SCREAMING_SNAKE_CASE_			)}"			)


if __name__ == "__main__":
 main()
