


from __future__ import annotations





class    A					:


							def __init__(self   ,       lowerCAmelCase      ):
									__lowercase=    data
									__lowercase=    None





class    A					:


							def __init__(self      ):
									__lowercase=    None


							def     _A (self   ,       lowerCAmelCase      ):
									__lowercase=    Node(lowerCamelCase__      )
									__lowercase=    self.head
									__lowercase=    new_node
									return self.head.data







							def     _A (self      ):
									__lowercase=    self.head
									__lowercase=    self.head
									if self.head:
											while fast_pointer and fast_pointer.next:
													__lowercase=    fast_pointer.next.next
													__lowercase=    slow_pointer.next
											return slow_pointer.data
									else:
											print('No element found.'      )
											return None


if __name__ == "__main__":
	lowerCAmelCase					 =	LinkedList()
	for _ in range(int(input().strip())):
		lowerCAmelCase					 =	int(input().strip())
		link.push(data)
	print(link.middle_element())










from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel





@require_tf
class    A					:
							UpperCamelCase_     :							int =MarianConfig
							UpperCamelCase_     :							List[Any] ={}
							UpperCamelCase_     :							Optional[int] ='''gelu'''


							def __init__(self   ,       lowerCAmelCase   ,       lowerCAmelCase=1_3   ,       lowerCAmelCase=7   ,       lowerCAmelCase=True   ,       lowerCAmelCase=False   ,       lowerCAmelCase=9_9   ,       lowerCAmelCase=3_2   ,       lowerCAmelCase=2   ,       lowerCAmelCase=4   ,       lowerCAmelCase=3_7   ,       lowerCAmelCase=0.1   ,       lowerCAmelCase=0.1   ,       lowerCAmelCase=2_0   ,       lowerCAmelCase=2   ,       lowerCAmelCase=1   ,       lowerCAmelCase=0   ,       ):
									__lowercase=    parent
									__lowercase=    batch_size
									__lowercase=    seq_length
									__lowercase=    is_training
									__lowercase=    use_labels
									__lowercase=    vocab_size
									__lowercase=    hidden_size
									__lowercase=    num_hidden_layers
									__lowercase=    num_attention_heads
									__lowercase=    intermediate_size

									__lowercase=    hidden_dropout_prob
									__lowercase=    attention_probs_dropout_prob
									__lowercase=    max_position_embeddings
									__lowercase=    eos_token_id
									__lowercase=    pad_token_id
									__lowercase=    bos_token_id


							def     _A (self      ):
									__lowercase=    ids_tensor([self.batch_size, self.seq_length - 1]   ,       self.vocab_size      )
									__lowercase=    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )   ,       1      )
									__lowercase=    tf.concat([input_ids, eos_tensor]   ,       axis=1      )

									__lowercase=    ids_tensor([self.batch_size, self.seq_length]   ,       self.vocab_size      )

									__lowercase=    self.config_cls(
									    vocab_size=self.vocab_size   ,       d_model=self.hidden_size   ,       encoder_layers=self.num_hidden_layers   ,       decoder_layers=self.num_hidden_layers   ,       encoder_attention_heads=self.num_attention_heads   ,       decoder_attention_heads=self.num_attention_heads   ,       encoder_ffn_dim=self.intermediate_size   ,       decoder_ffn_dim=self.intermediate_size   ,       dropout=self.hidden_dropout_prob   ,       attention_dropout=self.attention_probs_dropout_prob   ,       max_position_embeddings=self.max_position_embeddings   ,       eos_token_ids=[2]   ,       bos_token_id=self.bos_token_id   ,       pad_token_id=self.pad_token_id   ,       decoder_start_token_id=self.pad_token_id   ,       **self.config_updates   ,       )
									__lowercase=    prepare_marian_inputs_dict(lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      )
									return config, inputs_dict







							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
									__lowercase=    TFMarianModel(config=lowerCAmelCase      ).get_decoder()
									__lowercase=    inputs_dict['input_ids']

									__lowercase=    input_ids[:1, :]
									__lowercase=    inputs_dict['attention_mask'][:1, :]
									__lowercase=    inputs_dict['head_mask']
									__lowercase=    1

									# first forward pass
									__lowercase=    model(lowerCAmelCase   ,       attention_mask=lowerCAmelCase   ,       head_mask=lowerCAmelCase   ,       use_cache=lowerCAmelCase      )

									__lowercase,							__lowercase=    outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									__lowercase=    ids_tensor((self.batch_size, 3)   ,       config.vocab_size      )
									__lowercase=    tf.cast(ids_tensor((self.batch_size, 3)   ,       2      )   ,       tf.inta      )

									# append to next input_ids and
									__lowercase=    tf.concat([input_ids, next_tokens]   ,       axis=-1      )
									__lowercase=    tf.concat([attention_mask, next_attn_mask]   ,       axis=-1      )

									__lowercase=    model(lowerCAmelCase   ,       attention_mask=lowerCAmelCase      )[0]
									__lowercase=    model(lowerCAmelCase   ,       attention_mask=lowerCAmelCase   ,       past_key_values=lowerCAmelCase      )[0]

									self.parent.assertEqual(next_tokens.shape[1]   ,       output_from_past.shape[1]      )

									# select random slice
									__lowercase=    int(ids_tensor((1,)   ,       output_from_past.shape[-1]      )      )
									__lowercase=    output_from_no_past[:, -3:, random_slice_idx]
									__lowercase=    output_from_past[:, :, random_slice_idx]

									# test that outputs are equal for slice
									tf.debugging.assert_near(lowerCAmelCase   ,       lowerCAmelCase   ,       rtol=1E-3      )







def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__=None     ,				lowercase__=None     ,				lowercase__=None     ,				lowercase__=None     ,				lowercase__=None     ,				)		->			Any:

		'''simple docstring'''







		if attention_mask is None:
				__lowercase=    tf.cast(tf.math.not_equal(lowercase__     ,				config.pad_token_id			)     ,				tf.inta			)
		if decoder_attention_mask is None:
				__lowercase=    tf.concat(
				    [
				        tf.ones(decoder_input_ids[:, :1].shape     ,				dtype=tf.inta			),
				        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]     ,				config.pad_token_id			)     ,				tf.inta			),
				    ]     ,				axis=-1     ,				)
		if head_mask is None:
				__lowercase=    tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
		if decoder_head_mask is None:
				__lowercase=    tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
		if cross_attn_head_mask is None:
				__lowercase=    tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
		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,
		}





@require_tf
class    A					(     A_      ,       A_      ,       unittest.TestCase				):
							UpperCamelCase_     :							str =(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
							UpperCamelCase_     :							Union[str, Any] =(TFMarianMTModel,) if is_tf_available() else ()
							UpperCamelCase_     :							List[str] =(
							    {
							        '''conversational''': TFMarianMTModel,
							        '''feature-extraction''': TFMarianModel,
							        '''summarization''': TFMarianMTModel,
							        '''text2text-generation''': TFMarianMTModel,
							        '''translation''': TFMarianMTModel,
							    }
							    if is_tf_available()
							    else {}
							)
							UpperCamelCase_     :							str =True
							UpperCamelCase_     :							List[str] =False
							UpperCamelCase_     :							Optional[int] =False


							def     _A (self      ):
									__lowercase=    TFMarianModelTester(self      )
									__lowercase=    ConfigTester(self   ,       config_class=lowerCAmelCase      )


							def     _A (self      ):
									self.config_tester.run_common_tests()







							def     _A (self      ):
									__lowercase=    self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase      )





@require_tf
class    A					(     unittest.TestCase				):
							UpperCamelCase_     :							Tuple =1_000  # show more chars for failing integration tests


							@classmethod
							def     _A (cls      ):
									__lowercase=    f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
									return cls


							@cached_property
							def     _A (self      ):
									return AutoTokenizer.from_pretrained(self.model_name      )


							@property
							def     _A (self      ):
									return self.tokenizer.eos_token_id


							@cached_property
							def     _A (self      ):
									warnings.simplefilter('error'      )
									__lowercase=    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
									assert isinstance(lowerCAmelCase   ,       lowerCAmelCase      )
									__lowercase=    model.config
									self.assertListEqual(c.bad_words_ids   ,       [[c.pad_token_id]]      )
									self.assertEqual(c.max_length   ,       5_1_2      )
									self.assertEqual(c.decoder_start_token_id   ,       c.pad_token_id      )
									return model


							def     _A (self   ,       **lowerCAmelCase      ):
									__lowercase=    self.translate_src_text(**lowerCAmelCase      )
									self.assertListEqual(self.expected_text   ,       lowerCAmelCase      )







							def     _A (self   ,       **lowerCAmelCase      ):
									__lowercase=    self.tokenizer(self.src_text   ,       **lowerCAmelCase   ,       padding=lowerCAmelCase   ,       return_tensors='tf'      )
									__lowercase=    self.model.generate(
									    model_inputs.input_ids   ,       attention_mask=model_inputs.attention_mask   ,       num_beams=2   ,       max_length=1_2_8      )
									__lowercase=    self.tokenizer.batch_decode(generated_ids.numpy()   ,       skip_special_tokens=lowerCAmelCase      )
									return generated_words





@require_sentencepiece
@require_tokenizers
@require_tf
class    A					(     A_				):
							UpperCamelCase_     :							Union[str, Any] ='''mt'''
							UpperCamelCase_     :							Tuple ='''en'''
							UpperCamelCase_     :							Optional[Any] =['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
							UpperCamelCase_     :							List[str] =['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']


							@unittest.skip('Skipping until #12647 is resolved.'      )
							@slow
							def     _A (self      ):
									self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class    A					(     A_				):
							UpperCamelCase_     :							List[Any] ='''en'''
							UpperCamelCase_     :							str ='''zh'''
							UpperCamelCase_     :							Dict =['''My name is Wolfgang and I live in Berlin''']
							UpperCamelCase_     :							Optional[int] =['''我叫沃尔夫冈 我住在柏林''']


							@unittest.skip('Skipping until #12647 is resolved.'      )
							@slow
							def     _A (self      ):
									self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class    A					(     A_				):
							UpperCamelCase_     :							Tuple ='''en'''
							UpperCamelCase_     :							List[Any] ='''ROMANCE'''
							UpperCamelCase_     :							int =[
							    '''>>fr<< Don\'t spend so much time watching TV.''',
							    '''>>pt<< Your message has been sent.''',
							    '''>>es<< He\'s two years older than me.''',
							]
							UpperCamelCase_     :							str =[
							    '''Ne passez pas autant de temps à regarder la télé.''',
							    '''A sua mensagem foi enviada.''',
							    '''Es dos años más viejo que yo.''',
							]


							@unittest.skip('Skipping until #12647 is resolved.'      )
							@slow
							def     _A (self      ):
									self._assert_generated_batch_equal_expected()







							@unittest.skip('Skipping until #12647 is resolved.'      )
							@slow
							def     _A (self      ):
									__lowercase=    TranslationPipeline(self.model   ,       self.tokenizer   ,       framework='tf'      )
									__lowercase=    pipeline(self.src_text      )
									self.assertEqual(self.expected_text   ,       [x['translation_text'] for x in output]      )






