




import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors



@require_tokenizers
class       lowercase_		(SCREAMING_SNAKE_CASE__       ,  unittest.TestCase   ):
						lowerCAmelCase__ 			=BartTokenizer
						lowerCAmelCase__ 			=BartTokenizerFast
						lowerCAmelCase__ 			=True
						lowerCAmelCase__ 			=filter_roberta_detectors



						def   __a				( self  :      str     ):

								"""simple docstring"""

								super().setUp()
								SCREAMING_SNAKE_CASE_							=       [
								    'l',
								    'o',
								    'w',
								    'e',
								    'r',
								    's',
								    't',
								    'i',
								    'd',
								    'n',
								    '\u0120',
								    '\u0120l',
								    '\u0120n',
								    '\u0120lo',
								    '\u0120low',
								    'er',
								    '\u0120lowest',
								    '\u0120newer',
								    '\u0120wider',
								    '<unk>',
								]
								SCREAMING_SNAKE_CASE_							=       dict(zip(snake_case__				,       range(len(snake_case__     )     )     )     )
								SCREAMING_SNAKE_CASE_							=       ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
								SCREAMING_SNAKE_CASE_							=       {'unk_token': '<unk>'}

								SCREAMING_SNAKE_CASE_							=       os.path.join(self.tmpdirname				,       VOCAB_FILES_NAMES['vocab_file']     )
								SCREAMING_SNAKE_CASE_							=       os.path.join(self.tmpdirname				,       VOCAB_FILES_NAMES['merges_file']     )
								with open(self.vocab_file				,       'w'				,       encoding='utf-8'     ) as fp:
										fp.write(json.dumps(snake_case__     ) + '\n'     )
								with open(self.merges_file				,       'w'				,       encoding='utf-8'     ) as fp:
										fp.write('\n'.join(snake_case__     )     )



						def   __a				( self  :      List[Any]				,       **snake_case__  :      Dict     ):

								"""simple docstring"""

								kwargs.update(self.special_tokens_map     )
								return self.tokenizer_class.from_pretrained(self.tmpdirname				,       **snake_case__     )



						def   __a				( self  :      int				,       **snake_case__  :      Any     ):

								"""simple docstring"""

								kwargs.update(self.special_tokens_map     )
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,       **snake_case__     )



						def   __a				( self  :      int				,       snake_case__  :      Tuple     ):

								"""simple docstring"""

								return "lower newer", "lower newer"



						@cached_property
						def   __a				( self  :      str     ):

								"""simple docstring"""

								return BartTokenizer.from_pretrained('facebook/bart-large'     )



						@cached_property
						def   __a				( self  :      Optional[int]     ):

								"""simple docstring"""

								return BartTokenizerFast.from_pretrained('facebook/bart-large'     )



						@require_torch
						def   __a				( self  :      Tuple     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
								SCREAMING_SNAKE_CASE_							=       [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										SCREAMING_SNAKE_CASE_							=       tokenizer(snake_case__				,       max_length=len(snake_case__     )				,       padding=snake_case__				,       return_tensors='pt'     )
										self.assertIsInstance(snake_case__				,       snake_case__     )

										self.assertEqual((2, 9)				,       batch.input_ids.shape     )
										self.assertEqual((2, 9)				,       batch.attention_mask.shape     )
										SCREAMING_SNAKE_CASE_							=       batch.input_ids.tolist()[0]
										self.assertListEqual(snake_case__				,       snake_case__     )
										# Test that special tokens are reset



						@require_torch
						def   __a				( self  :      Dict     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										SCREAMING_SNAKE_CASE_							=       tokenizer(snake_case__				,       padding=snake_case__				,       return_tensors='pt'     )
										# check if input_ids are returned and no labels
										self.assertIn('input_ids'				,       snake_case__     )
										self.assertIn('attention_mask'				,       snake_case__     )
										self.assertNotIn('labels'				,       snake_case__     )
										self.assertNotIn('decoder_attention_mask'				,       snake_case__     )



						@require_torch
						def   __a				( self  :      Tuple     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       [
								    'Summary of the text.',
								    'Another summary.',
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										SCREAMING_SNAKE_CASE_							=       tokenizer(text_target=snake_case__				,       max_length=32				,       padding='max_length'				,       return_tensors='pt'     )
										self.assertEqual(32				,       targets['input_ids'].shape[1]     )



						@require_torch
						def   __a				( self  :      List[Any]     ):

								"""simple docstring"""

								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										SCREAMING_SNAKE_CASE_							=       tokenizer(
										    ['I am a small frog' * 10_24, 'I am a small frog']				,       padding=snake_case__				,       truncation=snake_case__				,       return_tensors='pt'     )
										self.assertIsInstance(snake_case__				,       snake_case__     )
										self.assertEqual(batch.input_ids.shape				,       (2, 10_24)     )



						@require_torch
						def   __a				( self  :      Tuple     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       ['A long paragraph for summarization.']
								SCREAMING_SNAKE_CASE_							=       [
								    'Summary of the text.',
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										SCREAMING_SNAKE_CASE_							=       tokenizer(snake_case__				,       return_tensors='pt'     )
										SCREAMING_SNAKE_CASE_							=       tokenizer(text_target=snake_case__				,       return_tensors='pt'     )
										SCREAMING_SNAKE_CASE_							=       inputs['input_ids']
										SCREAMING_SNAKE_CASE_							=       targets['input_ids']
										self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()     )
										self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()     )
										self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()     )
										self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()     )



						def   __a				( self  :      str     ):

								"""simple docstring"""

								pass






						def   __a				( self  :      Union[str, Any]     ):

								"""simple docstring"""

								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
												SCREAMING_SNAKE_CASE_							=       self.rust_tokenizer_class.from_pretrained(snake_case__				,       **snake_case__     )
												SCREAMING_SNAKE_CASE_							=       self.tokenizer_class.from_pretrained(snake_case__				,       **snake_case__     )
												SCREAMING_SNAKE_CASE_							=       'A, <mask> AllenNLP sentence.'
												SCREAMING_SNAKE_CASE_							=       tokenizer_r.encode_plus(snake_case__				,       add_special_tokens=snake_case__				,       return_token_type_ids=snake_case__     )
												SCREAMING_SNAKE_CASE_							=       tokenizer_p.encode_plus(snake_case__				,       add_special_tokens=snake_case__				,       return_token_type_ids=snake_case__     )

												# token_type_ids should put 0 everywhere
												self.assertEqual(sum(tokens_r['token_type_ids']     )				,       sum(tokens_p['token_type_ids']     )     )

												# attention_mask should put 1 everywhere, so sum over length should be 1
												self.assertEqual(
												    sum(tokens_r['attention_mask']     ) / len(tokens_r['attention_mask']     )				,       sum(tokens_p['attention_mask']     ) / len(tokens_p['attention_mask']     )				,       )

												SCREAMING_SNAKE_CASE_							=       tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']     )
												SCREAMING_SNAKE_CASE_							=       tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']     )

												# Rust correctly handles the space before the mask while python doesnt
												self.assertSequenceEqual(tokens_p['input_ids']				,       [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]     )
												self.assertSequenceEqual(tokens_r['input_ids']				,       [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]     )

												self.assertSequenceEqual(
												    snake_case__				,       ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']     )
												self.assertSequenceEqual(
												    snake_case__				,       ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']     )





from __future__ import annotations


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  Union[str, Any]:
		SCREAMING_SNAKE_CASE_							=       0
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   ) - 1
		while left <= right:
				SCREAMING_SNAKE_CASE_							=       (left + right) // 2
				if left == right:
						if sorted_list[middle] < item:
								SCREAMING_SNAKE_CASE_							=       middle + 1
						break
				elif sorted_list[middle] < item:
						SCREAMING_SNAKE_CASE_							=       middle + 1
				else:
						SCREAMING_SNAKE_CASE_							=       middle - 1
		sorted_list.insert(lowerCAmelCase						,	lowerCAmelCase   )
		return sorted_list


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  Any:
		SCREAMING_SNAKE_CASE_							=       []
		while left and right:
				if left[0][0] < right[0][0]:
						result.append(left.pop(0   )   )
				else:
						result.append(right.pop(0   )   )
		return result + left + right


def 				_a       (				lowerCAmelCase   )->  List[str]:
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   )
		if length <= 1:
				return list_ad
		SCREAMING_SNAKE_CASE_							=       length // 2
		return merge(sortlist_ad(list_ad[:middle]   )						,	sortlist_ad(list_ad[middle:]   )   )


def 				_a       (				lowerCAmelCase   )->  list[int]:

		if len(lowerCAmelCase   ) <= 1:
				return collection
		SCREAMING_SNAKE_CASE_							=       []
		SCREAMING_SNAKE_CASE_							=       False
		for i in range(0						,	len(lowerCAmelCase   )						,	2   ):
				if i == len(lowerCAmelCase   ) - 1:
						SCREAMING_SNAKE_CASE_							=       True
				else:
						if collection[i] < collection[i + 1]:
								two_paired_list.append([collection[i], collection[i + 1]]   )
						else:
								two_paired_list.append([collection[i + 1], collection[i]]   )
		SCREAMING_SNAKE_CASE_							=       sortlist_ad(lowerCAmelCase   )
		SCREAMING_SNAKE_CASE_							=       [i[0] for i in sorted_list_ad]
		result.append(sorted_list_ad[-1][1]   )
		if has_last_odd_item:
				SCREAMING_SNAKE_CASE_							=       collection[-1]
				SCREAMING_SNAKE_CASE_							=       binary_search_insertion(lowerCAmelCase						,	lowerCAmelCase   )
		SCREAMING_SNAKE_CASE_							=       False
		for i in range(len(lowerCAmelCase   ) - 1   ):
				if result[i] == collection[-1] and has_last_odd_item:
						SCREAMING_SNAKE_CASE_							=       True
				SCREAMING_SNAKE_CASE_							=       sorted_list_ad[i][1]
				# If last_odd_item is inserted before the item's index,
				# you should forward index one more.
				if is_last_odd_item_inserted_before_this_index:
						SCREAMING_SNAKE_CASE_							=       result[: i + 2] + binary_search_insertion(result[i + 2 :]						,	lowerCAmelCase   )
				else:
						SCREAMING_SNAKE_CASE_							=       result[: i + 1] + binary_search_insertion(result[i + 1 :]						,	lowerCAmelCase   )

		return result


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:    List[str]											=   input('''Enter numbers separated by a comma:\n''').strip()
						SCREAMING_SNAKE_CASE:    Any											=   [int(item) for item in user_input.split(''',''')]
						print(merge_insertion_sort(unsorted))