




import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
				SCREAMING_SNAKE_CASE__     :     int		    =       "pt"
elif is_tf_available():
				SCREAMING_SNAKE_CASE__     :     List[Any]		    =       "tf"
else:
				SCREAMING_SNAKE_CASE__     :     int		    =       "jax"



class       a_       (     SCREAMING_SNAKE_CASE__  ,    unittest.TestCase     ):
	A    							= ByTaTokenizer
	A    							= False





	def 						A_(							self      )  ->					Any:




				"""simple docstring"""



				super().setUp()
				SCREAMING_SNAKE_CASE_								=						ByTaTokenizer()
				tokenizer.save_pretrained(self.tmpdirname      )





	@cached_property
	def 						A_(							self      )  ->					Union[str, Any]:




				"""simple docstring"""



				return ByTaTokenizer.from_pretrained('google/byt5-small'      )





	def 						A_(							self      ,     **SCREAMING_SNAKE_CASE      )  ->					ByTaTokenizer:




				"""simple docstring"""



				return self.tokenizer_class.from_pretrained(self.tmpdirname      ,     **SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=False      ,     SCREAMING_SNAKE_CASE=20      ,     SCREAMING_SNAKE_CASE=5      )  ->					Tuple[str, list]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[]
				for i in range(len(SCREAMING_SNAKE_CASE      )      ):
							try:
										SCREAMING_SNAKE_CASE_								=						tokenizer.decode([i]      ,     clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE      )
							except UnicodeDecodeError:
										pass
							toks.append((i, tok)      )

				SCREAMING_SNAKE_CASE_								=						list(filter(lambda SCREAMING_SNAKE_CASE      : re.match(r'^[ a-zA-Z]+$'      ,     t[1]      )      ,     SCREAMING_SNAKE_CASE      )      )
				SCREAMING_SNAKE_CASE_								=						list(filter(lambda SCREAMING_SNAKE_CASE      : [t[0]] == tokenizer.encode(t[1]      ,     add_special_tokens=SCREAMING_SNAKE_CASE      )      ,     SCREAMING_SNAKE_CASE      )      )
				if max_length is not None and len(SCREAMING_SNAKE_CASE      ) > max_length:
							SCREAMING_SNAKE_CASE_								=						toks[:max_length]
				if min_length is not None and len(SCREAMING_SNAKE_CASE      ) < min_length and len(SCREAMING_SNAKE_CASE      ) > 0:
							while len(SCREAMING_SNAKE_CASE      ) < min_length:
										SCREAMING_SNAKE_CASE_								=						toks + toks
        # toks_str = [t[1] for t in toks]
				SCREAMING_SNAKE_CASE_								=						[t[0] for t in toks]

				# Ensure consistency
				SCREAMING_SNAKE_CASE_								=						tokenizer.decode(SCREAMING_SNAKE_CASE      ,     clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE      )
				if " " not in output_txt and len(SCREAMING_SNAKE_CASE      ) > 1:
							SCREAMING_SNAKE_CASE_								=						(
							    tokenizer.decode([toks_ids[0]]      ,     clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE      )
							    + ' '
							    + tokenizer.decode(toks_ids[1:]      ,     clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE      )
							)
				if with_prefix_space:
							SCREAMING_SNAKE_CASE_								=						' ' + output_txt
				SCREAMING_SNAKE_CASE_								=						tokenizer.encode(SCREAMING_SNAKE_CASE      ,     add_special_tokens=SCREAMING_SNAKE_CASE      )
				return output_txt, output_ids





	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.ta_base_tokenizer
				SCREAMING_SNAKE_CASE_								=						tokenizer(['hi</s>', 'I went to the gym</s>', '</s>']      )
				SCREAMING_SNAKE_CASE_								=						tokenizer(['hi', 'I went to the gym', '']      )
				self.assertListEqual(batch_with_eos_added['input_ids']      ,     batch_without_eos_added['input_ids']      )





	def 						A_(							self      )  ->					List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.ta_base_tokenizer
				SCREAMING_SNAKE_CASE_								=						'Unicode €.'
				SCREAMING_SNAKE_CASE_								=						tokenizer(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
				self.assertEqual(encoded['input_ids']      ,     SCREAMING_SNAKE_CASE      )

				# decoding
				SCREAMING_SNAKE_CASE_								=						tokenizer.decode(SCREAMING_SNAKE_CASE      )
				self.assertEqual(SCREAMING_SNAKE_CASE      ,     'Unicode €.</s>'      )

				SCREAMING_SNAKE_CASE_								=						tokenizer('e è é ê ë'      )
				SCREAMING_SNAKE_CASE_								=						[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
				self.assertEqual(encoded['input_ids']      ,     SCREAMING_SNAKE_CASE      )
				# decoding
				SCREAMING_SNAKE_CASE_								=						tokenizer.decode(SCREAMING_SNAKE_CASE      )
				self.assertEqual(SCREAMING_SNAKE_CASE      ,     'e è é ê ë</s>'      )

				# encode/decode, but with `encode` instead of `__call__`
				self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'      )      )      ,     'e è é ê ë</s>'      )





	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.ta_base_tokenizer
				SCREAMING_SNAKE_CASE_								=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
				# fmt: off
				SCREAMING_SNAKE_CASE_								=						[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
				# fmt: on
				SCREAMING_SNAKE_CASE_								=						tokenizer(SCREAMING_SNAKE_CASE      ,     padding=SCREAMING_SNAKE_CASE      ,     return_tensors=SCREAMING_SNAKE_CASE      )
				self.assertIsInstance(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )

				if FRAMEWORK != "jax":
							SCREAMING_SNAKE_CASE_								=						list(batch.input_ids.numpy()[0]      )
				else:
							SCREAMING_SNAKE_CASE_								=						list(batch.input_ids.tolist()[0]      )

				self.assertListEqual(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )

				self.assertEqual((2, 37)      ,     batch.input_ids.shape      )
				self.assertEqual((2, 37)      ,     batch.attention_mask.shape      )





	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.ta_base_tokenizer
				SCREAMING_SNAKE_CASE_								=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
				SCREAMING_SNAKE_CASE_								=						tokenizer(SCREAMING_SNAKE_CASE      ,     padding=SCREAMING_SNAKE_CASE      ,     return_tensors=SCREAMING_SNAKE_CASE      )
				# check if input_ids are returned and no decoder_input_ids
				self.assertIn('input_ids'      ,     SCREAMING_SNAKE_CASE      )
				self.assertIn('attention_mask'      ,     SCREAMING_SNAKE_CASE      )
				self.assertNotIn('decoder_input_ids'      ,     SCREAMING_SNAKE_CASE      )
				self.assertNotIn('decoder_attention_mask'      ,     SCREAMING_SNAKE_CASE      )





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




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.ta_base_tokenizer
				SCREAMING_SNAKE_CASE_								=						[
				    'Summary of the text.',
				    'Another summary.',
				]
				SCREAMING_SNAKE_CASE_								=						tokenizer(
				    text_target=SCREAMING_SNAKE_CASE      ,     max_length=32      ,     padding='max_length'      ,     truncation=SCREAMING_SNAKE_CASE      ,     return_tensors=SCREAMING_SNAKE_CASE      )
				self.assertEqual(32      ,     targets['input_ids'].shape[1]      )





	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.ta_base_tokenizer
				SCREAMING_SNAKE_CASE_								=						['A long paragraph for summarization. </s>']
				SCREAMING_SNAKE_CASE_								=						['Summary of the text. </s>']
				# fmt: off
				SCREAMING_SNAKE_CASE_								=						[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
				SCREAMING_SNAKE_CASE_								=						[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
				# fmt: on

				SCREAMING_SNAKE_CASE_								=						tokenizer(SCREAMING_SNAKE_CASE      ,     text_target=SCREAMING_SNAKE_CASE      )

				self.assertEqual(SCREAMING_SNAKE_CASE      ,     batch['input_ids'][0]      )
				self.assertEqual(SCREAMING_SNAKE_CASE      ,     batch['labels'][0]      )





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.get_tokenizers()
				for tokenizer in tokenizers:
							with self.subTest(f'{tokenizer.__class__.__name__}'      ):
										self.assertNotEqual(tokenizer.model_max_length      ,     42      )

        # Now let's start the test
				SCREAMING_SNAKE_CASE_								=						self.get_tokenizers()
				for tokenizer in tokenizers:
							with self.subTest(f'{tokenizer.__class__.__name__}'      ):
										# Isolate this from the other tests because we save additional tokens/etc
										SCREAMING_SNAKE_CASE_								=						tempfile.mkdtemp()

										SCREAMING_SNAKE_CASE_								=						' He is very happy, UNwant\u00E9d,running'
										SCREAMING_SNAKE_CASE_								=						tokenizer.encode(SCREAMING_SNAKE_CASE      ,     add_special_tokens=SCREAMING_SNAKE_CASE      )
										tokenizer.save_pretrained(SCREAMING_SNAKE_CASE      )

										SCREAMING_SNAKE_CASE_								=						tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE      )
										SCREAMING_SNAKE_CASE_								=						after_tokenizer.encode(SCREAMING_SNAKE_CASE      ,     add_special_tokens=SCREAMING_SNAKE_CASE      )
										self.assertListEqual(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )

										shutil.rmtree(SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						self.get_tokenizers(model_max_length=42      )
				for tokenizer in tokenizers:
							with self.subTest(f'{tokenizer.__class__.__name__}'      ):
										# Isolate this from the other tests because we save additional tokens/etc
										SCREAMING_SNAKE_CASE_								=						tempfile.mkdtemp()

										SCREAMING_SNAKE_CASE_								=						' He is very happy, UNwant\u00E9d,running'
										tokenizer.add_tokens(['bim', 'bambam']      )
										SCREAMING_SNAKE_CASE_								=						tokenizer.additional_special_tokens
										additional_special_tokens.append('new_additional_special_token'      )
										tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}      )
										SCREAMING_SNAKE_CASE_								=						tokenizer.encode(SCREAMING_SNAKE_CASE      ,     add_special_tokens=SCREAMING_SNAKE_CASE      )
										tokenizer.save_pretrained(SCREAMING_SNAKE_CASE      )

										SCREAMING_SNAKE_CASE_								=						tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE      )
										SCREAMING_SNAKE_CASE_								=						after_tokenizer.encode(SCREAMING_SNAKE_CASE      ,     add_special_tokens=SCREAMING_SNAKE_CASE      )
										self.assertListEqual(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )
										self.assertIn('new_additional_special_token'      ,     after_tokenizer.additional_special_tokens      )
										self.assertEqual(after_tokenizer.model_max_length      ,     42      )

										SCREAMING_SNAKE_CASE_								=						tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE      ,     model_max_length=43      )
										self.assertEqual(tokenizer.model_max_length      ,     43      )

										shutil.rmtree(SCREAMING_SNAKE_CASE      )





	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[]
				if self.test_slow_tokenizer:
							tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())      )

				if self.test_rust_tokenizer:
							tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())      )

				for tokenizer_class, tokenizer_utils in tokenizer_list:
							with tempfile.TemporaryDirectory() as tmp_dir:
										tokenizer_utils.save_pretrained(SCREAMING_SNAKE_CASE      )

										with open(os.path.join(SCREAMING_SNAKE_CASE      ,     'special_tokens_map.json'      )      ,     encoding='utf-8'      ) as json_file:
													SCREAMING_SNAKE_CASE_								=						json.load(SCREAMING_SNAKE_CASE      )

										with open(os.path.join(SCREAMING_SNAKE_CASE      ,     'tokenizer_config.json'      )      ,     encoding='utf-8'      ) as json_file:
													SCREAMING_SNAKE_CASE_								=						json.load(SCREAMING_SNAKE_CASE      )

										SCREAMING_SNAKE_CASE_								=						[f'<extra_id_{i}>' for i in range(125      )]

										SCREAMING_SNAKE_CASE_								=						added_tokens_extra_ids + [
										    'an_additional_special_token'
										]
										SCREAMING_SNAKE_CASE_								=						added_tokens_extra_ids + [
										    'an_additional_special_token'
										]

										with open(os.path.join(SCREAMING_SNAKE_CASE      ,     'special_tokens_map.json'      )      ,     'w'      ,     encoding='utf-8'      ) as outfile:
													json.dump(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )
										with open(os.path.join(SCREAMING_SNAKE_CASE      ,     'tokenizer_config.json'      )      ,     'w'      ,     encoding='utf-8'      ) as outfile:
													json.dump(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )

										# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
										# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
										# "special_tokens_map.json" files
										SCREAMING_SNAKE_CASE_								=						tokenizer_class.from_pretrained(
										    SCREAMING_SNAKE_CASE      ,     )
										self.assertIn(
										    'an_additional_special_token'      ,     tokenizer_without_change_in_init.additional_special_tokens      )
										# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
										self.assertEqual(
										    ['an_additional_special_token']      ,     tokenizer_without_change_in_init.convert_ids_to_tokens(
										        tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token']      )      )      ,     )

										# Now we test that we can change the value of additional_special_tokens in the from_pretrained
										SCREAMING_SNAKE_CASE_								=						added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'      ,     lstrip=SCREAMING_SNAKE_CASE      )]
										SCREAMING_SNAKE_CASE_								=						tokenizer_class.from_pretrained(
										    SCREAMING_SNAKE_CASE      ,     additional_special_tokens=SCREAMING_SNAKE_CASE      ,     )

										self.assertIn('a_new_additional_special_token'      ,     tokenizer.additional_special_tokens      )
										self.assertEqual(
										    ['a_new_additional_special_token']      ,     tokenizer.convert_ids_to_tokens(
										        tokenizer.convert_tokens_to_ids(['a_new_additional_special_token']      )      )      ,     )





	def 						A_(							self      )  ->					List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[]
				if self.test_slow_tokenizer:
							tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())      )

				if self.test_rust_tokenizer:
							tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())      )

				for tokenizer_class, tokenizer_utils in tokenizer_list:
							with tempfile.TemporaryDirectory() as tmp_dir:
										tokenizer_utils.save_pretrained(SCREAMING_SNAKE_CASE      )

										SCREAMING_SNAKE_CASE_								=						tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE      )

										self.assertTrue(tokenizer.decode([255]      ) == ''      )





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




				"""simple docstring"""



				pass





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				pass





	def 						A_(							self      )  ->					Any:




				"""simple docstring"""



				pass





	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				pass





	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.get_tokenizers(fast=SCREAMING_SNAKE_CASE      ,     do_lower_case=SCREAMING_SNAKE_CASE      )
				for tokenizer in tokenizers:
							with self.subTest(f'{tokenizer.__class__.__name__}'      ):
										SCREAMING_SNAKE_CASE_								=						['t', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 'x', 't', '</s>']
										SCREAMING_SNAKE_CASE_								=						tokenizer.convert_tokens_to_string(SCREAMING_SNAKE_CASE      )

										self.assertIsInstance(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )





	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.get_tokenizers()
				for tokenizer in tokenizers:
							with self.subTest(f'{tokenizer.__class__.__name__}'      ):
										SCREAMING_SNAKE_CASE_								=						[
										    'bos_token',
										    'eos_token',
										    'unk_token',
										    'sep_token',
										    'pad_token',
										    'cls_token',
										    'mask_token',
										]

										SCREAMING_SNAKE_CASE_								=						0
										SCREAMING_SNAKE_CASE_								=						tokenizer.convert_ids_to_tokens(
										    SCREAMING_SNAKE_CASE      ,     skip_special_tokens=SCREAMING_SNAKE_CASE      )

										for attr in attributes_list:
													setattr(SCREAMING_SNAKE_CASE      ,     attr + '_id'      ,     SCREAMING_SNAKE_CASE      )
													self.assertEqual(getattr(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )      ,     SCREAMING_SNAKE_CASE      )
													self.assertEqual(getattr(SCREAMING_SNAKE_CASE      ,     attr + '_id'      )      ,     SCREAMING_SNAKE_CASE      )

													setattr(SCREAMING_SNAKE_CASE      ,     attr + '_id'      ,     SCREAMING_SNAKE_CASE      )
													self.assertEqual(getattr(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )      ,     SCREAMING_SNAKE_CASE      )
													self.assertEqual(getattr(SCREAMING_SNAKE_CASE      ,     attr + '_id'      )      ,     SCREAMING_SNAKE_CASE      )

										setattr(SCREAMING_SNAKE_CASE      ,     'additional_special_tokens_ids'      ,     []      )
										self.assertListEqual(getattr(SCREAMING_SNAKE_CASE      ,     'additional_special_tokens'      )      ,     []      )
										self.assertListEqual(getattr(SCREAMING_SNAKE_CASE      ,     'additional_special_tokens_ids'      )      ,     []      )

										setattr(SCREAMING_SNAKE_CASE      ,     'additional_special_tokens_ids'      ,     [token_id_to_test_setters]      )
										self.assertListEqual(getattr(SCREAMING_SNAKE_CASE      ,     'additional_special_tokens'      )      ,     [token_to_test_setters]      )
										self.assertListEqual(getattr(SCREAMING_SNAKE_CASE      ,     'additional_special_tokens_ids'      )      ,     [token_id_to_test_setters]      )






def lowercase    (  SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						len(SCREAMING_SNAKE_CASE )
			while cur > 1:
						# Find the maximum number in arr
						SCREAMING_SNAKE_CASE_								=						arr.index(max(arr[0:cur] ) )
						# Reverse from 0 to mi
						SCREAMING_SNAKE_CASE_								=						arr[mi::-1] + arr[mi + 1 : len(SCREAMING_SNAKE_CASE )]
						# Reverse whole list
						SCREAMING_SNAKE_CASE_								=						arr[cur - 1 :: -1] + arr[cur : len(SCREAMING_SNAKE_CASE )]
						cur -= 1
			return arr


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