



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

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

from ...test_tokenization_common import TokenizerTesterMixin


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




class 					snake_case__						(       lowerCAmelCase_				,     unittest.TestCase     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	PerceiverTokenizer
     _SCREAMING_SNAKE_CASE						=	False






     def    lowercase_   (       self  :  Any					)   ->Optional[Any]:
            super().setUp()
            snake_case__							:	Optional[Any]   	=	PerceiverTokenizer()
            tokenizer.save_pretrained(self.tmpdirname					)






     @cached_property
     def    lowercase_   (       self  :  str					)   ->Optional[Any]:
            return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver'					)






     def    lowercase_   (       self  :  int, **_snake_case  :  Any					)   ->Optional[Any]:
            return self.tokenizer_class.from_pretrained(self.tmpdirname, **_snake_case					)






     def    lowercase_   (       self  :  Tuple, _snake_case  :  List[Any], _snake_case  :  List[str]=False, _snake_case  :  Any=2_0, _snake_case  :  str=5					)   ->Any:
            # XXX The default common tokenizer tests assume that every ID is decodable on its own.
            # This assumption is invalid for Perceiver because single bytes might not be
            # valid utf-8 (byte 128 for instance).
            # Here we're overriding the smallest possible method to provide
            # a clean sequence without making the same assumption.

            snake_case__							:	List[Any]   	=	[]
            for i in range(len(_snake_case					)					):
                   try:
                          snake_case__							:	Tuple   	=	tokenizer.decode([i], clean_up_tokenization_spaces=_snake_case					)
                   except UnicodeDecodeError:
                          pass
                   toks.append((i, tok)					)

            snake_case__							:	Optional[int]   	=	list(filter(lambda _snake_case					: re.match(R'^[ a-zA-Z]+$', t[1]					), _snake_case					)					)
            snake_case__							:	Any   	=	list(filter(lambda _snake_case					: [t[0]] == tokenizer.encode(t[1], add_special_tokens=_snake_case					), _snake_case					)					)
            if max_length is not None and len(_snake_case					) > max_length:
                   snake_case__							:	List[str]   	=	toks[:max_length]
            if min_length is not None and len(_snake_case					) < min_length and len(_snake_case					) > 0:
                   while len(_snake_case					) < min_length:
                          snake_case__							:	int   	=	toks + toks
        # toks_str = [t[1] for t in toks]
            snake_case__							:	str   	=	[t[0] for t in toks]

            # Ensure consistency
            snake_case__							:	Dict   	=	tokenizer.decode(_snake_case, clean_up_tokenization_spaces=_snake_case					)
            if " " not in output_txt and len(_snake_case					) > 1:
                   snake_case__							:	str   	=	(
                       tokenizer.decode([toks_ids[0]], clean_up_tokenization_spaces=_snake_case					)
                       + ' '
                       + tokenizer.decode(toks_ids[1:], clean_up_tokenization_spaces=_snake_case					)
                   )
            if with_prefix_space:
                   snake_case__							:	Optional[Any]   	=	' ' + output_txt
            snake_case__							:	Tuple   	=	tokenizer.encode(_snake_case, add_special_tokens=_snake_case					)
            return output_txt, output_ids






     def    lowercase_   (       self  :  Optional[int]					)   ->str:
            snake_case__							:	Optional[Any]   	=	self.perceiver_tokenizer
            snake_case__							:	Dict   	=	'Unicode €.'
            snake_case__							:	int   	=	tokenizer(_snake_case					)
            snake_case__							:	Tuple   	=	[4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
            self.assertEqual(encoded['input_ids'], _snake_case					)

            # decoding
            snake_case__							:	Optional[int]   	=	tokenizer.decode(_snake_case					)
            self.assertEqual(_snake_case, '[CLS]Unicode €.[SEP]'					)

            snake_case__							:	Union[str, Any]   	=	tokenizer('e è é ê ë'					)
            snake_case__							:	Tuple   	=	[4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
            self.assertEqual(encoded['input_ids'], _snake_case					)
            # decoding
            snake_case__							:	int   	=	tokenizer.decode(_snake_case					)
            self.assertEqual(_snake_case, '[CLS]e è é ê ë[SEP]'					)

            # encode/decode, but with `encode` instead of `__call__`
            self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'					)					), '[CLS]e è é ê ë[SEP]'					)






     def    lowercase_   (       self  :  Union[str, Any]					)   ->int:
            snake_case__							:	Optional[Any]   	=	self.perceiver_tokenizer
            snake_case__							:	Optional[Any]   	=	['A long paragraph for summarization.', 'Another paragraph for summarization.']
            # fmt: off
            snake_case__							:	List[Any]   	=	[4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
            # fmt: on
            snake_case__							:	Dict   	=	tokenizer(_snake_case, padding=_snake_case, return_tensors=_snake_case					)
            self.assertIsInstance(_snake_case, _snake_case					)

            if FRAMEWORK != "jax":
                   snake_case__							:	Optional[Any]   	=	list(batch.input_ids.numpy()[0]					)
            else:
                   snake_case__							:	Union[str, Any]   	=	list(batch.input_ids.tolist()[0]					)

            self.assertListEqual(_snake_case, _snake_case					)

            self.assertEqual((2, 3_8), batch.input_ids.shape					)
            self.assertEqual((2, 3_8), batch.attention_mask.shape					)






     def    lowercase_   (       self  :  str					)   ->List[str]:
            snake_case__							:	List[Any]   	=	self.perceiver_tokenizer
            snake_case__							:	List[str]   	=	['A long paragraph for summarization.', 'Another paragraph for summarization.']
            snake_case__							:	List[str]   	=	tokenizer(_snake_case, padding=_snake_case, return_tensors=_snake_case					)
            # check if input_ids are returned and no decoder_input_ids
            self.assertIn('input_ids', _snake_case					)
            self.assertIn('attention_mask', _snake_case					)
            self.assertNotIn('decoder_input_ids', _snake_case					)
            self.assertNotIn('decoder_attention_mask', _snake_case					)






     def    lowercase_   (       self  :  str					)   ->Dict:
            snake_case__							:	str   	=	self.perceiver_tokenizer
            snake_case__							:	Optional[int]   	=	[
                'Summary of the text.',
                'Another summary.',
            ]
            snake_case__							:	Optional[int]   	=	tokenizer(
                text_target=_snake_case, max_length=3_2, padding='max_length', truncation=_snake_case, return_tensors=_snake_case					)
            self.assertEqual(3_2, targets['input_ids'].shape[1]					)






     def    lowercase_   (       self  :  Optional[int]					)   ->Any:
            # safety check on max_len default value so we are sure the test works
            snake_case__							:	Tuple   	=	self.get_tokenizers()
            for tokenizer in tokenizers:
                   with self.subTest(F'''{tokenizer.__class__.__name__}'''					):
                          self.assertNotEqual(tokenizer.model_max_length, 4_2					)

        # Now let's start the test
            snake_case__							:	List[Any]   	=	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
                          snake_case__							:	Optional[Any]   	=	tempfile.mkdtemp()

                          snake_case__							:	List[str]   	=	' He is very happy, UNwant\u00E9d,running'
                          snake_case__							:	List[Any]   	=	tokenizer.encode(_snake_case, add_special_tokens=_snake_case					)
                          tokenizer.save_pretrained(_snake_case					)

                          snake_case__							:	Any   	=	tokenizer.__class__.from_pretrained(_snake_case					)
                          snake_case__							:	Optional[int]   	=	after_tokenizer.encode(_snake_case, add_special_tokens=_snake_case					)
                          self.assertListEqual(_snake_case, _snake_case					)

                          shutil.rmtree(_snake_case					)

            snake_case__							:	Optional[Any]   	=	self.get_tokenizers(model_max_length=4_2					)
            for tokenizer in tokenizers:
                   with self.subTest(F'''{tokenizer.__class__.__name__}'''					):
                          # Isolate this from the other tests because we save additional tokens/etc
                          snake_case__							:	Union[str, Any]   	=	tempfile.mkdtemp()

                          snake_case__							:	Union[str, Any]   	=	' He is very happy, UNwant\u00E9d,running'
                          tokenizer.add_tokens(['bim', 'bambam']					)
                          snake_case__							:	Dict   	=	tokenizer.additional_special_tokens
                          additional_special_tokens.append('new_additional_special_token'					)
                          tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}					)
                          snake_case__							:	Optional[int]   	=	tokenizer.encode(_snake_case, add_special_tokens=_snake_case					)
                          tokenizer.save_pretrained(_snake_case					)

                          snake_case__							:	str   	=	tokenizer.__class__.from_pretrained(_snake_case					)
                          snake_case__							:	Any   	=	after_tokenizer.encode(_snake_case, add_special_tokens=_snake_case					)
                          self.assertListEqual(_snake_case, _snake_case					)
                          self.assertIn('new_additional_special_token', after_tokenizer.additional_special_tokens					)
                          self.assertEqual(after_tokenizer.model_max_length, 4_2					)

                          snake_case__							:	List[Any]   	=	tokenizer.__class__.from_pretrained(_snake_case, model_max_length=4_3					)
                          self.assertEqual(tokenizer.model_max_length, 4_3					)

                          shutil.rmtree(_snake_case					)






     def    lowercase_   (       self  :  Tuple					)   ->Optional[int]:
            snake_case__							:	Optional[int]   	=	[]
            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(_snake_case					)

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

                          with open(os.path.join(_snake_case, 'tokenizer_config.json'					), encoding='utf-8'					) as json_file:
                                 snake_case__							:	List[str]   	=	json.load(_snake_case					)

                          snake_case__							:	Optional[int]   	=	[F'''<extra_id_{i}>''' for i in range(1_2_5					)]

                          snake_case__							:	List[Any]   	=	added_tokens_extra_ids + [
                              'an_additional_special_token'
                          ]
                          snake_case__							:	Tuple   	=	added_tokens_extra_ids + [
                              'an_additional_special_token'
                          ]

                          with open(os.path.join(_snake_case, 'special_tokens_map.json'					), 'w', encoding='utf-8'					) as outfile:
                                 json.dump(_snake_case, _snake_case					)
                          with open(os.path.join(_snake_case, 'tokenizer_config.json'					), 'w', encoding='utf-8'					) as outfile:
                                 json.dump(_snake_case, _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
                          snake_case__							:	Optional[int]   	=	tokenizer_class.from_pretrained(
                              _snake_case, )
                          self.assertIn(
                              'an_additional_special_token', tokenizer_without_change_in_init.additional_special_tokens					)
                          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
                          snake_case__							:	Any   	=	added_tokens_extra_ids + [AddedToken('a_new_additional_special_token', lstrip=_snake_case					)]
                          snake_case__							:	Any   	=	tokenizer_class.from_pretrained(
                              _snake_case, additional_special_tokens=_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    lowercase_   (       self  :  int					)   ->Any:
            snake_case__							:	Optional[int]   	=	self.perceiver_tokenizer
            self.assertEqual(tokenizer.decode([1_7_8]					), '�'					)






     def    lowercase_   (       self  :  str					)   ->str:
            pass






     def    lowercase_   (       self  :  Optional[int]					)   ->Optional[Any]:
            pass






     def    lowercase_   (       self  :  Optional[int]					)   ->str:
            pass






     def    lowercase_   (       self  :  Optional[int]					)   ->Optional[int]:
            pass






     def    lowercase_   (       self  :  Optional[Any]					)   ->List[Any]:
            # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
            # strings and special added tokens as tokens
            snake_case__							:	List[Any]   	=	self.get_tokenizers(fast=_snake_case, do_lower_case=_snake_case					)
            for tokenizer in tokenizers:
                   with self.subTest(F'''{tokenizer.__class__.__name__}'''					):
                          snake_case__							:	Dict   	=	['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
                          snake_case__							:	Optional[Any]   	=	tokenizer.convert_tokens_to_string(_snake_case					)

                          self.assertIsInstance(_snake_case, _snake_case					)












"""simple docstring"""






def      _snake_case			(				lowercase__		):
				stooge(lowercase__							,    0							,    len(lowercase__		) - 1		)
				return arr







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				if i >= h:
								return

				# If first element is smaller than the last then swap them
				if arr[i] > arr[h]:
								_lowerCamelCase, _lowerCamelCase     :			Optional[Any]    =    arr[h], arr[i]

				# If there are more than 2 elements in the array
				if h - i + 1 > 2:
								_lowerCamelCase     :			Union[str, Any]    =    (int)((h - i + 1) / 3		)

								# Recursively sort first 2/3 elements
								stooge(lowercase__							,    lowercase__							,    (h - t)		)

								# Recursively sort last 2/3 elements
								stooge(lowercase__							,    i + t							,    (lowercase__)		)

								# Recursively sort first 2/3 elements
								stooge(lowercase__							,    lowercase__							,    (h - t)		)


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