
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import AlignProcessor, EfficientNetImageProcessor


@require_vision
class  _lowerCAmelCase    (    unittest.TestCase    ):



							def    _a (   self )				->				Any:
								_UpperCAmelCase			=    tempfile.mkdtemp()

								_UpperCAmelCase			=    [
								    "[UNK]",
								    "[CLS]",
								    "[SEP]",
								    "[PAD]",
								    "[MASK]",
								    "want",
								    "##want",
								    "##ed",
								    "wa",
								    "un",
								    "runn",
								    "##ing",
								    ",",
								    "low",
								    "lowest",
								]
								_UpperCAmelCase			=    os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES["vocab_file"] )
								with open(self.vocab_file			,    "w"			,    encoding="utf-8" ) as vocab_writer:
									vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )

								_UpperCAmelCase			=    {
								    "do_resize": True,
								    "size": 20,
								    "do_center_crop": True,
								    "crop_size": 18,
								    "do_normalize": True,
								    "image_mean": [0.48145466, 0.4578275, 0.40821073],
								    "image_std": [0.26862954, 0.26130258, 0.27577711],
								}
								_UpperCAmelCase			=    os.path.join(self.tmpdirname			,    snake_case__ )
								with open(self.image_processor_file			,    "w"			,    encoding="utf-8" ) as fp:
									json.dump(snake_case__			,    snake_case__ )



							def    _a (   self			,    **a_ )				->				List[Any]:
								return BertTokenizer.from_pretrained(self.tmpdirname			,    **snake_case__ )



							def    _a (   self			,    **a_ )				->				Optional[Any]:
								return BertTokenizerFast.from_pretrained(self.tmpdirname			,    **snake_case__ )



							def    _a (   self			,    **a_ )				->				Optional[int]:
								return EfficientNetImageProcessor.from_pretrained(self.tmpdirname			,    **snake_case__ )



							def    _a (   self )				->				Dict:
								shutil.rmtree(self.tmpdirname )



							def    _a (   self )				->				List[Any]:
								_UpperCAmelCase			=    [np.random.randint(255			,    size=(3, 30, 400)			,    dtype=np.uinta )]
								_UpperCAmelCase			=    [Image.fromarray(np.moveaxis(snake_case__			,    0			,    -1 ) ) for x in image_inputs]
								return image_inputs



							def    _a (   self )				->				Any:
								_UpperCAmelCase			=    self.get_tokenizer()
								_UpperCAmelCase			=    self.get_rust_tokenizer()
								_UpperCAmelCase			=    self.get_image_processor()

								_UpperCAmelCase			=    AlignProcessor(tokenizer=snake_case__			,    image_processor=snake_case__ )
								processor_slow.save_pretrained(self.tmpdirname )
								_UpperCAmelCase			=    AlignProcessor.from_pretrained(self.tmpdirname			,    use_fast=snake_case__ )

								_UpperCAmelCase			=    AlignProcessor(tokenizer=snake_case__			,    image_processor=snake_case__ )
								processor_fast.save_pretrained(self.tmpdirname )
								_UpperCAmelCase			=    AlignProcessor.from_pretrained(self.tmpdirname )

								self.assertEqual(processor_slow.tokenizer.get_vocab()			,    tokenizer_slow.get_vocab() )
								self.assertEqual(processor_fast.tokenizer.get_vocab()			,    tokenizer_fast.get_vocab() )
								self.assertEqual(tokenizer_slow.get_vocab()			,    tokenizer_fast.get_vocab() )
								self.assertIsInstance(processor_slow.tokenizer			,    snake_case__ )
								self.assertIsInstance(processor_fast.tokenizer			,    snake_case__ )

								self.assertEqual(processor_slow.image_processor.to_json_string()			,    image_processor.to_json_string() )
								self.assertEqual(processor_fast.image_processor.to_json_string()			,    image_processor.to_json_string() )
								self.assertIsInstance(processor_slow.image_processor			,    snake_case__ )
								self.assertIsInstance(processor_fast.image_processor			,    snake_case__ )



							def    _a (   self )				->				Dict:
								_UpperCAmelCase			=    AlignProcessor(tokenizer=self.get_tokenizer()			,    image_processor=self.get_image_processor() )
								processor.save_pretrained(self.tmpdirname )

								_UpperCAmelCase			=    self.get_tokenizer(bos_token="(BOS)"			,    eos_token="(EOS)" )
								_UpperCAmelCase			=    self.get_image_processor(do_normalize=snake_case__			,    padding_value=1.0 )

								_UpperCAmelCase			=    AlignProcessor.from_pretrained(
								    self.tmpdirname			,    bos_token="(BOS)"			,    eos_token="(EOS)"			,    do_normalize=snake_case__			,    padding_value=1.0 )

								self.assertEqual(processor.tokenizer.get_vocab()			,    tokenizer_add_kwargs.get_vocab() )
								self.assertIsInstance(processor.tokenizer			,    snake_case__ )

								self.assertEqual(processor.image_processor.to_json_string()			,    image_processor_add_kwargs.to_json_string() )
								self.assertIsInstance(processor.image_processor			,    snake_case__ )



							def    _a (   self )				->				List[str]:
								_UpperCAmelCase			=    self.get_image_processor()
								_UpperCAmelCase			=    self.get_tokenizer()

								_UpperCAmelCase			=    AlignProcessor(tokenizer=snake_case__			,    image_processor=snake_case__ )

								_UpperCAmelCase			=    self.prepare_image_inputs()

								_UpperCAmelCase			=    image_processor(snake_case__			,    return_tensors="np" )
								_UpperCAmelCase			=    processor(images=snake_case__			,    return_tensors="np" )

								for key in input_image_proc.keys():
									self.assertAlmostEqual(input_image_proc[key].sum()			,    input_processor[key].sum()			,    delta=1e-2 )



							def    _a (   self )				->				str:
								_UpperCAmelCase			=    self.get_image_processor()
								_UpperCAmelCase			=    self.get_tokenizer()

								_UpperCAmelCase			=    AlignProcessor(tokenizer=snake_case__			,    image_processor=snake_case__ )

								_UpperCAmelCase			=    "lower newer"

								_UpperCAmelCase			=    processor(text=snake_case__ )

								_UpperCAmelCase			=    tokenizer(snake_case__			,    padding="max_length"			,    max_length=64 )

								for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]			,    encoded_processor[key] )



							def    _a (   self )				->				Union[str, Any]:
								_UpperCAmelCase			=    self.get_image_processor()
								_UpperCAmelCase			=    self.get_tokenizer()

								_UpperCAmelCase			=    AlignProcessor(tokenizer=snake_case__			,    image_processor=snake_case__ )

								_UpperCAmelCase			=    "lower newer"
								_UpperCAmelCase			=    self.prepare_image_inputs()

								_UpperCAmelCase			=    processor(text=snake_case__			,    images=snake_case__ )

								self.assertListEqual(list(inputs.keys() )			,    ["input_ids", "token_type_ids", "attention_mask", "pixel_values"] )

								# test if it raises when no input is passed
								with pytest.raises(snake_case__ ):
									processor()



							def    _a (   self )				->				Any:
								_UpperCAmelCase			=    self.get_image_processor()
								_UpperCAmelCase			=    self.get_tokenizer()

								_UpperCAmelCase			=    AlignProcessor(tokenizer=snake_case__			,    image_processor=snake_case__ )

								_UpperCAmelCase			=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								_UpperCAmelCase			=    processor.batch_decode(snake_case__ )
								_UpperCAmelCase			=    tokenizer.batch_decode(snake_case__ )

								self.assertListEqual(snake_case__			,    snake_case__ )



							def    _a (   self )				->				Any:
								_UpperCAmelCase			=    self.get_image_processor()
								_UpperCAmelCase			=    self.get_tokenizer()

								_UpperCAmelCase			=    AlignProcessor(tokenizer=snake_case__			,    image_processor=snake_case__ )

								_UpperCAmelCase			=    "lower newer"
								_UpperCAmelCase			=    self.prepare_image_inputs()

								_UpperCAmelCase			=    processor(text=snake_case__			,    images=snake_case__ )

								self.assertListEqual(list(inputs.keys() )			,    processor.model_input_names )












"""simple docstring"""



def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Tuple				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    List[str]							=							len(snake_case				)
     for i in range(length - 1				):
          UpperCAmelCase__						:    Any							=							i
          for k in range(i + 1			,					snake_case				):
               if collection[k] < collection[least]:
                    UpperCAmelCase__						:    Union[str, Any]							=							k
          if least != i:
               UpperCAmelCase__							,				UpperCAmelCase__						:    Union[str, Any]							=							(collection[i], collection[least])
     return collection


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


