




'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 CLIPSegProcessor, ViTImageProcessor






@require_vision
class 		a							(  unittest.TestCase	):


				def     A_					(						self       :   Tuple   ):
									snake_case_		     =							tempfile.mkdtemp()

									# fmt: off
									snake_case_		     =							['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
									# fmt: on
									snake_case_		     =							dict(zip(lowercase_			,							range(len(lowercase_   )   )   )   )
									snake_case_		     =							['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
									snake_case_		     =							{'unk_token': '<unk>'}

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

									snake_case_		     =							{
									    'do_resize': True,
									    'size': 20,
									    'do_center_crop': True,
									    'crop_size': 18,
									    'do_normalize': True,
									    'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
									    'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
									}
									snake_case_		     =							os.path.join(self.tmpdirname			,							lowercase_   )
									with open(self.image_processor_file			,							'''w'''			,							encoding='''utf-8'''   ) as fp:
														json.dump(lowercase_			,							lowercase_   )


				def     A_					(						self       :   Any			,							**lowercase_       :   List[str]   ):
									return CLIPTokenizer.from_pretrained(self.tmpdirname			,							**lowercase_   )


				def     A_					(						self       :   Optional[int]			,							**lowercase_       :   Optional[Any]   ):
									return CLIPTokenizerFast.from_pretrained(self.tmpdirname			,							**lowercase_   )


				def     A_					(						self       :   Optional[Any]			,							**lowercase_       :   Optional[int]   ):
									return ViTImageProcessor.from_pretrained(self.tmpdirname			,							**lowercase_   )


				def     A_					(						self       :   List[Any]   ):
									shutil.rmtree(self.tmpdirname   )


				def     A_					(						self       :   Optional[int]   ):
									snake_case_		     =							[np.random.randint(255			,							size=(3, 30, 400)			,							dtype=np.uinta   )]

									snake_case_		     =							[Image.fromarray(np.moveaxis(lowercase_			,							0			,							-1   )   ) for x in image_inputs]

									return image_inputs


				def     A_					(						self       :   str   ):
									snake_case_		     =							self.get_tokenizer()
									snake_case_		     =							self.get_rust_tokenizer()
									snake_case_		     =							self.get_image_processor()

									snake_case_		     =							CLIPSegProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )
									processor_slow.save_pretrained(self.tmpdirname   )
									snake_case_		     =							CLIPSegProcessor.from_pretrained(self.tmpdirname			,							use_fast=lowercase_   )

									snake_case_		     =							CLIPSegProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )
									processor_fast.save_pretrained(self.tmpdirname   )
									snake_case_		     =							CLIPSegProcessor.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			,							lowercase_   )
									self.assertIsInstance(processor_fast.tokenizer			,							lowercase_   )

									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			,							lowercase_   )
									self.assertIsInstance(processor_fast.image_processor			,							lowercase_   )


				def     A_					(						self       :   int   ):
									snake_case_		     =							CLIPSegProcessor(tokenizer=self.get_tokenizer()			,							image_processor=self.get_image_processor()   )
									processor.save_pretrained(self.tmpdirname   )

									snake_case_		     =							self.get_tokenizer(bos_token='''(BOS)'''			,							eos_token='''(EOS)'''   )
									snake_case_		     =							self.get_image_processor(do_normalize=lowercase_			,							padding_value=1.0   )

									snake_case_		     =							CLIPSegProcessor.from_pretrained(
									    self.tmpdirname			,							bos_token='''(BOS)'''			,							eos_token='''(EOS)'''			,							do_normalize=lowercase_			,							padding_value=1.0   )

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

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


				def     A_					(						self       :   List[Any]   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							CLIPSegProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							self.prepare_image_inputs()

									snake_case_		     =							image_processor(lowercase_			,							return_tensors='''np'''   )
									snake_case_		     =							processor(images=lowercase_			,							return_tensors='''np'''   )

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


				def     A_					(						self       :   int   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							CLIPSegProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							'lower newer'

									snake_case_		     =							processor(text=lowercase_   )

									snake_case_		     =							tokenizer(lowercase_   )

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


				def     A_					(						self       :   Optional[Any]   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							CLIPSegProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							'lower newer'
									snake_case_		     =							self.prepare_image_inputs()

									snake_case_		     =							processor(text=lowercase_			,							images=lowercase_   )

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

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


				def     A_					(						self       :   List[str]   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							CLIPSegProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							self.prepare_image_inputs()
									snake_case_		     =							self.prepare_image_inputs()

									snake_case_		     =							processor(images=lowercase_			,							visual_prompt=lowercase_   )

									self.assertListEqual(list(inputs.keys()   )			,							['''pixel_values''', '''conditional_pixel_values''']   )

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


				def     A_					(						self       :   List[Any]   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							CLIPSegProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

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

									snake_case_		     =							processor.batch_decode(lowercase_   )
									snake_case_		     =							tokenizer.batch_decode(lowercase_   )

									self.assertListEqual(lowercase_			,							lowercase_   )






"""simple docstring"""







def     UpperCAmelCase_					(				__a   :			list						):




			'''simple docstring'''



			if len(__a						) <= 1:
						return lst

			_lowerCamelCase					:					str	       =    1

			while i < len(__a						):
						if lst[i - 1] <= lst[i]:
									i += 1
						else:
									_lowerCamelCase       ,							_lowerCamelCase					:					Optional[Any]	       =    lst[i], lst[i - 1]
									i -= 1
									if i == 0:
												_lowerCamelCase					:					Dict	       =    1

			return lst


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





