
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       ):

       """simple docstring"""





       def     SCREAMING_SNAKE_CASE  (					self	):
        _UpperCAmelCase 				=tempfile.mkdtemp()

        # fmt: off
        _UpperCAmelCase 				=["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
        _UpperCAmelCase 				=dict(zip(_snake_case       ,					range(len(_snake_case	)	)	)	)
        _UpperCAmelCase 				=["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
        _UpperCAmelCase 				={"unk_token": "<unk>"}

        _UpperCAmelCase 				=os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES["vocab_file"]	)
        _UpperCAmelCase 				=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	)	)

        _UpperCAmelCase 				={
            "do_resize": True,
            "size": 20,
            "do_center_crop": True,
            "crop_size": 18,
            "do_normalize": True,
            "image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
            "image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
        }
        _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     SCREAMING_SNAKE_CASE  (					self       ,					**_snake_case	):
        return CLIPTokenizer.from_pretrained(self.tmpdirname       ,					**_snake_case	)
       def     SCREAMING_SNAKE_CASE  (					self       ,					**_snake_case	):
        return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,					**_snake_case	)
       def     SCREAMING_SNAKE_CASE  (					self       ,					**_snake_case	):
        return ViTImageProcessor.from_pretrained(self.tmpdirname       ,					**_snake_case	)
       def     SCREAMING_SNAKE_CASE  (					self	):
        shutil.rmtree(self.tmpdirname	)
       def     SCREAMING_SNAKE_CASE  (					self	):
        _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     SCREAMING_SNAKE_CASE  (					self	):
        _UpperCAmelCase 				=self.get_tokenizer()
        _UpperCAmelCase 				=self.get_rust_tokenizer()
        _UpperCAmelCase 				=self.get_image_processor()

        _UpperCAmelCase 				=CLIPSegProcessor(tokenizer=_snake_case       ,					image_processor=_snake_case	)
        processor_slow.save_pretrained(self.tmpdirname	)
        _UpperCAmelCase 				=CLIPSegProcessor.from_pretrained(self.tmpdirname       ,					use_fast=_snake_case	)

        _UpperCAmelCase 				=CLIPSegProcessor(tokenizer=_snake_case       ,					image_processor=_snake_case	)
        processor_fast.save_pretrained(self.tmpdirname	)
        _UpperCAmelCase 				=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       ,					_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     SCREAMING_SNAKE_CASE  (					self	):
        _UpperCAmelCase 				=CLIPSegProcessor(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 				=CLIPSegProcessor.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     SCREAMING_SNAKE_CASE  (					self	):
        _UpperCAmelCase 				=self.get_image_processor()
        _UpperCAmelCase 				=self.get_tokenizer()

        _UpperCAmelCase 				=CLIPSegProcessor(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_feat_extract.keys():
         self.assertAlmostEqual(input_feat_extract[key].sum()       ,					input_processor[key].sum()       ,					delta=1E-2	)
       def     SCREAMING_SNAKE_CASE  (					self	):
        _UpperCAmelCase 				=self.get_image_processor()
        _UpperCAmelCase 				=self.get_tokenizer()

        _UpperCAmelCase 				=CLIPSegProcessor(tokenizer=_snake_case       ,					image_processor=_snake_case	)

        _UpperCAmelCase 				="lower newer"

        _UpperCAmelCase 				=processor(text=_snake_case	)

        _UpperCAmelCase 				=tokenizer(_snake_case	)

        for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]       ,					encoded_processor[key]	)
       def     SCREAMING_SNAKE_CASE  (					self	):
        _UpperCAmelCase 				=self.get_image_processor()
        _UpperCAmelCase 				=self.get_tokenizer()

        _UpperCAmelCase 				=CLIPSegProcessor(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", "attention_mask", "pixel_values"]	)

        # test if it raises when no input is passed
        with pytest.raises(_snake_case	):
         processor()
       def     SCREAMING_SNAKE_CASE  (					self	):
        _UpperCAmelCase 				=self.get_image_processor()
        _UpperCAmelCase 				=self.get_tokenizer()

        _UpperCAmelCase 				=CLIPSegProcessor(tokenizer=_snake_case       ,					image_processor=_snake_case	)

        _UpperCAmelCase 				=self.prepare_image_inputs()
        _UpperCAmelCase 				=self.prepare_image_inputs()

        _UpperCAmelCase 				=processor(images=_snake_case       ,					visual_prompt=_snake_case	)

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

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

       def     SCREAMING_SNAKE_CASE  (					self	):
        _UpperCAmelCase 				=self.get_image_processor()
        _UpperCAmelCase 				=self.get_tokenizer()

        _UpperCAmelCase 				=CLIPSegProcessor(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	)










"""simple docstring"""




from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge






@total_ordering
class       UpperCAmelCase_    (							_UpperCamelCase		):



      def __lt__(	self	:      int	,      A	:      Dict		):
           return self[-1] < other[-1]



      def __eq__(	self	:      Union[str, Any]	,      A	:      Union[str, Any]		):
           return self[-1] == other[-1]







def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		list							)    ->  list:





     '''simple docstring'''

     _UpperCAmelCase     :      list[Stack]														=       []
     # sort into stacks
     for element in collection:
          _UpperCAmelCase     :      Any														=       Stack([element]							)
          _UpperCAmelCase     :      str														=       bisect_left(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
          if i != len(SCREAMING_SNAKE_CASE__							):
               stacks[i].append(SCREAMING_SNAKE_CASE__							)
          else:
               stacks.append(SCREAMING_SNAKE_CASE__							)

    # use a heap-based merge to merge stack efficiently
     _UpperCAmelCase     :      Optional[Any]														=       merge(*(reversed(SCREAMING_SNAKE_CASE__							) for stack in stacks)							)
     return collection


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