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 CLIPImageProcessor, CLIPProcessor




@require_vision
class 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						tempfile.mkdtemp()

          # fmt: off
          _lowerCamelCase     :		List[Any] 						=						["""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
          _lowerCamelCase     :		List[Any] 						=						dict(zip(_UpperCamelCase    ,  range(len(_UpperCamelCase))))
          _lowerCamelCase     :		List[str] 						=						["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
          _lowerCamelCase     :		Optional[Any] 						=						{"""unk_token""": """<unk>"""}

          _lowerCamelCase     :		List[str] 						=						os.path.join(self.tmpdirname    ,  VOCAB_FILES_NAMES["""vocab_file"""])
          _lowerCamelCase     :		Optional[Any] 						=						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(_UpperCamelCase) + """\n""")
          with open(self.merges_file    ,  """w"""    ,  encoding="""utf-8""") as fp:
              fp.write("""\n""".join(_UpperCamelCase))

          _lowerCamelCase     :		List[str] 						=						{
              """do_resize""": True,
              """size""": 20,
              """do_center_crop""": True,
              """crop_size""": 18,
              """do_normalize""": True,
              """image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
              """image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
          }
          _lowerCamelCase     :		List[str] 						=						os.path.join(self.tmpdirname    ,  _UpperCamelCase)
          with open(self.image_processor_file    ,  """w"""    ,  encoding="""utf-8""") as fp:
              json.dump(_UpperCamelCase    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict    ,  **_UpperCamelCase     :				int)				->Dict:


          """simple docstring"""


          return CLIPTokenizer.from_pretrained(self.tmpdirname    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  **_UpperCamelCase     :				Union[str, Any])				->Any:


          """simple docstring"""


          return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  **_UpperCamelCase     :				List[str])				->List[Any]:


          """simple docstring"""


          return CLIPImageProcessor.from_pretrained(self.tmpdirname    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->Optional[Any]:


          """simple docstring"""


          shutil.rmtree(self.tmpdirname)



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


          """simple docstring"""


          _lowerCamelCase     :		str 						=						[np.random.randint(255    ,  size=(3, 30, 400)    ,  dtype=np.uinta)]

          _lowerCamelCase     :		List[Any] 						=						[Image.fromarray(np.moveaxis(_UpperCamelCase    ,  0    ,  -1)) for x in image_inputs]

          return image_inputs



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->str:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.get_tokenizer()
          _lowerCamelCase     :		Tuple 						=						self.get_rust_tokenizer()
          _lowerCamelCase     :		List[Any] 						=						self.get_image_processor()

          _lowerCamelCase     :		str 						=						CLIPProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)
          processor_slow.save_pretrained(self.tmpdirname)
          _lowerCamelCase     :		Optional[int] 						=						CLIPProcessor.from_pretrained(self.tmpdirname    ,  use_fast=_UpperCamelCase)

          _lowerCamelCase     :		Optional[Any] 						=						CLIPProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)
          processor_fast.save_pretrained(self.tmpdirname)
          _lowerCamelCase     :		str 						=						CLIPProcessor.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    ,  _UpperCamelCase)
          self.assertIsInstance(processor_fast.tokenizer    ,  _UpperCamelCase)

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



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


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						CLIPProcessor(tokenizer=self.get_tokenizer()    ,  image_processor=self.get_image_processor())
          processor.save_pretrained(self.tmpdirname)

          _lowerCamelCase     :		int 						=						self.get_tokenizer(bos_token="""(BOS)"""    ,  eos_token="""(EOS)""")
          _lowerCamelCase     :		Tuple 						=						self.get_image_processor(do_normalize=_UpperCamelCase    ,  padding_value=1.0)

          _lowerCamelCase     :		str 						=						CLIPProcessor.from_pretrained(
              self.tmpdirname    ,  bos_token="""(BOS)"""    ,  eos_token="""(EOS)"""    ,  do_normalize=_UpperCamelCase    ,  padding_value=1.0)

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

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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->int:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						self.get_image_processor()
          _lowerCamelCase     :		int 						=						self.get_tokenizer()

          _lowerCamelCase     :		List[Any] 						=						CLIPProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		Union[str, Any] 						=						self.prepare_image_inputs()

          _lowerCamelCase     :		List[Any] 						=						image_processor(_UpperCamelCase    ,  return_tensors="""np""")
          _lowerCamelCase     :		Any 						=						processor(images=_UpperCamelCase    ,  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 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->str:


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						self.get_image_processor()
          _lowerCamelCase     :		int 						=						self.get_tokenizer()

          _lowerCamelCase     :		Any 						=						CLIPProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		Tuple 						=						"""lower newer"""

          _lowerCamelCase     :		Optional[int] 						=						processor(text=_UpperCamelCase)

          _lowerCamelCase     :		int 						=						tokenizer(_UpperCamelCase)

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



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


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						self.get_image_processor()
          _lowerCamelCase     :		Optional[Any] 						=						self.get_tokenizer()

          _lowerCamelCase     :		Union[str, Any] 						=						CLIPProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		Union[str, Any] 						=						"""lower newer"""
          _lowerCamelCase     :		Optional[int] 						=						self.prepare_image_inputs()

          _lowerCamelCase     :		Union[str, Any] 						=						processor(text=_UpperCamelCase    ,  images=_UpperCamelCase)

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

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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->str:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.get_image_processor()
          _lowerCamelCase     :		str 						=						self.get_tokenizer()

          _lowerCamelCase     :		List[str] 						=						CLIPProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		List[Any] 						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          _lowerCamelCase     :		Any 						=						processor.batch_decode(_UpperCamelCase)
          _lowerCamelCase     :		Union[str, Any] 						=						tokenizer.batch_decode(_UpperCamelCase)

          self.assertListEqual(_UpperCamelCase    ,  _UpperCamelCase)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		str 						=						self.get_image_processor()
          _lowerCamelCase     :		Any 						=						self.get_tokenizer()

          _lowerCamelCase     :		Optional[Any] 						=						CLIPProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		Any 						=						"""lower newer"""
          _lowerCamelCase     :		Any 						=						self.prepare_image_inputs()

          _lowerCamelCase     :		List[str] 						=						processor(text=_UpperCamelCase    ,  images=_UpperCamelCase)

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





lowerCAmelCase			:							Tuple     =0  # The first color of the flag.
lowerCAmelCase			:							Union[str, Any]     =1  # The second color of the flag.
lowerCAmelCase			:							Any     =2  # The third color of the flag.
lowerCAmelCase			:							List[str]     =(red, white, blue)







def 						A__  (					__A	):







    '''simple docstring'''
    if not sequence:
        return []
    if len(__A	) == 1:
        return list(__A	)
    _lowerCamelCase     :		int 						=						0
    _lowerCamelCase     :		Dict 						=						len(__A	) - 1
    _lowerCamelCase     :		str 						=						0
    while mid <= high:
        if sequence[mid] == colors[0]:
            _lowerCamelCase						,							_lowerCamelCase     :		Tuple 						=						sequence[mid], sequence[low]
            low += 1
            mid += 1
        elif sequence[mid] == colors[1]:
            mid += 1
        elif sequence[mid] == colors[2]:
            _lowerCamelCase						,							_lowerCamelCase     :		str 						=						sequence[high], sequence[mid]
            high -= 1
        else:
            _lowerCamelCase     :		int 						=						F"""The elements inside the sequence must contains only {colors} values"""
            raise ValueError(__A	)
    return sequence


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lowerCAmelCase			:							List[str]     =input("Enter numbers separated by commas:\n").strip()
    lowerCAmelCase			:							Dict     =[int(item.strip()) for item in user_input.split(",")]
    print(F"""{dutch_national_flag_sort(unsorted)}""")




