





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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _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(A,       range(len(A      )      )      )      )
            _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(A      ) + '''\n'''      )
            with open(self.merges_file,       '''w''',       encoding='''utf-8'''      ) as fp:
                   fp.write('''\n'''.join(A      )      )

            _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,       A      )
            with open(self.image_processor_file,       '''w''',       encoding='''utf-8'''      ) as fp:
                   json.dump(A,       A      )

     def 	_UpperCamelCase     (      self,       **A      ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self,       **A      ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self,       **A      ):





            """simple docstring"""

            return ViTImageProcessor.from_pretrained(self.tmpdirname,       **A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            shutil.rmtree(self.tmpdirname      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

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

            return image_inputs

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_tokenizer()
            _UpperCamelCase              =						self.get_rust_tokenizer()
            _UpperCamelCase              =						self.get_image_processor()

            _UpperCamelCase              =						CLIPSegProcessor(tokenizer=A,       image_processor=A      )
            processor_slow.save_pretrained(self.tmpdirname      )
            _UpperCamelCase              =						CLIPSegProcessor.from_pretrained(self.tmpdirname,       use_fast=A      )

            _UpperCamelCase              =						CLIPSegProcessor(tokenizer=A,       image_processor=A      )
            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,       A      )
            self.assertIsInstance(processor_fast.tokenizer,       A      )

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _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=A,       padding_value=1.0      )

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

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

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_image_processor()
            _UpperCamelCase              =						self.get_tokenizer()

            _UpperCamelCase              =						CLIPSegProcessor(tokenizer=A,       image_processor=A      )

            _UpperCamelCase              =						self.prepare_image_inputs()

            _UpperCamelCase              =						image_processor(A,       return_tensors='''np'''      )
            _UpperCamelCase              =						processor(images=A,       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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_image_processor()
            _UpperCamelCase              =						self.get_tokenizer()

            _UpperCamelCase              =						CLIPSegProcessor(tokenizer=A,       image_processor=A      )

            _UpperCamelCase              =						'''lower newer'''

            _UpperCamelCase              =						processor(text=A      )

            _UpperCamelCase              =						tokenizer(A      )

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_image_processor()
            _UpperCamelCase              =						self.get_tokenizer()

            _UpperCamelCase              =						CLIPSegProcessor(tokenizer=A,       image_processor=A      )

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

            _UpperCamelCase              =						processor(text=A,       images=A      )

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

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_image_processor()
            _UpperCamelCase              =						self.get_tokenizer()

            _UpperCamelCase              =						CLIPSegProcessor(tokenizer=A,       image_processor=A      )

            _UpperCamelCase              =						self.prepare_image_inputs()
            _UpperCamelCase              =						self.prepare_image_inputs()

            _UpperCamelCase              =						processor(images=A,       visual_prompt=A      )

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

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



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_image_processor()
            _UpperCamelCase              =						self.get_tokenizer()

            _UpperCamelCase              =						CLIPSegProcessor(tokenizer=A,       image_processor=A      )

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

            _UpperCamelCase              =						processor.batch_decode(A      )
            _UpperCamelCase              =						tokenizer.batch_decode(A      )

            self.assertListEqual(A,       A      )













def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase = 1_000					):
       _UpperCamelCase              =						3
       _UpperCamelCase              =						0
       while a < n:
              if a % 3 == 0 or a % 5 == 0:
                     result += a
              elif a % 15 == 0:
                     result -= a
              a += 1
       return result


if __name__ == "__main__":
       print(F'''{solution() = }''')






