

"""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 			__magic_name__       (       unittest.TestCase ):
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         _lowercase:			Any       =     tempfile.mkdtemp()

         # fmt: off
         _lowercase:			int       =     ['''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
         _lowercase:			Optional[int]       =     dict(zip(A_ ,				range(len(A_       )       )       )       )
         _lowercase:			str       =     ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
         _lowercase:			Optional[int]       =     {'''unk_token''': '''<unk>'''}

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

         _lowercase:			str       =     {
             '''do_resize''': True,
             '''size''': 20,
             '''do_center_crop''': True,
             '''crop_size''': 18,
             '''do_normalize''': True,
             '''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
             '''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
         }
         _lowercase:			Dict       =     os.path.join(self.tmpdirname ,				A_       )
         with open(self.image_processor_file ,				'''w''' ,				encoding='''utf-8'''       ) as fp:
           json.dump(A_ ,				A_       )
       def 							lowercase_							(     self ,				**A_       )     ->	str:





         """simple docstring"""




         return CLIPTokenizer.from_pretrained(self.tmpdirname ,				**A_       )
       def 							lowercase_							(     self ,				**A_       )     ->	int:





         """simple docstring"""




         return CLIPTokenizerFast.from_pretrained(self.tmpdirname ,				**A_       )
       def 							lowercase_							(     self ,				**A_       )     ->	str:





         """simple docstring"""




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





         """simple docstring"""




         shutil.rmtree(self.tmpdirname       )
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




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

         _lowercase:			Optional[int]       =     [Image.fromarray(np.moveaxis(A_ ,				0 ,				-1       )       ) for x in image_inputs]

         return image_inputs
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         _lowercase:			List[Any]       =     self.get_tokenizer()
         _lowercase:			Dict       =     self.get_rust_tokenizer()
         _lowercase:			Union[str, Any]       =     self.get_image_processor()

         _lowercase:			Any       =     CLIPSegProcessor(tokenizer=A_ ,				image_processor=A_       )
         processor_slow.save_pretrained(self.tmpdirname       )
         _lowercase:			Any       =     CLIPSegProcessor.from_pretrained(self.tmpdirname ,				use_fast=A_       )

         _lowercase:			Tuple       =     CLIPSegProcessor(tokenizer=A_ ,				image_processor=A_       )
         processor_fast.save_pretrained(self.tmpdirname       )
         _lowercase:			Any       =     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 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     CLIPSegProcessor(tokenizer=self.get_tokenizer() ,				image_processor=self.get_image_processor()       )
         processor.save_pretrained(self.tmpdirname       )

         _lowercase:			Dict       =     self.get_tokenizer(bos_token='''(BOS)''' ,				eos_token='''(EOS)'''       )
         _lowercase:			Tuple       =     self.get_image_processor(do_normalize=A_ ,				padding_value=1.0       )

         _lowercase:			Union[str, Any]       =     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 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Any       =     self.get_image_processor()
         _lowercase:			Tuple       =     self.get_tokenizer()

         _lowercase:			Union[str, Any]       =     CLIPSegProcessor(tokenizer=A_ ,				image_processor=A_       )

         _lowercase:			Tuple       =     self.prepare_image_inputs()

         _lowercase:			Optional[Any]       =     image_processor(A_ ,				return_tensors='''np'''       )
         _lowercase:			Any       =     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 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         _lowercase:			str       =     self.get_image_processor()
         _lowercase:			int       =     self.get_tokenizer()

         _lowercase:			Optional[int]       =     CLIPSegProcessor(tokenizer=A_ ,				image_processor=A_       )

         _lowercase:			Union[str, Any]       =     '''lower newer'''

         _lowercase:			str       =     processor(text=A_       )

         _lowercase:			Any       =     tokenizer(A_       )

         for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key] ,				encoded_processor[key]       )
       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         _lowercase:			str       =     self.get_image_processor()
         _lowercase:			str       =     self.get_tokenizer()

         _lowercase:			Union[str, Any]       =     CLIPSegProcessor(tokenizer=A_ ,				image_processor=A_       )

         _lowercase:			Dict       =     '''lower newer'''
         _lowercase:			Any       =     self.prepare_image_inputs()

         _lowercase:			Optional[Any]       =     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 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         _lowercase:			Any       =     self.get_image_processor()
         _lowercase:			int       =     self.get_tokenizer()

         _lowercase:			Any       =     CLIPSegProcessor(tokenizer=A_ ,				image_processor=A_       )

         _lowercase:			List[Any]       =     self.prepare_image_inputs()
         _lowercase:			Tuple       =     self.prepare_image_inputs()

         _lowercase:			Dict       =     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 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			int       =     self.get_image_processor()
         _lowercase:			List[Any]       =     self.get_tokenizer()

         _lowercase:			Union[str, Any]       =     CLIPSegProcessor(tokenizer=A_ ,				image_processor=A_       )

         _lowercase:			str       =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         _lowercase:			Tuple       =     processor.batch_decode(A_       )
         _lowercase:			Optional[int]       =     tokenizer.batch_decode(A_       )

         self.assertListEqual(A_ ,				A_       )


"""simple docstring"""




from __future__ import annotations

import numpy as np




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase ,				_lowercase:			List[str]       =     np.shape(_UpperCamelCase						)
  if rows != columns:
    _lowercase:			int       =     (
        '''\'table\' has to be of square shaped array but got a '''
        f'''{rows}x{columns} array:\n{table}'''
    )
    raise ValueError(_UpperCamelCase						)

  _lowercase:			Tuple       =     np.zeros((rows, columns)						)
  _lowercase:			Tuple       =     np.zeros((rows, columns)						)
  for i in range(_UpperCamelCase						):
    for j in range(_UpperCamelCase						):
      _lowercase:			List[Any]       =     sum(lower[i][k] * upper[k][j] for k in range(_UpperCamelCase						)						)
      if upper[j][j] == 0:
        raise ArithmeticError('''No LU decomposition exists'''						)
      _lowercase:			Any       =     (table[i][j] - total) / upper[j][j]
    _lowercase:			Any       =     1
    for j in range(_UpperCamelCase ,				_UpperCamelCase						):
      _lowercase:			Optional[int]       =     sum(lower[i][k] * upper[k][j] for k in range(_UpperCamelCase						)						)
      _lowercase:			List[str]       =     table[i][j] - total
  return lower, upper


if __name__ == "__main__":
  import doctest

  doctest.testmod()
