
'''simple docstring'''



import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert 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 FlavaImageProcessor, FlavaProcessor
     from transformers.models.flava.image_processing_flava import (
         FLAVA_CODEBOOK_MEAN,
         FLAVA_CODEBOOK_STD,
         FLAVA_IMAGE_MEAN,
         FLAVA_IMAGE_STD,
     )

@require_vision
class 				_a			(unittest.TestCase):





       """simple docstring"""




       def     UpperCamelCase			(							self )      ->     Tuple:
          _SCREAMING_SNAKE_CASE       	=			tempfile.mkdtemp()

          # fmt: off
          _SCREAMING_SNAKE_CASE       	=			["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
          # fmt: on
          _SCREAMING_SNAKE_CASE       	=			os.path.join(self.tmpdirname    ,							VOCAB_FILES_NAMES["""vocab_file"""] )

          with open(self.vocab_file    ,							"""w"""    ,							encoding="""utf-8""" ) as fp:
             fp.write("""""".join([x + """\n""" for x in vocab_tokens] ) )

          _SCREAMING_SNAKE_CASE       	=			{
              """image_mean""": FLAVA_IMAGE_MEAN,
              """image_std""": FLAVA_IMAGE_STD,
              """do_normalize""": True,
              """do_resize""": True,
              """size""": 2_24,
              """do_center_crop""": True,
              """crop_size""": 2_24,
              """input_size_patches""": 14,
              """total_mask_patches""": 75,
              """mask_group_max_patches""": None,
              """mask_group_min_patches""": 16,
              """mask_group_min_aspect_ratio""": 0.3,
              """mask_group_max_aspect_ratio""": None,
              """codebook_do_resize""": True,
              """codebook_size""": 1_12,
              """codebook_do_center_crop""": True,
              """codebook_crop_size""": 1_12,
              """codebook_do_map_pixels""": True,
              """codebook_do_normalize""": True,
              """codebook_image_mean""": FLAVA_CODEBOOK_MEAN,
              """codebook_image_std""": FLAVA_CODEBOOK_STD,
          }

          _SCREAMING_SNAKE_CASE       	=			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__ )      ->     str:
          return BertTokenizer.from_pretrained(self.tmpdirname    ,							**A__ )






       def     UpperCamelCase			(							self    ,							**A__ )      ->     List[str]:
          return BertTokenizerFast.from_pretrained(self.tmpdirname    ,							**A__ )






       def     UpperCamelCase			(							self    ,							**A__ )      ->     int:
          return FlavaImageProcessor.from_pretrained(self.tmpdirname    ,							**A__ )






       def     UpperCamelCase			(							self )      ->     Tuple:
          shutil.rmtree(self.tmpdirname )






       def     UpperCamelCase			(							self )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			[np.random.randint(2_55    ,							size=(3, 30, 4_00)    ,							dtype=np.uinta )]

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

          return image_inputs






       def     UpperCamelCase			(							self )      ->     List[str]:
          _SCREAMING_SNAKE_CASE       	=			self.get_tokenizer()
          _SCREAMING_SNAKE_CASE       	=			self.get_rust_tokenizer()
          _SCREAMING_SNAKE_CASE       	=			self.get_image_processor()

          _SCREAMING_SNAKE_CASE       	=			FlavaProcessor(tokenizer=A__    ,							image_processor=A__ )
          processor_slow.save_pretrained(self.tmpdirname )
          _SCREAMING_SNAKE_CASE       	=			FlavaProcessor.from_pretrained(self.tmpdirname    ,							use_fast=A__ )

          _SCREAMING_SNAKE_CASE       	=			FlavaProcessor(tokenizer=A__    ,							image_processor=A__ )
          processor_fast.save_pretrained(self.tmpdirname )
          _SCREAMING_SNAKE_CASE       	=			FlavaProcessor.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 )      ->     Dict:
          _SCREAMING_SNAKE_CASE       	=			FlavaProcessor(tokenizer=self.get_tokenizer()    ,							image_processor=self.get_image_processor() )
          processor.save_pretrained(self.tmpdirname )

          _SCREAMING_SNAKE_CASE       	=			self.get_tokenizer(bos_token="""(BOS)"""    ,							eos_token="""(EOS)""" )
          _SCREAMING_SNAKE_CASE       	=			self.get_image_processor(do_normalize=A__    ,							padding_value=1.0 )

          _SCREAMING_SNAKE_CASE       	=			FlavaProcessor.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 )      ->     Optional[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.get_image_processor()
          _SCREAMING_SNAKE_CASE       	=			self.get_tokenizer()

          _SCREAMING_SNAKE_CASE       	=			FlavaProcessor(tokenizer=A__    ,							image_processor=A__ )

          _SCREAMING_SNAKE_CASE       	=			self.prepare_image_inputs()

          _SCREAMING_SNAKE_CASE       	=			image_processor(A__    ,							return_tensors="""np""" )
          _SCREAMING_SNAKE_CASE       	=			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 )

          # With rest of the args
          random.seed(12_34 )
          _SCREAMING_SNAKE_CASE       	=			image_processor(
              A__    ,							return_image_mask=A__    ,							return_codebook_pixels=A__    ,							return_tensors="""np""" )
          random.seed(12_34 )
          _SCREAMING_SNAKE_CASE       	=			processor(
              images=A__    ,							return_image_mask=A__    ,							return_codebook_pixels=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 )      ->     List[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.get_image_processor()
          _SCREAMING_SNAKE_CASE       	=			self.get_tokenizer()

          _SCREAMING_SNAKE_CASE       	=			FlavaProcessor(tokenizer=A__    ,							image_processor=A__ )

          _SCREAMING_SNAKE_CASE       	=			"""lower newer"""

          _SCREAMING_SNAKE_CASE       	=			processor(text=A__ )

          _SCREAMING_SNAKE_CASE       	=			tokenizer(A__ )

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






       def     UpperCamelCase			(							self )      ->     List[str]:
          _SCREAMING_SNAKE_CASE       	=			self.get_image_processor()
          _SCREAMING_SNAKE_CASE       	=			self.get_tokenizer()

          _SCREAMING_SNAKE_CASE       	=			FlavaProcessor(tokenizer=A__    ,							image_processor=A__ )

          _SCREAMING_SNAKE_CASE       	=			"""lower newer"""
          _SCREAMING_SNAKE_CASE       	=			self.prepare_image_inputs()

          _SCREAMING_SNAKE_CASE       	=			processor(text=A__    ,							images=A__ )

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

          # add extra args
          _SCREAMING_SNAKE_CASE       	=			processor(text=A__    ,							images=A__    ,							return_codebook_pixels=A__    ,							return_image_mask=A__ )

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

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






       def     UpperCamelCase			(							self )      ->     Any:
          _SCREAMING_SNAKE_CASE       	=			self.get_image_processor()
          _SCREAMING_SNAKE_CASE       	=			self.get_tokenizer()

          _SCREAMING_SNAKE_CASE       	=			FlavaProcessor(tokenizer=A__    ,							image_processor=A__ )

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

          _SCREAMING_SNAKE_CASE       	=			processor.batch_decode(A__ )
          _SCREAMING_SNAKE_CASE       	=			tokenizer.batch_decode(A__ )

          self.assertListEqual(A__    ,							A__ )






       def     UpperCamelCase			(							self )      ->     Any:
          _SCREAMING_SNAKE_CASE       	=			self.get_image_processor()
          _SCREAMING_SNAKE_CASE       	=			self.get_tokenizer()

          _SCREAMING_SNAKE_CASE       	=			FlavaProcessor(tokenizer=A__    ,							image_processor=A__ )

          _SCREAMING_SNAKE_CASE       	=			"""lower newer"""
          _SCREAMING_SNAKE_CASE       	=			self.prepare_image_inputs()

          _SCREAMING_SNAKE_CASE       	=			processor(text=A__    ,							images=A__ )

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








'''simple docstring'''



from cva import destroyAllWindows, imread, imshow, waitKey
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			img.shape[0], img.shape[1]

   def to_grayscale(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  ):
      return 0.2126 * red + 0.587 * green + 0.114 * blue

   def normalize(SCREAMING_SNAKE_CASE_  ):
      return min(SCREAMING_SNAKE_CASE_      , 2_55  )

   for i in range(SCREAMING_SNAKE_CASE_  ):
      for j in range(SCREAMING_SNAKE_CASE_  ):
         _SCREAMING_SNAKE_CASE       	=			int(to_grayscale(*img[i][j]  )  )
         _SCREAMING_SNAKE_CASE       	=			[
             normalize(SCREAMING_SNAKE_CASE_  ),
             normalize(greyscale + factor  ),
             normalize(greyscale + 2 * factor  ),
         ]

   return img


if __name__ == "__main__":
     # read original image
     UpperCamelCase__ :						Optional[int]    							=					{
         percentage: imread("image_data/lena.jpg", 1) for percentage in (10, 20, 30, 40)
     }

     for percentage, img in images.items():
          make_sepia(img, percentage)

     for percentage, img in images.items():
          imshow(f"""Original image with sepia (factor: {percentage})""", img)

     waitKey(0)
     destroyAllWindows()






