
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 					__lowercase		(					unittest.TestCase       ):


					def        lowerCAmelCase_    (	self						)		->							Dict:





									'''simple docstring'''
									A_       	=  tempfile.mkdtemp()

									# fmt: off
									A_       	=  ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
									# fmt: on
									A_       	=  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]						)						)

									A_       	=  {
									    '''image_mean''': FLAVA_IMAGE_MEAN,
									    '''image_std''': FLAVA_IMAGE_STD,
									    '''do_normalize''': True,
									    '''do_resize''': True,
									    '''size''': 2_2_4,
									    '''do_center_crop''': True,
									    '''crop_size''': 2_2_4,
									    '''input_size_patches''': 1_4,
									    '''total_mask_patches''': 7_5,
									    '''mask_group_max_patches''': None,
									    '''mask_group_min_patches''': 1_6,
									    '''mask_group_min_aspect_ratio''': 0.3,
									    '''mask_group_max_aspect_ratio''': None,
									    '''codebook_do_resize''': True,
									    '''codebook_size''': 1_1_2,
									    '''codebook_do_center_crop''': True,
									    '''codebook_crop_size''': 1_1_2,
									    '''codebook_do_map_pixels''': True,
									    '''codebook_do_normalize''': True,
									    '''codebook_image_mean''': FLAVA_CODEBOOK_MEAN,
									    '''codebook_image_std''': FLAVA_CODEBOOK_STD,
									}

									A_       	=  os.path.join(self.tmpdirname       ,       a__						)
									with open(self.image_processor_file       ,       '''w'''       ,       encoding='''utf-8'''						) as fp:
													json.dump(a__       ,       a__						)


					def        lowerCAmelCase_    (	self       ,       **a__						)		->							int:





									'''simple docstring'''
									return BertTokenizer.from_pretrained(self.tmpdirname       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       **a__						)		->							Tuple:





									'''simple docstring'''
									return BertTokenizerFast.from_pretrained(self.tmpdirname       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       **a__						)		->							str:





									'''simple docstring'''
									return FlavaImageProcessor.from_pretrained(self.tmpdirname       ,       **a__						)


					def        lowerCAmelCase_    (	self						)		->							List[str]:





									'''simple docstring'''
									shutil.rmtree(self.tmpdirname						)


					def        lowerCAmelCase_    (	self						)		->							Dict:





									'''simple docstring'''
									A_       	=  [np.random.randint(2_5_5       ,       size=(3, 3_0, 4_0_0)       ,       dtype=np.uinta						)]

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

									return image_inputs


					def        lowerCAmelCase_    (	self						)		->							Tuple:





									'''simple docstring'''
									A_       	=  self.get_tokenizer()
									A_       	=  self.get_rust_tokenizer()
									A_       	=  self.get_image_processor()

									A_       	=  FlavaProcessor(tokenizer=a__       ,       image_processor=a__						)
									processor_slow.save_pretrained(self.tmpdirname						)
									A_       	=  FlavaProcessor.from_pretrained(self.tmpdirname       ,       use_fast=a__						)

									A_       	=  FlavaProcessor(tokenizer=a__       ,       image_processor=a__						)
									processor_fast.save_pretrained(self.tmpdirname						)
									A_       	=  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        lowerCAmelCase_    (	self						)		->							str:





									'''simple docstring'''
									A_       	=  FlavaProcessor(tokenizer=self.get_tokenizer()       ,       image_processor=self.get_image_processor()						)
									processor.save_pretrained(self.tmpdirname						)

									A_       	=  self.get_tokenizer(bos_token='''(BOS)'''       ,       eos_token='''(EOS)'''						)
									A_       	=  self.get_image_processor(do_normalize=a__       ,       padding_value=1.0						)

									A_       	=  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        lowerCAmelCase_    (	self						)		->							List[str]:





									'''simple docstring'''
									A_       	=  self.get_image_processor()
									A_       	=  self.get_tokenizer()

									A_       	=  FlavaProcessor(tokenizer=a__       ,       image_processor=a__						)

									A_       	=  self.prepare_image_inputs()

									A_       	=  image_processor(a__       ,       return_tensors='''np'''						)
									A_       	=  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(1_2_3_4						)
									A_       	=  image_processor(
									    a__       ,       return_image_mask=a__       ,       return_codebook_pixels=a__       ,       return_tensors='''np'''						)
									random.seed(1_2_3_4						)
									A_       	=  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        lowerCAmelCase_    (	self						)		->							Union[str, Any]:





									'''simple docstring'''
									A_       	=  self.get_image_processor()
									A_       	=  self.get_tokenizer()

									A_       	=  FlavaProcessor(tokenizer=a__       ,       image_processor=a__						)

									A_       	=  '''lower newer'''

									A_       	=  processor(text=a__						)

									A_       	=  tokenizer(a__						)

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


					def        lowerCAmelCase_    (	self						)		->							str:





									'''simple docstring'''
									A_       	=  self.get_image_processor()
									A_       	=  self.get_tokenizer()

									A_       	=  FlavaProcessor(tokenizer=a__       ,       image_processor=a__						)

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

									A_       	=  processor(text=a__       ,       images=a__						)

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

									# add extra args
									A_       	=  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        lowerCAmelCase_    (	self						)		->							Optional[Any]:





									'''simple docstring'''
									A_       	=  self.get_image_processor()
									A_       	=  self.get_tokenizer()

									A_       	=  FlavaProcessor(tokenizer=a__       ,       image_processor=a__						)

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

									A_       	=  processor.batch_decode(a__						)
									A_       	=  tokenizer.batch_decode(a__						)

									self.assertListEqual(a__       ,       a__						)


					def        lowerCAmelCase_    (	self						)		->							Union[str, Any]:





									'''simple docstring'''
									A_       	=  self.get_image_processor()
									A_       	=  self.get_tokenizer()

									A_       	=  FlavaProcessor(tokenizer=a__       ,       image_processor=a__						)

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

									A_       	=  processor(text=a__       ,       images=a__						)

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

from cva import destroyAllWindows, imread, imshow, waitKey



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				A_	,					A_       	=  img.shape[0], img.shape[1]

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

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

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

				return img


if __name__ == "__main__":
						# read original image
						SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       {
						    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()