






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    :	List[Any]					)  ->       Union[str, Any]:
           __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": 224,
               "do_center_crop": True,
               "crop_size": 224,
               "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": 112,
               "codebook_do_center_crop": True,
               "codebook_crop_size": 112,
               "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				,				lowercase__					)
           with open(self.image_processor_file				,				"w"				,				encoding="utf-8"					) as fp:
                 json.dump(lowercase__				,				lowercase__					)






     def     _UpperCamelCase   (self    :	List[str]				,				**lowercase__    :	Optional[Any]					)  ->       List[Any]:
           return BertTokenizer.from_pretrained(self.tmpdirname				,				**lowercase__					)






     def     _UpperCamelCase   (self    :	Union[str, Any]				,				**lowercase__    :	int					)  ->       Union[str, Any]:
           return BertTokenizerFast.from_pretrained(self.tmpdirname				,				**lowercase__					)






     def     _UpperCamelCase   (self    :	str				,				**lowercase__    :	Any					)  ->       Dict:
           return FlavaImageProcessor.from_pretrained(self.tmpdirname				,				**lowercase__					)






     def     _UpperCamelCase   (self    :	List[str]					)  ->       Optional[int]:
           shutil.rmtree(self.tmpdirname					)






     def     _UpperCamelCase   (self    :	Dict					)  ->       Tuple:
           __A		   =      [np.random.randint(255				,				size=(3, 30, 400)				,				dtype=np.uinta					)]

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

           return image_inputs






     def     _UpperCamelCase   (self    :	int					)  ->       Optional[Any]:
           __A		   =      self.get_tokenizer()
           __A		   =      self.get_rust_tokenizer()
           __A		   =      self.get_image_processor()

           __A		   =      FlavaProcessor(tokenizer=lowercase__				,				image_processor=lowercase__					)
           processor_slow.save_pretrained(self.tmpdirname					)
           __A		   =      FlavaProcessor.from_pretrained(self.tmpdirname				,				use_fast=lowercase__					)

           __A		   =      FlavaProcessor(tokenizer=lowercase__				,				image_processor=lowercase__					)
           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				,				lowercase__					)
           self.assertIsInstance(processor_fast.tokenizer				,				lowercase__					)

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






     def     _UpperCamelCase   (self    :	Dict					)  ->       Optional[Any]:
           __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=lowercase__				,				padding_value=1.0					)

           __A		   =      FlavaProcessor.from_pretrained(
               self.tmpdirname				,				bos_token="(BOS)"				,				eos_token="(EOS)"				,				do_normalize=lowercase__				,				padding_value=1.0					)

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

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






     def     _UpperCamelCase   (self    :	Any					)  ->       Dict:
           __A		   =      self.get_image_processor()
           __A		   =      self.get_tokenizer()

           __A		   =      FlavaProcessor(tokenizer=lowercase__				,				image_processor=lowercase__					)

           __A		   =      self.prepare_image_inputs()

           __A		   =      image_processor(lowercase__				,				return_tensors="np"					)
           __A		   =      processor(images=lowercase__				,				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(1234					)
           __A		   =      image_processor(
               lowercase__				,				return_image_mask=lowercase__				,				return_codebook_pixels=lowercase__				,				return_tensors="np"					)
           random.seed(1234					)
           __A		   =      processor(
               images=lowercase__				,				return_image_mask=lowercase__				,				return_codebook_pixels=lowercase__				,				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    :	Union[str, Any]					)  ->       List[str]:
           __A		   =      self.get_image_processor()
           __A		   =      self.get_tokenizer()

           __A		   =      FlavaProcessor(tokenizer=lowercase__				,				image_processor=lowercase__					)

           __A		   =      "lower newer"

           __A		   =      processor(text=lowercase__					)

           __A		   =      tokenizer(lowercase__					)

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






     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       int:
           __A		   =      self.get_image_processor()
           __A		   =      self.get_tokenizer()

           __A		   =      FlavaProcessor(tokenizer=lowercase__				,				image_processor=lowercase__					)

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

           __A		   =      processor(text=lowercase__				,				images=lowercase__					)

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

           # add extra args
           __A		   =      processor(text=lowercase__				,				images=lowercase__				,				return_codebook_pixels=lowercase__				,				return_image_mask=lowercase__					)

           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(lowercase__					):
                 processor()






     def     _UpperCamelCase   (self    :	int					)  ->       Optional[int]:
           __A		   =      self.get_image_processor()
           __A		   =      self.get_tokenizer()

           __A		   =      FlavaProcessor(tokenizer=lowercase__				,				image_processor=lowercase__					)

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

           __A		   =      processor.batch_decode(lowercase__					)
           __A		   =      tokenizer.batch_decode(lowercase__					)

           self.assertListEqual(lowercase__				,				lowercase__					)






     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       Tuple:
           __A		   =      self.get_image_processor()
           __A		   =      self.get_tokenizer()

           __A		   =      FlavaProcessor(tokenizer=lowercase__				,				image_processor=lowercase__					)

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

           __A		   =      processor(text=lowercase__				,				images=lowercase__					)

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










'''simple docstring'''







from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


__A      :       Tuple      =	datasets.utils.logging.get_logger(__name__)






class 					a__    (			folder_based_builder.FolderBasedBuilderConfig						):
     '''simple docstring'''







     _snake_case             =  None
     _snake_case             =  None







class 					a__    (			folder_based_builder.FolderBasedBuilder						):
     '''simple docstring'''







     _snake_case             =  datasets.Image()
     _snake_case             =  'image'
     _snake_case             =  ImageFolderConfig
     _snake_case             =  42  # definition at the bottom of the script
     _snake_case             =  ImageClassification(image_column='image' ,						label_column='label'						)







__A      :       Any      =	[
    '.blp',
    '.bmp',
    '.dib',
    '.bufr',
    '.cur',
    '.pcx',
    '.dcx',
    '.dds',
    '.ps',
    '.eps',
    '.fit',
    '.fits',
    '.fli',
    '.flc',
    '.ftc',
    '.ftu',
    '.gbr',
    '.gif',
    '.grib',
    '.h5',
    '.hdf',
    '.png',
    '.apng',
    '.jp2',
    '.j2k',
    '.jpc',
    '.jpf',
    '.jpx',
    '.j2c',
    '.icns',
    '.ico',
    '.im',
    '.iim',
    '.tif',
    '.tiff',
    '.jfif',
    '.jpe',
    '.jpg',
    '.jpeg',
    '.mpg',
    '.mpeg',
    '.msp',
    '.pcd',
    '.pxr',
    '.pbm',
    '.pgm',
    '.ppm',
    '.pnm',
    '.psd',
    '.bw',
    '.rgb',
    '.rgba',
    '.sgi',
    '.ras',
    '.tga',
    '.icb',
    '.vda',
    '.vst',
    '.webp',
    '.wmf',
    '.emf',
    '.xbm',
    '.xpm',
]
__A      :       List[Any]      =	IMAGE_EXTENSIONS


