

'''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						):





  def 		A						(	self   :			int				)			->					str:





       '''simple docstring'''






       _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': 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,
       }

       _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 		A						(	self   :			str							,      **_a   :			List[Any]				)			->					Dict:





       '''simple docstring'''






       return BertTokenizer.from_pretrained(self.tmpdirname							,      **_a				)





  def 		A						(	self   :			Dict							,      **_a   :			Dict				)			->					Tuple:





       '''simple docstring'''






       return BertTokenizerFast.from_pretrained(self.tmpdirname							,      **_a				)





  def 		A						(	self   :			Union[str, Any]							,      **_a   :			Any				)			->					Dict:





       '''simple docstring'''






       return FlavaImageProcessor.from_pretrained(self.tmpdirname							,      **_a				)





  def 		A						(	self   :			Optional[Any]				)			->					Optional[Any]:





       '''simple docstring'''






       shutil.rmtree(self.tmpdirname				)





  def 		A						(	self   :			str				)			->					Dict:





       '''simple docstring'''






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

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

       return image_inputs





  def 		A						(	self   :			Any				)			->					Tuple:





       '''simple docstring'''






       _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 		A						(	self   :			str				)			->					List[Any]:





       '''simple docstring'''






       _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 		A						(	self   :			List[Any]				)			->					str:





       '''simple docstring'''






       _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(1234				)
       _SCREAMING_SNAKE_CASE												=image_processor(
           _a							,      return_image_mask=_a							,      return_codebook_pixels=_a							,      return_tensors='np'				)
       random.seed(1234				)
       _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 		A						(	self   :			Union[str, Any]				)			->					Any:





       '''simple docstring'''






       _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 		A						(	self   :			List[Any]				)			->					Union[str, Any]:





       '''simple docstring'''






       _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 		A						(	self   :			Optional[int]				)			->					str:





       '''simple docstring'''






       _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 		A						(	self   :			Tuple				)			->					Any:





       '''simple docstring'''






       _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 typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


lowerCamelCase				:   Dict      				=  datasets.utils.logging.get_logger(__name__)







class 							A__					( folder_based_builder.FolderBasedBuilderConfig						):
  A__					=       None
  A__					=       None







class 							A__					( folder_based_builder.FolderBasedBuilder						):
  A__					=       datasets.Image()
  A__					=       'image'
  A__					=       ImageFolderConfig
  A__					=       42  # definition at the bottom of the script
  A__					=       ImageClassification(image_column='image'  ,     label_column='label'						)

lowerCamelCase				:   Optional[int]      				=  [
    ".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",
]
lowerCamelCase				:   Any      				=  IMAGE_EXTENSIONS
