






import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor


@require_tokenizers
@require_vision
class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     def 				_A       (				self       :	str      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    tempfile.mkdtemp()

       # fmt: off
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
       # fmt: on
       SCREAMING_SNAKE_CASE       :      Any							=    os.path.join(self.tmpdirname					, VOCAB_FILES_NAMES["vocab_file"]      )
       with open(self.vocab_file					, "w"					, encoding="utf-8"      ) as vocab_writer:
         vocab_writer.write("".join([x + "\n" for x in vocab_tokens]      )      )

       SCREAMING_SNAKE_CASE       :      List[Any]							=    {
           'do_resize': True,
           'size': {'height': 18, 'width': 18},
           'do_normalize': True,
           'image_mean': [0.5, 0.5, 0.5],
           'image_std': [0.5, 0.5, 0.5],
       }
       SCREAMING_SNAKE_CASE       :      Any							=    os.path.join(self.tmpdirname					, UpperCamelCase_      )
       with open(self.image_processor_file					, "w"					, encoding="utf-8"      ) as fp:
         json.dump(UpperCamelCase_					, UpperCamelCase_      )


     def 				_A       (				self       :	Any					, **UpperCAmelCase_       :	List[str]      ):
       return BertTokenizer.from_pretrained(self.tmpdirname					, **UpperCamelCase_      )


     def 				_A       (				self       :	Optional[Any]					, **UpperCAmelCase_       :	Dict      ):
       return ViTImageProcessor.from_pretrained(self.tmpdirname					, **UpperCamelCase_      )


     def 				_A       (				self       :	Union[str, Any]      ):
       shutil.rmtree(self.tmpdirname      )


     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    [np.random.randint(255					, size=(3, 30, 400)					, dtype=np.uinta      )]

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    [Image.fromarray(np.moveaxis(UpperCamelCase_					, 0					, -1      )      ) for x in image_inputs]

       return image_inputs


     def 				_A       (				self       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.get_tokenizer()
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.get_image_processor()

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_					, image_processor=UpperCamelCase_      )

       processor.save_pretrained(self.tmpdirname      )
       SCREAMING_SNAKE_CASE       :      str							=    VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname      )

       self.assertEqual(processor.tokenizer.get_vocab()					, tokenizer.get_vocab()      )
       self.assertIsInstance(processor.tokenizer					, (BertTokenizer, BertTokenizerFast)      )

       self.assertEqual(processor.image_processor.to_json_string()					, image_processor.to_json_string()      )
       self.assertIsInstance(processor.image_processor					, UpperCamelCase_      )


     def 				_A       (				self       :	List[str]      ):
       SCREAMING_SNAKE_CASE       :      str							=    VisionTextDualEncoderProcessor(
           tokenizer=self.get_tokenizer()					, image_processor=self.get_image_processor()      )
       processor.save_pretrained(self.tmpdirname      )

       SCREAMING_SNAKE_CASE       :      List[str]							=    self.get_tokenizer(bos_token="(BOS)"					, eos_token="(EOS)"      )
       SCREAMING_SNAKE_CASE       :      Tuple							=    self.get_image_processor(do_normalize=UpperCamelCase_					, padding_value=1.0      )

       SCREAMING_SNAKE_CASE       :      int							=    VisionTextDualEncoderProcessor.from_pretrained(
           self.tmpdirname					, bos_token="(BOS)"					, eos_token="(EOS)"					, do_normalize=UpperCamelCase_					, padding_value=1.0      )

       self.assertEqual(processor.tokenizer.get_vocab()					, tokenizer_add_kwargs.get_vocab()      )
       self.assertIsInstance(processor.tokenizer					, (BertTokenizer, BertTokenizerFast)      )

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


     def 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.get_image_processor()
       SCREAMING_SNAKE_CASE       :      Dict							=    self.get_tokenizer()

       SCREAMING_SNAKE_CASE       :      List[str]							=    VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_					, image_processor=UpperCamelCase_      )

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.prepare_image_inputs()

       SCREAMING_SNAKE_CASE       :      Any							=    image_processor(UpperCamelCase_					, return_tensors="np"      )
       SCREAMING_SNAKE_CASE       :      str							=    processor(images=UpperCamelCase_					, 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       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    self.get_image_processor()
       SCREAMING_SNAKE_CASE       :      Dict							=    self.get_tokenizer()

       SCREAMING_SNAKE_CASE       :      Dict							=    VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_					, image_processor=UpperCamelCase_      )

       SCREAMING_SNAKE_CASE       :      int							=    'lower newer'

       SCREAMING_SNAKE_CASE       :      Tuple							=    processor(text=UpperCamelCase_      )

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    tokenizer(UpperCamelCase_      )

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


     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    self.get_image_processor()
       SCREAMING_SNAKE_CASE       :      str							=    self.get_tokenizer()

       SCREAMING_SNAKE_CASE       :      List[Any]							=    VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_					, image_processor=UpperCamelCase_      )

       SCREAMING_SNAKE_CASE       :      List[Any]							=    'lower newer'
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.prepare_image_inputs()

       SCREAMING_SNAKE_CASE       :      Dict							=    processor(text=UpperCamelCase_					, images=UpperCamelCase_      )

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

       # test if it raises when no input is passed
       with self.assertRaises(UpperCamelCase_      ):
         processor()


     def 				_A       (				self       :	str      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    self.get_image_processor()
       SCREAMING_SNAKE_CASE       :      str							=    self.get_tokenizer()

       SCREAMING_SNAKE_CASE       :      int							=    VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_					, image_processor=UpperCamelCase_      )

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

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    processor.batch_decode(UpperCamelCase_      )
       SCREAMING_SNAKE_CASE       :      List[str]							=    tokenizer.batch_decode(UpperCamelCase_      )

       self.assertListEqual(UpperCamelCase_					, UpperCamelCase_      )






     def 				_A       (				self       :	List[str]      ):
       SCREAMING_SNAKE_CASE       :      str							=    self.get_image_processor()
       SCREAMING_SNAKE_CASE       :      Tuple							=    self.get_tokenizer()

       SCREAMING_SNAKE_CASE       :      str							=    VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_					, image_processor=UpperCamelCase_      )

       SCREAMING_SNAKE_CASE       :      Any							=    'lower newer'
       SCREAMING_SNAKE_CASE       :      str							=    self.prepare_image_inputs()

       SCREAMING_SNAKE_CASE       :      Dict							=    processor(text=UpperCamelCase_					, images=UpperCamelCase_      )

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














import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import DonutImageProcessor


class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     def __init__(				self       :	int					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Tuple=7					, UpperCAmelCase_       :	Dict=3					, UpperCAmelCase_       :	Any=18					, UpperCAmelCase_       :	Optional[int]=30					, UpperCAmelCase_       :	Any=400					, UpperCAmelCase_       :	Optional[int]=True					, UpperCAmelCase_       :	Union[str, Any]=None					, UpperCAmelCase_       :	int=True					, UpperCAmelCase_       :	Optional[int]=False					, UpperCAmelCase_       :	List[Any]=True					, UpperCAmelCase_       :	Optional[Any]=True					, UpperCAmelCase_       :	Any=[0.5, 0.5, 0.5]					, UpperCAmelCase_       :	Dict=[0.5, 0.5, 0.5]					, ):
       SCREAMING_SNAKE_CASE       :      Any							=    parent
       SCREAMING_SNAKE_CASE       :      int							=    batch_size
       SCREAMING_SNAKE_CASE       :      Dict							=    num_channels
       SCREAMING_SNAKE_CASE       :      str							=    image_size
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    min_resolution
       SCREAMING_SNAKE_CASE       :      Any							=    max_resolution
       SCREAMING_SNAKE_CASE       :      int							=    do_resize
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    size if size is not None else {"height": 18, "width": 20}
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    do_thumbnail
       SCREAMING_SNAKE_CASE       :      Tuple							=    do_align_axis
       SCREAMING_SNAKE_CASE       :      List[str]							=    do_pad
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    do_normalize
       SCREAMING_SNAKE_CASE       :      Any							=    image_mean
       SCREAMING_SNAKE_CASE       :      Any							=    image_std






     def 				_A       (				self       :	str      ):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_thumbnail": self.do_thumbnail,
           "do_align_long_axis": self.do_align_axis,
           "do_pad": self.do_pad,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }





@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		str			=	DonutImageProcessor if is_vision_available() else None


     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    DonutImageProcessingTester(self      )


     @property
     def 				_A       (				self       :	Tuple      ):
       return self.image_processor_tester.prepare_image_processor_dict()


     def 				_A       (				self       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.image_processing_class(**self.image_processor_dict      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_resize"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "size"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_thumbnail"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_align_long_axis"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_pad"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_normalize"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "image_mean"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "image_std"      )      )


     def 				_A       (				self       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      Any							=    self.image_processing_class.from_dict(self.image_processor_dict      )
       self.assertEqual(image_processor.size					, {"height": 18, "width": 20}      )

       SCREAMING_SNAKE_CASE       :      List[Any]							=    self.image_processing_class.from_dict(self.image_processor_dict					, size=42      )
       self.assertEqual(image_processor.size					, {"height": 42, "width": 42}      )

       # Previous config had dimensions in (width, height) order
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.image_processing_class.from_dict(self.image_processor_dict					, size=(42, 84)      )
       self.assertEqual(image_processor.size					, {"height": 84, "width": 42}      )


     def 				_A       (				self       :	Dict      ):
       pass


     @is_flaky()
     def 				_A       (				self       :	Optional[Any]      ):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.image_processing_class(**self.image_processor_dict      )
       # create random PIL images
       SCREAMING_SNAKE_CASE       :      Tuple							=    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase_      )
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_					, Image.Image      )

       # Test not batched input
       SCREAMING_SNAKE_CASE       :      List[str]							=    image_processing(image_inputs[0]					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )

       # Test batched
       SCREAMING_SNAKE_CASE       :      Dict							=    image_processing(UpperCAmelCase_					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )


     @is_flaky()
     def 				_A       (				self       :	List[str]      ):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE       :      Dict							=    self.image_processing_class(**self.image_processor_dict      )
       # create random numpy tensors
       SCREAMING_SNAKE_CASE       :      List[Any]							=    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase_					, numpify=UpperCAmelCase_      )
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_					, np.ndarray      )

       # Test not batched input
       SCREAMING_SNAKE_CASE       :      Any							=    image_processing(image_inputs[0]					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )

       # Test batched
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    image_processing(UpperCAmelCase_					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )






     @is_flaky()
     def 				_A       (				self       :	Dict      ):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.image_processing_class(**self.image_processor_dict      )
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE       :      Tuple							=    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase_					, torchify=UpperCAmelCase_      )
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_					, torch.Tensor      )

       # Test not batched input
       SCREAMING_SNAKE_CASE       :      int							=    image_processing(image_inputs[0]					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )

       # Test batched
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    image_processing(UpperCAmelCase_					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )






