





"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_pytesseract_available():
  from PIL import Image

  from transformers import LayoutLMvaImageProcessor



class      __SCREAMING_SNAKE_CASE    (						unittest.TestCase   ):







  '''simple docstring'''


  def __init__(		self   :       Any					,   __a   :       str					,   __a   :       Dict=7					,   __a   :       Optional[int]=3					,   __a   :       Optional[Any]=18					,   __a   :       Union[str, Any]=30					,   __a   :       Union[str, Any]=400					,   __a   :       Tuple=True					,   __a   :       Union[str, Any]=None					,   __a   :       Any=True					,   )		->				Any:
        _UpperCamelCase      :	Any						=       size if size is not None else {"height": 18, "width": 18}
        _UpperCamelCase      :	List[Any]						=       parent
        _UpperCamelCase      :	str						=       batch_size
        _UpperCamelCase      :	Optional[int]						=       num_channels
        _UpperCamelCase      :	Union[str, Any]						=       image_size
        _UpperCamelCase      :	int						=       min_resolution
        _UpperCamelCase      :	Dict						=       max_resolution
        _UpperCamelCase      :	Union[str, Any]						=       do_resize
        _UpperCamelCase      :	Optional[int]						=       size
        _UpperCamelCase      :	Union[str, Any]						=       apply_ocr


  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				Optional[Any]:
        return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}





@require_torch
@require_pytesseract
class      __SCREAMING_SNAKE_CASE    (						_UpperCAmelCase		,  unittest.TestCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Optional[int]	     =					LayoutLMvaImageProcessor if is_pytesseract_available() else None

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Any:
        _UpperCamelCase      :	Optional[int]						=       LayoutLMvaImageProcessingTester(self	)

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				Dict:
        return self.image_processor_tester.prepare_image_processor_dict()

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				Dict:
        _UpperCamelCase      :	Optional[Any]						=       self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(A_					,   "do_resize"	)	)
        self.assertTrue(hasattr(A_					,   "size"	)	)
        self.assertTrue(hasattr(A_					,   "apply_ocr"	)	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				Union[str, Any]:
        _UpperCamelCase      :	str						=       self.image_processing_class.from_dict(self.image_processor_dict	)
        self.assertEqual(image_processor.size					,   {"height": 18, "width": 18}	)

        _UpperCamelCase      :	int						=       self.image_processing_class.from_dict(self.image_processor_dict					,   size=42	)
        self.assertEqual(image_processor.size					,   {"height": 42, "width": 42}	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				Optional[Any]:
        pass

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				List[str]:
        # Initialize image_processing
        _UpperCamelCase      :	List[Any]						=       self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        _UpperCamelCase      :	Any						=       prepare_image_inputs(self.image_processor_tester					,   equal_resolution=A_	)
        for image in image_inputs:
              self.assertIsInstance(A_					,   Image.Image	)

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

        self.assertIsInstance(encoding.words					,   A_	)
        self.assertIsInstance(encoding.boxes					,   A_	)

        # Test batched
        _UpperCamelCase      :	Tuple						=       image_processing(A_					,   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"],
            )					,   )

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				Tuple:
        # Initialize image_processing
        _UpperCamelCase      :	str						=       self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        _UpperCamelCase      :	Any						=       prepare_image_inputs(self.image_processor_tester					,   equal_resolution=A_					,   numpify=A_	)
        for image in image_inputs:
              self.assertIsInstance(A_					,   np.ndarray	)

        # Test not batched input
        _UpperCamelCase      :	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
        _UpperCamelCase      :	str						=       image_processing(A_					,   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"],
            )					,   )

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				Tuple:
        # Initialize image_processing
        _UpperCamelCase      :	Any						=       self.image_processing_class(**self.image_processor_dict	)
        # create random PyTorch tensors
        _UpperCamelCase      :	Optional[int]						=       prepare_image_inputs(self.image_processor_tester					,   equal_resolution=A_					,   torchify=A_	)
        for image in image_inputs:
              self.assertIsInstance(A_					,   torch.Tensor	)

        # Test not batched input
        _UpperCamelCase      :	Dict						=       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
        _UpperCamelCase      :	Tuple						=       image_processing(A_					,   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"],
            )					,   )


  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				int:
        # with apply_OCR = True
        _UpperCamelCase      :	int						=       LayoutLMvaImageProcessor()

        from datasets import load_dataset

        _UpperCamelCase      :	Optional[Any]						=       load_dataset("hf-internal-testing/fixtures_docvqa"					,   split="test"	)

        _UpperCamelCase      :	Union[str, Any]						=       Image.open(ds[0]["file"]	).convert("RGB"	)

        _UpperCamelCase      :	int						=       image_processing(A_					,   return_tensors="pt"	)

        self.assertEqual(encoding.pixel_values.shape					,   (1, 3, 224, 224)	)
        self.assertEqual(len(encoding.words	)					,   len(encoding.boxes	)	)

        # fmt: off
        # the words and boxes were obtained with Tesseract 4.1.1
        _UpperCamelCase      :	int						=       [["11:14", "to", "11:39", "a.m", "11:39", "to", "11:44", "a.m.", "11:44", "a.m.", "to", "12:25", "p.m.", "12:25", "to", "12:58", "p.m.", "12:58", "to", "4:00", "p.m.", "2:00", "to", "5:00", "p.m.", "Coffee", "Break", "Coffee", "will", "be", "served", "for", "men", "and", "women", "in", "the", "lobby", "adjacent", "to", "exhibit", "area.", "Please", "move", "into", "exhibit", "area.", "(Exhibits", "Open)", "TRRF", "GENERAL", "SESSION", "(PART", "|)", "Presiding:", "Lee", "A.", "Waller", "TRRF", "Vice", "President", "“Introductory", "Remarks”", "Lee", "A.", "Waller,", "TRRF", "Vice", "Presi-", "dent", "Individual", "Interviews", "with", "TRRF", "Public", "Board", "Members", "and", "Sci-", "entific", "Advisory", "Council", "Mem-", "bers", "Conducted", "by", "TRRF", "Treasurer", "Philip", "G.", "Kuehn", "to", "get", "answers", "which", "the", "public", "refrigerated", "warehousing", "industry", "is", "looking", "for.", "Plus", "questions", "from", "the", "floor.", "Dr.", "Emil", "M.", "Mrak,", "University", "of", "Cal-", "ifornia,", "Chairman,", "TRRF", "Board;", "Sam", "R.", "Cecil,", "University", "of", "Georgia", "College", "of", "Agriculture;", "Dr.", "Stanley", "Charm,", "Tufts", "University", "School", "of", "Medicine;", "Dr.", "Robert", "H.", "Cotton,", "ITT", "Continental", "Baking", "Company;", "Dr.", "Owen", "Fennema,", "University", "of", "Wis-", "consin;", "Dr.", "Robert", "E.", "Hardenburg,", "USDA.", "Questions", "and", "Answers", "Exhibits", "Open", "Capt.", "Jack", "Stoney", "Room", "TRRF", "Scientific", "Advisory", "Council", "Meeting", "Ballroom", "Foyer"]]  # noqa: E231
        _UpperCamelCase      :	int						=       [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
        # fmt: on

        self.assertListEqual(encoding.words					,   A_	)
        self.assertListEqual(encoding.boxes					,   A_	)

        # with apply_OCR = False
        _UpperCamelCase      :	Any						=       LayoutLMvaImageProcessor(apply_ocr=A_	)

        _UpperCamelCase      :	Optional[int]						=       image_processing(A_					,   return_tensors="pt"	)

        self.assertEqual(encoding.pixel_values.shape					,   (1, 3, 224, 224)	)







"""simple docstring"""


import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				Optional[Any]:
        _UpperCamelCase      :	Tuple						=       tempfile.mkdtemp()
        _UpperCamelCase      :	str						=       5

        # Realm tok
        _UpperCamelCase      :	Tuple						=       [
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "[PAD]",
            "[MASK]",
            "test",
            "question",
            "this",
            "is",
            "the",
            "first",
            "second",
            "third",
            "fourth",
            "fifth",
            "record",
            "want",
            "##want",
            "##ed",
            "wa",
            "un",
            "runn",
            "##ing",
            ",",
            "low",
            "lowest",
        ]
        _UpperCamelCase      :	Optional[int]						=       os.path.join(self.tmpdirname					,   "realm_tokenizer"	)
        os.makedirs(__a					,   exist_ok=__a	)
        _UpperCamelCase      :	Optional[Any]						=       os.path.join(__a					,   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]	)	)

        _UpperCamelCase      :	Optional[int]						=       os.path.join(self.tmpdirname					,   "realm_block_records"	)
        os.makedirs(__a					,   exist_ok=__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				RealmTokenizer:
        return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname					,   "realm_tokenizer"	)	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				Dict:
        shutil.rmtree(self.tmpdirname	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				List[str]:
        _UpperCamelCase      :	Optional[Any]						=       RealmConfig(num_block_records=self.num_block_records	)
        return config

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				int:
        _UpperCamelCase      :	Any						=       Dataset.from_dict(
            {
                "id": ["0", "1"],
                "question": ["foo", "bar"],
                "answers": [["Foo", "Bar"], ["Bar"]],
            }	)
        return dataset

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				str:
        _UpperCamelCase      :	int						=       np.array(
            [
                b"This is the first record",
                b"This is the second record",
                b"This is the third record",
                b"This is the fourth record",
                b"This is the fifth record",
                b"This is a longer longer longer record",
            ]					,   dtype=__a					,   )
        return block_records

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				Optional[Any]:
        _UpperCamelCase      :	List[str]						=       RealmRetriever(
            block_records=self.get_dummy_block_records()					,   tokenizer=self.get_tokenizer()					,   )
        return retriever

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				Tuple:
        _UpperCamelCase      :	Tuple						=       self.get_config()
        _UpperCamelCase      :	int						=       self.get_dummy_retriever()
        _UpperCamelCase      :	Tuple						=       retriever.tokenizer

        _UpperCamelCase      :	List[str]						=       np.array([0, 3]					,   dtype="long"	)
        _UpperCamelCase      :	Union[str, Any]						=       tokenizer(["Test question"]	).input_ids
        _UpperCamelCase      :	List[str]						=       tokenizer(
            ["the fourth"]					,   add_special_tokens=__a					,   return_token_type_ids=__a					,   return_attention_mask=__a					,   ).input_ids
        _UpperCamelCase      :	str						=       config.reader_seq_len

        _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase      :	List[Any]						=       retriever(
            __a					,   __a					,   answer_ids=__a					,   max_length=__a					,   return_tensors="np"	)

        self.assertEqual(len(__a	)					,   2	)
        self.assertEqual(len(__a	)					,   2	)
        self.assertEqual(len(__a	)					,   2	)
        self.assertEqual(concat_inputs.input_ids.shape					,   (2, 10)	)
        self.assertEqual(concat_inputs.attention_mask.shape					,   (2, 10)	)
        self.assertEqual(concat_inputs.token_type_ids.shape					,   (2, 10)	)
        self.assertEqual(concat_inputs.special_tokens_mask.shape					,   (2, 10)	)
        self.assertEqual(
            tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0]	)					,   ["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "first", "record", "[SEP]"]					,   )
        self.assertEqual(
            tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1]	)					,   ["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "fourth", "record", "[SEP]"]					,   )

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				List[Any]:
        _UpperCamelCase      :	Any						=       self.get_config()
        _UpperCamelCase      :	Dict						=       self.get_dummy_retriever()
        _UpperCamelCase      :	Dict						=       retriever.tokenizer

        _UpperCamelCase      :	List[Any]						=       np.array([0, 3, 5]					,   dtype="long"	)
        _UpperCamelCase      :	Optional[int]						=       tokenizer(["Test question"]	).input_ids
        _UpperCamelCase      :	str						=       tokenizer(
            ["the fourth", "longer longer"]					,   add_special_tokens=__a					,   return_token_type_ids=__a					,   return_attention_mask=__a					,   ).input_ids
        _UpperCamelCase      :	Union[str, Any]						=       config.reader_seq_len

        _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase      :	Optional[Any]						=       retriever(
            __a					,   __a					,   answer_ids=__a					,   max_length=__a					,   return_tensors="np"	)

        self.assertEqual([False, True, True]					,   __a	)
        self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]					,   __a	)
        self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]					,   __a	)


  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				List[str]:
        _UpperCamelCase      :	List[Any]						=       self.get_dummy_retriever()
        retriever.save_pretrained(os.path.join(self.tmpdirname					,   "realm_block_records"	)	)

        # Test local path
        _UpperCamelCase      :	int						=       retriever.from_pretrained(os.path.join(self.tmpdirname					,   "realm_block_records"	)	)
        self.assertEqual(retriever.block_records[0]					,   b"This is the first record"	)

        # Test mocked remote path
        with patch("transformers.models.realm.retrieval_realm.hf_hub_download"	) as mock_hf_hub_download:
              _UpperCamelCase      :	List[Any]						=       os.path.join(
                  os.path.join(self.tmpdirname					,   "realm_block_records"	)					,   _REALM_BLOCK_RECORDS_FILENAME	)
              _UpperCamelCase      :	int						=       RealmRetriever.from_pretrained("google/realm-cc-news-pretrained-openqa"	)

        self.assertEqual(retriever.block_records[0]					,   b"This is the first record"	)
