






'''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     a_						(      unittest.TestCase    ):

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=7      ,		_SCREAMING_SNAKE_CASE=3      ,		_SCREAMING_SNAKE_CASE=18      ,		_SCREAMING_SNAKE_CASE=30      ,		_SCREAMING_SNAKE_CASE=400      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=True      ,		)  ->				Dict:



        """simple docstring"""






        UpperCamelCase      = size if size is not None else {"""height""": 18, """width""": 18}
        UpperCamelCase      = parent
        UpperCamelCase      = batch_size
        UpperCamelCase      = num_channels
        UpperCamelCase      = image_size
        UpperCamelCase      = min_resolution
        UpperCamelCase      = max_resolution
        UpperCamelCase      = do_resize
        UpperCamelCase      = size
        UpperCamelCase      = apply_ocr


  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}






@require_torch
@require_pytesseract
class     a_						(      lowerCamelCase     ,	unittest.TestCase    ):
  lowercase							=				LayoutLMvaImageProcessor if is_pytesseract_available() else None

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        UpperCamelCase      = LayoutLMvaImageProcessingTester(self				)

  @property
  def   A__						(				self				)  ->				Any:



        """simple docstring"""






        return self.image_processor_tester.prepare_image_processor_dict()

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



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""do_resize"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""size"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""apply_ocr"""				)				)

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class.from_dict(self.image_processor_dict				)
        self.assertEqual(image_processor.size      ,		{"""height""": 18, """width""": 18}				)

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

  def   A__						(				self				)  ->				str:



        """simple docstring"""






        pass

  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
        # create random PIL images
        UpperCamelCase      = prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_SCREAMING_SNAKE_CASE				)
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,		Image.Image				)

        # Test not batched input
        UpperCamelCase      = 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      ,		_SCREAMING_SNAKE_CASE				)
        self.assertIsInstance(encoding.boxes      ,		_SCREAMING_SNAKE_CASE				)

        # Test batched
        UpperCamelCase      = image_processing(_SCREAMING_SNAKE_CASE      ,		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   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
        # create random numpy tensors
        UpperCamelCase      = prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_SCREAMING_SNAKE_CASE      ,		numpify=_SCREAMING_SNAKE_CASE				)
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,		np.ndarray				)

        # Test not batched input
        UpperCamelCase      = 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      = image_processing(_SCREAMING_SNAKE_CASE      ,		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   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
        # create random PyTorch tensors
        UpperCamelCase      = prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_SCREAMING_SNAKE_CASE      ,		torchify=_SCREAMING_SNAKE_CASE				)
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,		torch.Tensor				)

        # Test not batched input
        UpperCamelCase      = 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      = image_processing(_SCREAMING_SNAKE_CASE      ,		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   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = LayoutLMvaImageProcessor()

        from datasets import load_dataset

        UpperCamelCase      = load_dataset("""hf-internal-testing/fixtures_docvqa"""      ,		split="""test"""				)

        UpperCamelCase      = Image.open(ds[0]["""file"""]				).convert("""RGB"""				)

        UpperCamelCase      = image_processing(_SCREAMING_SNAKE_CASE      ,		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      = [["""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      = [[[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      ,		_SCREAMING_SNAKE_CASE				)
        self.assertListEqual(encoding.boxes      ,		_SCREAMING_SNAKE_CASE				)

        # with apply_OCR = False
        UpperCamelCase      = LayoutLMvaImageProcessor(apply_ocr=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = image_processing(_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				)

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








'''simple docstring'''

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     =       {'vocab_file': 'spm_char.model'}

SCREAMING_SNAKE_CASE__     =       {
    'vocab_file': {
        'microsoft/speecht5_asr': 'https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model',
        'microsoft/speecht5_tts': 'https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model',
        'microsoft/speecht5_vc': 'https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model',
    }
}

SCREAMING_SNAKE_CASE__     =       {
    'microsoft/speecht5_asr': 1_0_2_4,
    'microsoft/speecht5_tts': 1_0_2_4,
    'microsoft/speecht5_vc': 1_0_2_4,
}






class     a_						(      lowerCamelCase    ):
  lowercase							=				VOCAB_FILES_NAMES
  lowercase							=				PRETRAINED_VOCAB_FILES_MAP
  lowercase							=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowercase							=				["""input_ids""", """attention_mask"""]

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE="<s>"      ,		_SCREAMING_SNAKE_CASE="</s>"      ,		_SCREAMING_SNAKE_CASE="<unk>"      ,		_SCREAMING_SNAKE_CASE="<pad>"      ,		_SCREAMING_SNAKE_CASE = None      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				None:



        """simple docstring"""






        UpperCamelCase      = {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=_SCREAMING_SNAKE_CASE      ,		eos_token=_SCREAMING_SNAKE_CASE      ,		unk_token=_SCREAMING_SNAKE_CASE      ,		pad_token=_SCREAMING_SNAKE_CASE      ,		sp_model_kwargs=self.sp_model_kwargs      ,		**_SCREAMING_SNAKE_CASE      ,		)

        UpperCamelCase      = vocab_file

        UpperCamelCase      = spm.SentencePieceProcessor(**self.sp_model_kwargs				)
        self.sp_model.Load(_SCREAMING_SNAKE_CASE				)

  @property
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        return self.sp_model.get_piece_size()

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        UpperCamelCase      = {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE				): i for i in range(self.vocab_size				)}
        vocab.update(self.added_tokens_encoder				)
        return vocab

  def __getstate__(				self				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = self.__dict__.copy()
        UpperCamelCase      = None
        return state

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



        """simple docstring"""






        UpperCamelCase      = d

        # for backward compatibility
        if not hasattr(self      ,		"""sp_model_kwargs"""				):
              UpperCamelCase      = {}

        UpperCamelCase      = spm.SentencePieceProcessor(**self.sp_model_kwargs				)
        self.sp_model.Load(self.vocab_file				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				List[str]:



        """simple docstring"""






        return self.sp_model.encode(_SCREAMING_SNAKE_CASE      ,		out_type=_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Tuple:



        """simple docstring"""






        return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE				)
        return token

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				List[str]:



        """simple docstring"""






        UpperCamelCase      = []
        UpperCamelCase      = """"""
        for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                    out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE				) + token
                    UpperCamelCase      = []
              else:
                    current_sub_tokens.append(_SCREAMING_SNAKE_CASE				)
        out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE				)
        return out_string.strip()

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=None				)  ->				List[int]:



        """simple docstring"""






        if token_ids_a is None:
              return token_ids_a + [self.eos_token_id]
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return token_ids_a + token_ids_a + [self.eos_token_id]

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = False				)  ->				List[int]:



        """simple docstring"""






        if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=_SCREAMING_SNAKE_CASE      ,		token_ids_a=_SCREAMING_SNAKE_CASE      ,		already_has_special_tokens=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = [1]
        if token_ids_a is None:
              return ([0] * len(_SCREAMING_SNAKE_CASE				)) + suffix_ones
        return ([0] * len(_SCREAMING_SNAKE_CASE				)) + ([0] * len(_SCREAMING_SNAKE_CASE				)) + suffix_ones


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None				)  ->				Tuple[str]:



        """simple docstring"""






        if not os.path.isdir(_SCREAMING_SNAKE_CASE				):
              logger.error(F"Vocabulary path ({save_directory}) should be a directory"				)
              return
        UpperCamelCase      = os.path.join(
            _SCREAMING_SNAKE_CASE      ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

        if os.path.abspath(self.vocab_file				) != os.path.abspath(_SCREAMING_SNAKE_CASE				) and os.path.isfile(self.vocab_file				):
              copyfile(self.vocab_file      ,		_SCREAMING_SNAKE_CASE				)
        elif not os.path.isfile(self.vocab_file				):
              with open(_SCREAMING_SNAKE_CASE      ,		"""wb"""				) as fi:
                    UpperCamelCase      = self.sp_model.serialized_model_proto()
                    fi.write(_SCREAMING_SNAKE_CASE				)

        return (out_vocab_file,)
