






'''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						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase=7			,     __lowerCAmelCase=3			,     __lowerCAmelCase=18			,     __lowerCAmelCase=30			,     __lowerCAmelCase=4_00			,     __lowerCAmelCase=True			,     __lowerCAmelCase=None			,     __lowerCAmelCase=True			,     ):
       UpperCamelCase_				:   Dict					    =							size if size is not None else {"""height""": 18, """width""": 18}
       UpperCamelCase_				:   List[str]					    =							parent
       UpperCamelCase_				:   List[str]					    =							batch_size
       UpperCamelCase_				:   List[Any]					    =							num_channels
       UpperCamelCase_				:   List[str]					    =							image_size
       UpperCamelCase_				:   Tuple					    =							min_resolution
       UpperCamelCase_				:   List[str]					    =							max_resolution
       UpperCamelCase_				:   Optional[int]					    =							do_resize
       UpperCamelCase_				:   List[Any]					    =							size
       UpperCamelCase_				:   Any					    =							apply_ocr





 def   _UpperCAmelCase       (							self ):
       return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}






@require_torch
@require_pytesseract
class     A    (      SCREAMING_SNAKE_CASE__,   unittest.TestCase						):






 """simple docstring"""





 __a			:						Optional[int]      =						LayoutLMvaImageProcessor if is_pytesseract_available() else None
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   int					    =							LayoutLMvaImageProcessingTester(self )
 @property
 def   _UpperCAmelCase       (							self ):
       return self.image_processor_tester.prepare_image_processor_dict()
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(__lowerCAmelCase			,     """do_resize""" ) )
       self.assertTrue(hasattr(__lowerCAmelCase			,     """size""" ) )
       self.assertTrue(hasattr(__lowerCAmelCase			,     """apply_ocr""" ) )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Union[str, Any]					    =							self.image_processing_class.from_dict(self.image_processor_dict )
       self.assertEqual(image_processor.size			,     {"""height""": 18, """width""": 18} )

       UpperCamelCase_				:   Union[str, Any]					    =							self.image_processing_class.from_dict(self.image_processor_dict			,     size=42 )
       self.assertEqual(image_processor.size			,     {"""height""": 42, """width""": 42} )
 def   _UpperCAmelCase       (							self ):
       pass
 def   _UpperCAmelCase       (							self ):
       # Initialize image_processing
       UpperCamelCase_				:   str					    =							self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       UpperCamelCase_				:   List[str]					    =							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=__lowerCAmelCase )
       for image in image_inputs:
             self.assertIsInstance(__lowerCAmelCase			,     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			,     __lowerCAmelCase )
       self.assertIsInstance(encoding.boxes			,     __lowerCAmelCase )

       # Test batched
       UpperCamelCase_				:   List[str]					    =							image_processing(__lowerCAmelCase			,     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   _UpperCAmelCase       (							self ):
       # Initialize image_processing
       UpperCamelCase_				:   int					    =							self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       UpperCamelCase_				:   Optional[Any]					    =							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=__lowerCAmelCase			,     numpify=__lowerCAmelCase )
       for image in image_inputs:
             self.assertIsInstance(__lowerCAmelCase			,     np.ndarray )

       # Test not batched input
       UpperCamelCase_				:   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
       UpperCamelCase_				:   List[str]					    =							image_processing(__lowerCAmelCase			,     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   _UpperCAmelCase       (							self ):
       # Initialize image_processing
       UpperCamelCase_				:   str					    =							self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       UpperCamelCase_				:   List[Any]					    =							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=__lowerCAmelCase			,     torchify=__lowerCAmelCase )
       for image in image_inputs:
             self.assertIsInstance(__lowerCAmelCase			,     torch.Tensor )

       # Test not batched input
       UpperCamelCase_				:   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
       UpperCamelCase_				:   Union[str, Any]					    =							image_processing(__lowerCAmelCase			,     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   _UpperCAmelCase       (							self ):
       # with apply_OCR = True
       UpperCamelCase_				:   Optional[int]					    =							LayoutLMvaImageProcessor()

       from datasets import load_dataset

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

       UpperCamelCase_				:   int					    =							Image.open(ds[0]["""file"""] ).convert("""RGB""" )

       UpperCamelCase_				:   Union[str, Any]					    =							image_processing(__lowerCAmelCase			,     return_tensors="""pt""" )

       self.assertEqual(encoding.pixel_values.shape			,     (1, 3, 2_24, 2_24) )
       self.assertEqual(len(encoding.words )			,     len(encoding.boxes ) )

       # fmt: off
       # the words and boxes were obtained with Tesseract 4.1.1
       UpperCamelCase_				:   Optional[Any]					    =							[["""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_				:   List[Any]					    =							[[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]]  # noqa: E231
       # fmt: on

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

       # with apply_OCR = False
       UpperCamelCase_				:   Optional[int]					    =							LayoutLMvaImageProcessor(apply_ocr=__lowerCAmelCase )

       UpperCamelCase_				:   Optional[int]					    =							image_processing(__lowerCAmelCase			,     return_tensors="""pt""" )

       self.assertEqual(encoding.pixel_values.shape			,     (1, 3, 2_24, 2_24) )














'''simple docstring'''

from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse("3.8"):
      import importlib_metadata
else:
      import importlib.metadata as importlib_metadata


UpperCamelCase							=""


if version.parse(importlib_metadata.version("jiwer")) < version.parse("2.3.0"):




      class     A    (      tr.AbstractTransform						):






       """simple docstring"""





       def __init__(							self			,     __lowerCAmelCase = " " ):
             UpperCamelCase_				:   str					    =							sentence_delimiter
       def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
             return list(__lowerCAmelCase )





       def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
             UpperCamelCase_				:   List[Any]					    =							[]
             for sent_idx, sentence in enumerate(__lowerCAmelCase ):
                   chars.extend(self.process_string(__lowerCAmelCase ) )
                   if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__lowerCAmelCase ) - 1:
                         chars.append(self.sentence_delimiter )
             return chars






      UpperCamelCase							=tr.Compose(
          [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
      )
else:
      UpperCamelCase							=tr.Compose(
          [
              tr.RemoveMultipleSpaces(),
              tr.Strip(),
              tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
              tr.ReduceToListOfListOfChars(),
          ]
      )


UpperCamelCase							="\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

UpperCamelCase							="\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n"

UpperCamelCase							="\nComputes CER score of transcribed segments against references.\nArgs:\n    references: list of references for each speech input.\n    predictions: list of transcribtions to score.\n    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n    (float): the character error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> cer = datasets.load_metric(\"cer\")\n    >>> cer_score = cer.compute(predictions=predictions, references=references)\n    >>> print(cer_score)\n    0.34146341463414637\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,   _KWARGS_DESCRIPTION						)
class     A    (      datasets.Metric						):






 """simple docstring"""





 def   _UpperCAmelCase       (							self ):
       return datasets.MetricInfo(
           description=_DESCRIPTION			,     citation=_CITATION			,     inputs_description=_KWARGS_DESCRIPTION			,     features=datasets.Features(
               {
                   """predictions""": datasets.Value("""string"""			,     id="""sequence""" ),
                   """references""": datasets.Value("""string"""			,     id="""sequence""" ),
               } )			,     codebase_urls=["""https://github.com/jitsi/jiwer/"""]			,     reference_urls=[
               """https://en.wikipedia.org/wiki/Word_error_rate""",
               """https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates""",
           ]			,     )





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase=False ):
       if concatenate_texts:
             return jiwer.compute_measures(
                 __lowerCAmelCase			,     __lowerCAmelCase			,     truth_transform=__lowerCAmelCase			,     hypothesis_transform=__lowerCAmelCase			,     )["wer"]

       UpperCamelCase_				:   Optional[Any]					    =							0
       UpperCamelCase_				:   str					    =							0
       for prediction, reference in zip(__lowerCAmelCase			,     __lowerCAmelCase ):
             UpperCamelCase_				:   Optional[int]					    =							jiwer.compute_measures(
                 __lowerCAmelCase			,     __lowerCAmelCase			,     truth_transform=__lowerCAmelCase			,     hypothesis_transform=__lowerCAmelCase			,     )
             incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
             total += measures["substitutions"] + measures["deletions"] + measures["hits"]

       return incorrect / total






