





"""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 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      def __init__(							self       :      List[Any]						,				__lowercase       :      List[str]						,				__lowercase       :      Union[str, Any]=7						,				__lowercase       :      List[Any]=3						,				__lowercase       :      int=18						,				__lowercase       :      Union[str, Any]=30						,				__lowercase       :      Optional[Any]=400						,				__lowercase       :      Optional[int]=True						,				__lowercase       :      str=None						,				__lowercase       :      Optional[int]=True						,				):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						size if size is not None else {'''height''': 18, '''width''': 18}
        __UpperCAmelCase    :						Dict       =						parent
        __UpperCAmelCase    :						int       =						batch_size
        __UpperCAmelCase    :						Optional[Any]       =						num_channels
        __UpperCAmelCase    :						Union[str, Any]       =						image_size
        __UpperCAmelCase    :						Any       =						min_resolution
        __UpperCAmelCase    :						List[str]       =						max_resolution
        __UpperCAmelCase    :						Optional[Any]       =						do_resize
        __UpperCAmelCase    :						List[Any]       =						size
        __UpperCAmelCase    :						List[Any]       =						apply_ocr







      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




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




@require_torch
@require_pytesseract
class 							snake_case							(			__UpperCAmelCase      ,  unittest.TestCase ):

      '''simple docstring'''



      _A     :       Union[str, Any]         =       LayoutLMvaImageProcessor if is_pytesseract_available() else None

      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						LayoutLMvaImageProcessingTester(self )

      @property
      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        return self.image_processor_tester.prepare_image_processor_dict()

      def       A_					(							self       :      str ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[str]       =						self.image_processing_class(**self.image_processor_dict )
        self.assertTrue(hasattr(__lowercase						,				'''do_resize''' ) )
        self.assertTrue(hasattr(__lowercase						,				'''size''' ) )
        self.assertTrue(hasattr(__lowercase						,				'''apply_ocr''' ) )

      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[Any]       =						self.image_processing_class.from_dict(self.image_processor_dict )
        self.assertEqual(image_processor.size						,				{'''height''': 18, '''width''': 18} )

        __UpperCAmelCase    :						Any       =						self.image_processing_class.from_dict(self.image_processor_dict						,				size=42 )
        self.assertEqual(image_processor.size						,				{'''height''': 42, '''width''': 42} )

      def       A_					(							self       :      int ):


        '''simple docstring'''




        pass

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




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

        # Test not batched input
        __UpperCAmelCase    :						Any       =						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						,				__lowercase )
        self.assertIsInstance(encoding.boxes						,				__lowercase )

        # Test batched
        __UpperCAmelCase    :						Tuple       =						image_processing(__lowercase						,				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       :      Dict ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						self.image_processing_class(**self.image_processor_dict )
        # create random numpy tensors
        __UpperCAmelCase    :						Dict       =						prepare_image_inputs(self.image_processor_tester						,				equal_resolution=__lowercase						,				numpify=__lowercase )
        for image in image_inputs:
          self.assertIsInstance(__lowercase						,				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    :						Optional[Any]       =						image_processing(__lowercase						,				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       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						self.image_processing_class(**self.image_processor_dict )
        # create random PyTorch tensors
        __UpperCAmelCase    :						List[Any]       =						prepare_image_inputs(self.image_processor_tester						,				equal_resolution=__lowercase						,				torchify=__lowercase )
        for image in image_inputs:
          self.assertIsInstance(__lowercase						,				torch.Tensor )

        # Test not batched input
        __UpperCAmelCase    :						Optional[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    :						Dict       =						image_processing(__lowercase						,				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       :      int ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						LayoutLMvaImageProcessor()

        from datasets import load_dataset

        __UpperCAmelCase    :						str       =						load_dataset('''hf-internal-testing/fixtures_docvqa'''						,				split='''test''' )

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

        __UpperCAmelCase    :						List[Any]       =						image_processing(__lowercase						,				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    :						Tuple       =						[['''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    :						Union[str, Any]       =						[[[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						,				__lowercase )
        self.assertListEqual(encoding.boxes						,				__lowercase )

        # with apply_OCR = False
        __UpperCAmelCase    :						str       =						LayoutLMvaImageProcessor(apply_ocr=__lowercase )

        __UpperCAmelCase    :						int       =						image_processing(__lowercase						,				return_tensors='''pt''' )

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






"""simple docstring"""





import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowercase__				:int				   =       'src/transformers'
lowercase__				:List[str]				   =       'docs/source/en/tasks'


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->str:





  """simple docstring"""


  with open(UpperCAmelCase_  ,			'''r'''  ,			encoding='''utf-8'''  ,			newline='''\n'''  ) as f:
    __UpperCAmelCase    :						Union[str, Any]       =						f.readlines()
  # Find the start prompt.
  __UpperCAmelCase    :						Any       =						0
  while not lines[start_index].startswith(UpperCAmelCase_  ):
    start_index += 1
  start_index += 1

  __UpperCAmelCase    :						Optional[Any]       =						start_index
  while not lines[end_index].startswith(UpperCAmelCase_  ):
    end_index += 1
  end_index -= 1

  while len(lines[start_index]  ) <= 1:
    start_index += 1
  while len(lines[end_index]  ) <= 1:
    end_index -= 1
  end_index += 1
  return "".join(lines[start_index:end_index]  ), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
lowercase__				:Any				   =       direct_transformers_import(TRANSFORMERS_PATH)

lowercase__				:List[Any]				   =       {
    'asr.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    'audio_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    'language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    'image_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    'masked_language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    'multiple_choice.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    'object_detection.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    'question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    'semantic_segmentation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    'sequence_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    'summarization.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'token_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    'translation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'video_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    'document_question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    'monocular_depth_estimation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
lowercase__				:Union[str, Any]				   =       {
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Union[str, Any]:





  """simple docstring"""


  __UpperCAmelCase    :						List[str]       =						TASK_GUIDE_TO_MODELS[task_guide]
  __UpperCAmelCase    :						Dict       =						SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(UpperCAmelCase_  ,			set()  )
  __UpperCAmelCase    :						List[Any]       =						{
      code: name
      for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
      if (code in model_maping_names or code in special_model_types)
  }
  return ", ".join([f'''[{name}](../model_doc/{code})''' for code, name in model_names.items()]  ) + "\n"


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_=False  )						->Tuple:





  """simple docstring"""


  __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						Dict       =						_find_text_in_file(
      filename=os.path.join(UpperCAmelCase_  ,			UpperCAmelCase_  )  ,			start_prompt='''<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'''  ,			end_prompt='''<!--End of the generated tip-->'''  ,			)

  __UpperCAmelCase    :						List[str]       =						get_model_list_for_task(UpperCAmelCase_  )

  if current_list != new_list:
    if overwrite:
      with open(os.path.join(UpperCAmelCase_  ,			UpperCAmelCase_  )  ,			'''w'''  ,			encoding='''utf-8'''  ,			newline='''\n'''  ) as f:
        f.writelines(lines[:start_index] + [new_list] + lines[end_index:]  )
    else:
      raise ValueError(
          f'''The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'''
          ''' to fix this.'''  )


if __name__ == "__main__":
  lowercase__				:int				   =       argparse.ArgumentParser()
  parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
  lowercase__				:Optional[Any]				   =       parser.parse_args()

  for task_guide in TASK_GUIDE_TO_MODELS.keys():
    check_model_list_for_task(task_guide, args.fix_and_overwrite)