


'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase     :  Any											=    {
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Dict											=    [
           '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''LukeForEntityClassification''',
           '''LukeForEntityPairClassification''',
           '''LukeForEntitySpanClassification''',
           '''LukeForMultipleChoice''',
           '''LukeForQuestionAnswering''',
           '''LukeForSequenceClassification''',
           '''LukeForTokenClassification''',
           '''LukeForMaskedLM''',
           '''LukeModel''',
           '''LukePreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
       from .tokenization_luke import LukeTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_luke import (
                  LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LukeForEntityClassification,
                  LukeForEntityPairClassification,
                  LukeForEntitySpanClassification,
                  LukeForMaskedLM,
                  LukeForMultipleChoice,
                  LukeForQuestionAnswering,
                  LukeForSequenceClassification,
                  LukeForTokenClassification,
                  LukeModel,
                  LukePreTrainedModel,
              )

else:
       import sys

       lowerCAmelCase     :  List[Any]											=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


'''simple docstring'''

import unittest

from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_detectrona,
    require_pytesseract,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image

 from transformers.image_utils import load_image
else:


 class     __UpperCAmelCase       :





   @staticmethod
   def 						UpperCAmelCase_	(	*_lowerCamelCase    ,     **_lowerCamelCase ):
      pass






 def snake_case_      (					__snake_case  :							int)			->					Union[str, Any]:
    return None


# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
A_     :						Any    							=(
    '''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'''
)


@is_pipeline_test
@require_torch
@require_vision
class     __UpperCAmelCase       ( unittest.TestCase		):
  __A :				Union[str, Any]											= MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING





  @require_pytesseract
  @require_vision
  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      pipeline(
         '''document-question-answering'''    ,     model=_lowerCamelCase    ,     tokenizer=_lowerCamelCase    ,     image_processor=_lowerCamelCase )

     lowerCAmelCase_			=      INVOICE_URL
     lowerCAmelCase_			=      list(zip(*apply_tesseract(load_image(_lowerCamelCase )    ,     _lowerCamelCase    ,     '''''' ) ) )
     lowerCAmelCase_			=      '''What is the placebo?'''
     lowerCAmelCase_			=      [
         {
             '''image''': load_image(_lowerCamelCase ),
             '''question''': question,
         },
         {
             '''image''': image,
             '''question''': question,
         },
         {
             '''image''': image,
             '''question''': question,
             '''word_boxes''': word_boxes,
         },
     ]
     return dqa_pipeline, examples





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      dqa_pipeline(_lowerCamelCase    ,     top_k=2 )
     self.assertEqual(
         _lowerCamelCase    ,     [
             [
                 {'''score''': ANY(_lowerCamelCase ), '''answer''': ANY(_lowerCamelCase ), '''start''': ANY(_lowerCamelCase ), '''end''': ANY(_lowerCamelCase )},
                 {'''score''': ANY(_lowerCamelCase ), '''answer''': ANY(_lowerCamelCase ), '''start''': ANY(_lowerCamelCase ), '''end''': ANY(_lowerCamelCase )},
             ]
         ]
         * 3    ,     )





  @require_torch
  @require_detectrona
  @require_pytesseract
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      pipeline('''document-question-answering'''    ,     model='''hf-internal-testing/tiny-random-layoutlmv2''' )
     lowerCAmelCase_			=      INVOICE_URL
     lowerCAmelCase_			=      '''How many cats are there?'''

     lowerCAmelCase_			=      [
         {'''score''': 0.00_01, '''answer''': '''oy 2312/2019''', '''start''': 38, '''end''': 39},
         {'''score''': 0.00_01, '''answer''': '''oy 2312/2019 DUE''', '''start''': 38, '''end''': 40},
     ]
     lowerCAmelCase_			=      dqa_pipeline(image=_lowerCamelCase    ,     question=_lowerCamelCase    ,     top_k=2 )
     self.assertEqual(nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     _lowerCamelCase )

     lowerCAmelCase_			=      dqa_pipeline({'''image''': image, '''question''': question}    ,     top_k=2 )
     self.assertEqual(nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     _lowerCamelCase )

     # This image does not detect ANY text in it, meaning layoutlmv2 should fail.
     # Empty answer probably
     lowerCAmelCase_			=      '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
     lowerCAmelCase_			=      dqa_pipeline(image=_lowerCamelCase    ,     question=_lowerCamelCase    ,     top_k=2 )
     self.assertEqual(_lowerCamelCase    ,     [] )

     # We can optionnally pass directly the words and bounding boxes
     lowerCAmelCase_			=      '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
     lowerCAmelCase_			=      []
     lowerCAmelCase_			=      []
     lowerCAmelCase_			=      dqa_pipeline(image=_lowerCamelCase    ,     question=_lowerCamelCase    ,     words=_lowerCamelCase    ,     boxes=_lowerCamelCase    ,     top_k=2 )
     self.assertEqual(_lowerCamelCase    ,     [] )





  @slow
  @require_torch
  @require_detectrona
  @require_pytesseract
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      pipeline(
         '''document-question-answering'''    ,     model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'''    ,     revision='''9977165'''    ,     )
     lowerCAmelCase_			=      INVOICE_URL
     lowerCAmelCase_			=      '''What is the invoice number?'''

     lowerCAmelCase_			=      dqa_pipeline(image=_lowerCamelCase    ,     question=_lowerCamelCase    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             {'''score''': 0.99_44, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             {'''score''': 0.00_09, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
         ]    ,     )

     lowerCAmelCase_			=      dqa_pipeline({'''image''': image, '''question''': question}    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             {'''score''': 0.99_44, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             {'''score''': 0.00_09, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
         ]    ,     )

     lowerCAmelCase_			=      dqa_pipeline(
         [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             [
                 {'''score''': 0.99_44, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 {'''score''': 0.00_09, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             ],
         ]
         * 2    ,     )





  @slow
  @require_torch
  @require_detectrona
  @require_pytesseract
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      pipeline(
         '''document-question-answering'''    ,     model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'''    ,     revision='''9977165'''    ,     max_seq_len=50    ,     )
     lowerCAmelCase_			=      INVOICE_URL
     lowerCAmelCase_			=      '''What is the invoice number?'''

     lowerCAmelCase_			=      dqa_pipeline(image=_lowerCamelCase    ,     question=_lowerCamelCase    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             {'''score''': 0.99_74, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
             {'''score''': 0.99_48, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
         ]    ,     )

     lowerCAmelCase_			=      dqa_pipeline({'''image''': image, '''question''': question}    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             {'''score''': 0.99_74, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
             {'''score''': 0.99_48, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
         ]    ,     )

     lowerCAmelCase_			=      dqa_pipeline(
         [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             [
                 {'''score''': 0.99_74, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
                 {'''score''': 0.99_48, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             ]
         ]
         * 2    ,     )





  @slow
  @require_torch
  @require_pytesseract
  @require_vision
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      AutoTokenizer.from_pretrained(
         '''impira/layoutlm-document-qa'''    ,     revision='''3dc6de3'''    ,     add_prefix_space=_lowerCamelCase )
     lowerCAmelCase_			=      pipeline(
         '''document-question-answering'''    ,     model='''impira/layoutlm-document-qa'''    ,     tokenizer=_lowerCamelCase    ,     revision='''3dc6de3'''    ,     )
     lowerCAmelCase_			=      INVOICE_URL
     lowerCAmelCase_			=      '''What is the invoice number?'''

     lowerCAmelCase_			=      dqa_pipeline(image=_lowerCamelCase    ,     question=_lowerCamelCase    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             {'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             {'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
         ]    ,     )

     lowerCAmelCase_			=      dqa_pipeline({'''image''': image, '''question''': question}    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             {'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             {'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
         ]    ,     )

     lowerCAmelCase_			=      dqa_pipeline(
         [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             [
                 {'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 {'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
             ]
         ]
         * 2    ,     )

     lowerCAmelCase_			=      list(zip(*apply_tesseract(load_image(_lowerCamelCase )    ,     _lowerCamelCase    ,     '''''' ) ) )

     # This model should also work if `image` is set to None
     lowerCAmelCase_			=      dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             {'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             {'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
         ]    ,     )





  @slow
  @require_torch
  @require_pytesseract
  @require_vision
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      AutoTokenizer.from_pretrained(
         '''impira/layoutlm-document-qa'''    ,     revision='''3dc6de3'''    ,     add_prefix_space=_lowerCamelCase )
     lowerCAmelCase_			=      pipeline(
         '''document-question-answering'''    ,     model='''impira/layoutlm-document-qa'''    ,     tokenizer=_lowerCamelCase    ,     revision='''3dc6de3'''    ,     max_seq_len=50    ,     )
     lowerCAmelCase_			=      INVOICE_URL
     lowerCAmelCase_			=      '''What is the invoice number?'''

     lowerCAmelCase_			=      dqa_pipeline(image=_lowerCamelCase    ,     question=_lowerCamelCase    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             {'''score''': 0.99_99, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             {'''score''': 0.99_98, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
         ]    ,     )

     lowerCAmelCase_			=      dqa_pipeline(
         [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             [
                 {'''score''': 0.99_99, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 {'''score''': 0.99_98, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             ]
         ]
         * 2    ,     )

     lowerCAmelCase_			=      list(zip(*apply_tesseract(load_image(_lowerCamelCase )    ,     _lowerCamelCase    ,     '''''' ) ) )

     # This model should also work if `image` is set to None
     lowerCAmelCase_			=      dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             {'''score''': 0.99_99, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             {'''score''': 0.99_98, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
         ]    ,     )





  @slow
  @require_torch
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      pipeline(
         '''document-question-answering'''    ,     model='''naver-clova-ix/donut-base-finetuned-docvqa'''    ,     tokenizer=AutoTokenizer.from_pretrained('''naver-clova-ix/donut-base-finetuned-docvqa''' )    ,     feature_extractor='''naver-clova-ix/donut-base-finetuned-docvqa'''    ,     )

     lowerCAmelCase_			=      INVOICE_URL
     lowerCAmelCase_			=      '''What is the invoice number?'''
     lowerCAmelCase_			=      dqa_pipeline(image=_lowerCamelCase    ,     question=_lowerCamelCase    ,     top_k=2 )
     self.assertEqual(nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [{'''answer''': '''us-001'''}] )







  @require_tf
  @unittest.skip('''Document question answering not implemented in TF''' )
  def 						UpperCAmelCase_	(	self ):
     pass





