





from math import factorial

snake_case_				=			{str(d): factorial(d) for d in range(10)}






def lowerCamelCase__    (							snake_case_	:  int       )      ->				int:
   return sum(DIGIT_FACTORIAL[d] for d in str(snake_case_       )       )






def lowerCamelCase__    (							)      ->				int:
   __snake_case							       =   7 * factorial(9       ) + 1
   return sum(i for i in range(3       ,      snake_case_       ) if sum_of_digit_factorial(snake_case_       ) == i       )


if __name__ == "__main__":
    print(F'{solution() = }')








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 		SCREAMING_SNAKE_CASE__				:


          @staticmethod
          def    a (*a__       :      List[str]				,			**a__       :      List[str]      ):





             """simple docstring"""



             pass






    def lowerCamelCase__    (							snake_case_	:  int       )      ->				Optional[int]:
       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.
snake_case_				=			(
    'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)

@is_pipeline_test
@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):
      A_						:				Optional[Any]							=    MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING


      @require_pytesseract
      @require_vision
      def    a (self       :      List[Any]				,			a__       :      Tuple				,			a__       :      Union[str, Any]				,			a__       :      Any      ):





         """simple docstring"""



         __snake_case							       =   pipeline(
             '''document-question-answering'''				,			model=a__				,			tokenizer=a__				,			image_processor=a__      )

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


      def    a (self       :      Union[str, Any]				,			a__       :      Optional[int]				,			a__       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   dqa_pipeline(a__				,			top_k=2      )
         self.assertEqual(
             a__				,			[
                 [
                     {'''score''': ANY(a__      ), '''answer''': ANY(a__      ), '''start''': ANY(a__      ), '''end''': ANY(a__      )},
                     {'''score''': ANY(a__      ), '''answer''': ANY(a__      ), '''start''': ANY(a__      ), '''end''': ANY(a__      )},
                 ]
             ]
             * 3				,			)


      @require_torch
      @require_detectrona
      @require_pytesseract
      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   pipeline('''document-question-answering'''				,			model='''hf-internal-testing/tiny-random-layoutlmv2'''      )
         __snake_case							       =   INVOICE_URL
         __snake_case							       =   '''How many cats are there?'''

         __snake_case							       =   [
             {'''score''': 0.0_0_0_1, '''answer''': '''oy 2312/2019''', '''start''': 38, '''end''': 39},
             {'''score''': 0.0_0_0_1, '''answer''': '''oy 2312/2019 DUE''', '''start''': 38, '''end''': 40},
         ]
         __snake_case							       =   dqa_pipeline(image=a__				,			question=a__				,			top_k=2      )
         self.assertEqual(nested_simplify(a__				,			decimals=4      )				,			a__      )

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

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

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


      @slow
      @require_torch
      @require_detectrona
      @require_pytesseract
      def    a (self       :      str      ):





         """simple docstring"""



         __snake_case							       =   pipeline(
             '''document-question-answering'''				,			model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'''				,			revision='''9977165'''				,			)
         __snake_case							       =   INVOICE_URL
         __snake_case							       =   '''What is the invoice number?'''

         __snake_case							       =   dqa_pipeline(image=a__				,			question=a__				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 {'''score''': 0.9_9_4_4, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 {'''score''': 0.0_0_0_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             ]				,			)

         __snake_case							       =   dqa_pipeline({'''image''': image, '''question''': question}				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 {'''score''': 0.9_9_4_4, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 {'''score''': 0.0_0_0_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             ]				,			)

         __snake_case							       =   dqa_pipeline(
             [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 [
                     {'''score''': 0.9_9_4_4, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                     {'''score''': 0.0_0_0_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 ],
             ]
             * 2				,			)


      @slow
      @require_torch
      @require_detectrona
      @require_pytesseract
      def    a (self       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   pipeline(
             '''document-question-answering'''				,			model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'''				,			revision='''9977165'''				,			max_seq_len=50				,			)
         __snake_case							       =   INVOICE_URL
         __snake_case							       =   '''What is the invoice number?'''

         __snake_case							       =   dqa_pipeline(image=a__				,			question=a__				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 {'''score''': 0.9_9_7_4, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
                 {'''score''': 0.9_9_4_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             ]				,			)

         __snake_case							       =   dqa_pipeline({'''image''': image, '''question''': question}				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 {'''score''': 0.9_9_7_4, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
                 {'''score''': 0.9_9_4_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             ]				,			)

         __snake_case							       =   dqa_pipeline(
             [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 [
                     {'''score''': 0.9_9_7_4, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
                     {'''score''': 0.9_9_4_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 ]
             ]
             * 2				,			)


      @slow
      @require_torch
      @require_pytesseract
      @require_vision
      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   AutoTokenizer.from_pretrained(
             '''impira/layoutlm-document-qa'''				,			revision='''3dc6de3'''				,			add_prefix_space=a__      )
         __snake_case							       =   pipeline(
             '''document-question-answering'''				,			model='''impira/layoutlm-document-qa'''				,			tokenizer=a__				,			revision='''3dc6de3'''				,			)
         __snake_case							       =   INVOICE_URL
         __snake_case							       =   '''What is the invoice number?'''

         __snake_case							       =   dqa_pipeline(image=a__				,			question=a__				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 {'''score''': 0.4_2_5_1, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 {'''score''': 0.0_8_1_9, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
             ]				,			)

         __snake_case							       =   dqa_pipeline({'''image''': image, '''question''': question}				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 {'''score''': 0.4_2_5_1, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 {'''score''': 0.0_8_1_9, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
             ]				,			)

         __snake_case							       =   dqa_pipeline(
             [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 [
                     {'''score''': 0.4_2_5_1, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                     {'''score''': 0.0_8_1_9, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
                 ]
             ]
             * 2				,			)

         __snake_case							       =   list(zip(*apply_tesseract(load_image(a__      )				,			a__				,			''''''      )      )      )

         # This model should also work if `image` is set to None
         __snake_case							       =   dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 {'''score''': 0.4_2_5_1, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 {'''score''': 0.0_8_1_9, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
             ]				,			)


      @slow
      @require_torch
      @require_pytesseract
      @require_vision
      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   AutoTokenizer.from_pretrained(
             '''impira/layoutlm-document-qa'''				,			revision='''3dc6de3'''				,			add_prefix_space=a__      )
         __snake_case							       =   pipeline(
             '''document-question-answering'''				,			model='''impira/layoutlm-document-qa'''				,			tokenizer=a__				,			revision='''3dc6de3'''				,			max_seq_len=50				,			)
         __snake_case							       =   INVOICE_URL
         __snake_case							       =   '''What is the invoice number?'''

         __snake_case							       =   dqa_pipeline(image=a__				,			question=a__				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 {'''score''': 0.9_9_9_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 {'''score''': 0.9_9_9_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             ]				,			)

         __snake_case							       =   dqa_pipeline(
             [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 [
                     {'''score''': 0.9_9_9_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                     {'''score''': 0.9_9_9_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 ]
             ]
             * 2				,			)

         __snake_case							       =   list(zip(*apply_tesseract(load_image(a__      )				,			a__				,			''''''      )      )      )

         # This model should also work if `image` is set to None
         __snake_case							       =   dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 {'''score''': 0.9_9_9_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 {'''score''': 0.9_9_9_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             ]				,			)


      @slow
      @require_torch
      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   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'''				,			)

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


      @require_tf
      @unittest.skip('''Document question answering not implemented in TF'''      )
      def    a (self       :      List[str]      ):





         """simple docstring"""



         pass

