






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 					A__     :

            '''simple docstring'''






            @staticmethod
            def   _SCREAMING_SNAKE_CASE     ( *_SCREAMING_SNAKE_CASE      :    int   ,						**_SCREAMING_SNAKE_CASE      :    str ):






                  """simple docstring"""




                  pass

       def 		lowercase__  (				_UpperCamelCase) ->			Optional[int]:
             """simple docstring"""

             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.
__magic_name__   :  List[Any]        =							(
    '''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'''
)





@is_pipeline_test
@require_torch
@require_vision
class 					A__     (     unittest.TestCase ):

     '''simple docstring'''






     snake_case__					=					MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING







     @require_pytesseract
     @require_vision
     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    Any   ,						_SCREAMING_SNAKE_CASE      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  pipeline(
               'document-question-answering'   ,						model=_SCREAMING_SNAKE_CASE   ,						tokenizer=_SCREAMING_SNAKE_CASE   ,						image_processor=_SCREAMING_SNAKE_CASE )

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







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    str   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any] ):






           """simple docstring"""




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







     @require_torch
     @require_detectrona
     @require_pytesseract
     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int] ):






           """simple docstring"""




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

           UpperCamelCase					       =  [
               {'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},
           ]
           UpperCamelCase					       =  dqa_pipeline(image=_SCREAMING_SNAKE_CASE   ,						question=_SCREAMING_SNAKE_CASE   ,						top_k=2 )
           self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE   ,						decimals=4 )   ,						_SCREAMING_SNAKE_CASE )

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

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

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







     @slow
     @require_torch
     @require_detectrona
     @require_pytesseract
     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




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

           UpperCamelCase					       =  dqa_pipeline(image=_SCREAMING_SNAKE_CASE   ,						question=_SCREAMING_SNAKE_CASE   ,						top_k=2 )
           self.assertEqual(
               nested_simplify(_SCREAMING_SNAKE_CASE   ,						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},
               ]   ,						)

           UpperCamelCase					       =  dqa_pipeline({'image': image, 'question': question}   ,						top_k=2 )
           self.assertEqual(
               nested_simplify(_SCREAMING_SNAKE_CASE   ,						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},
               ]   ,						)

           UpperCamelCase					       =  dqa_pipeline(
               [{'image': image, 'question': question}, {'image': image, 'question': question}]   ,						top_k=2 )
           self.assertEqual(
               nested_simplify(_SCREAMING_SNAKE_CASE   ,						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   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




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

           UpperCamelCase					       =  dqa_pipeline(image=_SCREAMING_SNAKE_CASE   ,						question=_SCREAMING_SNAKE_CASE   ,						top_k=2 )
           self.assertEqual(
               nested_simplify(_SCREAMING_SNAKE_CASE   ,						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},
               ]   ,						)

           UpperCamelCase					       =  dqa_pipeline({'image': image, 'question': question}   ,						top_k=2 )
           self.assertEqual(
               nested_simplify(_SCREAMING_SNAKE_CASE   ,						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},
               ]   ,						)

           UpperCamelCase					       =  dqa_pipeline(
               [{'image': image, 'question': question}, {'image': image, 'question': question}]   ,						top_k=2 )
           self.assertEqual(
               nested_simplify(_SCREAMING_SNAKE_CASE   ,						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   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




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

           UpperCamelCase					       =  dqa_pipeline(image=_SCREAMING_SNAKE_CASE   ,						question=_SCREAMING_SNAKE_CASE   ,						top_k=2 )
           self.assertEqual(
               nested_simplify(_SCREAMING_SNAKE_CASE   ,						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},
               ]   ,						)

           UpperCamelCase					       =  dqa_pipeline({'image': image, 'question': question}   ,						top_k=2 )
           self.assertEqual(
               nested_simplify(_SCREAMING_SNAKE_CASE   ,						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},
               ]   ,						)

           UpperCamelCase					       =  dqa_pipeline(
               [{'image': image, 'question': question}, {'image': image, 'question': question}]   ,						top_k=2 )
           self.assertEqual(
               nested_simplify(_SCREAMING_SNAKE_CASE   ,						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   ,						)

           UpperCamelCase					       =  list(zip(*apply_tesseract(load_image(_SCREAMING_SNAKE_CASE )   ,						_SCREAMING_SNAKE_CASE   ,						'' ) ) )

           # This model should also work if `image` is set to None
           UpperCamelCase					       =  dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question}   ,						top_k=2 )
           self.assertEqual(
               nested_simplify(_SCREAMING_SNAKE_CASE   ,						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   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




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

           UpperCamelCase					       =  dqa_pipeline(image=_SCREAMING_SNAKE_CASE   ,						question=_SCREAMING_SNAKE_CASE   ,						top_k=2 )
           self.assertEqual(
               nested_simplify(_SCREAMING_SNAKE_CASE   ,						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},
               ]   ,						)

           UpperCamelCase					       =  dqa_pipeline(
               [{'image': image, 'question': question}, {'image': image, 'question': question}]   ,						top_k=2 )
           self.assertEqual(
               nested_simplify(_SCREAMING_SNAKE_CASE   ,						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   ,						)

           UpperCamelCase					       =  list(zip(*apply_tesseract(load_image(_SCREAMING_SNAKE_CASE )   ,						_SCREAMING_SNAKE_CASE   ,						'' ) ) )

           # This model should also work if `image` is set to None
           UpperCamelCase					       =  dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question}   ,						top_k=2 )
           self.assertEqual(
               nested_simplify(_SCREAMING_SNAKE_CASE   ,						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   _SCREAMING_SNAKE_CASE     ( self      :    List[str] ):






           """simple docstring"""




           UpperCamelCase					       =  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'   ,						)

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







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






           """simple docstring"""




           pass











from collections.abc import Sequence
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			float:
      """simple docstring"""

      return sum(c * (x**i) for i, c in enumerate(_UpperCamelCase))
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			float:
      """simple docstring"""

      UpperCamelCase					       =  0.0
      for coeff in reversed(_UpperCamelCase):
            UpperCamelCase					       =  result * x + coeff
      return result


if __name__ == "__main__":
       __magic_name__   :  Union[str, Any]        =							(0.0, 0.0, 5.0, 9.3, 7.0)
       __magic_name__   :  List[str]        =							10.0
       print(evaluate_poly(poly, x))
       print(horner(poly, x))



