




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__      :

           '''simple docstring'''



           @staticmethod
           def        _lowercase	(       *UpperCamelCase__	,    **UpperCamelCase__	)	-> Tuple:
                  pass




       def 	A ( _SCREAMING_SNAKE_CASE       )      ->					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.
SCREAMING_SNAKE_CASE__			: List[Any]    =  (
    '''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'''
)




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

    '''simple docstring'''



    lowerCamelCase_ :      Union[str, Any]      	= MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING







    @require_pytesseract
    @require_vision
    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	)	-> Tuple:
           lowerCamelCase  : Dict     					=   pipeline(
               "document-question-answering"	,    model=_UpperCAmelCase	,    tokenizer=_UpperCAmelCase	,    image_processor=_UpperCAmelCase	)

           lowerCamelCase  : Optional[int]     					=   INVOICE_URL
           lowerCamelCase  : Any     					=   list(zip(*apply_tesseract(load_image(_UpperCAmelCase	)	,    _UpperCAmelCase	,    ""	)	)	)
           lowerCamelCase  : Tuple     					=   'What is the placebo?'
           lowerCamelCase  : List[Any]     					=   [
               {
                   'image': load_image(_UpperCAmelCase	),
                   'question': question,
               },
               {
                   'image': image,
                   'question': question,
               },
               {
                   'image': image,
                   'question': question,
                   'word_boxes': word_boxes,
               },
           ]
           return dqa_pipeline, examples







    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__	)	-> str:
           lowerCamelCase  : str     					=   dqa_pipeline(_UpperCAmelCase	,    top_k=2	)
           self.assertEqual(
               _UpperCAmelCase	,    [
                   [
                       {"score": ANY(_UpperCAmelCase	), "answer": ANY(_UpperCAmelCase	), "start": ANY(_UpperCAmelCase	), "end": ANY(_UpperCAmelCase	)},
                       {"score": ANY(_UpperCAmelCase	), "answer": ANY(_UpperCAmelCase	), "start": ANY(_UpperCAmelCase	), "end": ANY(_UpperCAmelCase	)},
                   ]
               ]
               * 3	,    )







    @require_torch
    @require_detectrona
    @require_pytesseract
    def        _lowercase	(       self	)	-> int:
           lowerCamelCase  : List[str]     					=   pipeline("document-question-answering"	,    model="hf-internal-testing/tiny-random-layoutlmv2"	)
           lowerCamelCase  : Any     					=   INVOICE_URL
           lowerCamelCase  : List[str]     					=   'How many cats are there?'

           lowerCamelCase  : Union[str, Any]     					=   [
               {'score': 0.0001, 'answer': 'oy 2312/2019', 'start': 38, 'end': 39},
               {'score': 0.0001, 'answer': 'oy 2312/2019 DUE', 'start': 38, 'end': 40},
           ]
           lowerCamelCase  : Optional[Any]     					=   dqa_pipeline(image=_UpperCAmelCase	,    question=_UpperCAmelCase	,    top_k=2	)
           self.assertEqual(nested_simplify(_UpperCAmelCase	,    decimals=4	)	,    _UpperCAmelCase	)

           lowerCamelCase  : Union[str, Any]     					=   dqa_pipeline({"image": image, "question": question}	,    top_k=2	)
           self.assertEqual(nested_simplify(_UpperCAmelCase	,    decimals=4	)	,    _UpperCAmelCase	)

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

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







    @slow
    @require_torch
    @require_detectrona
    @require_pytesseract
    def        _lowercase	(       self	)	-> str:
           lowerCamelCase  : List[str]     					=   pipeline(
               "document-question-answering"	,    model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"	,    revision="9977165"	,    )
           lowerCamelCase  : Optional[Any]     					=   INVOICE_URL
           lowerCamelCase  : int     					=   'What is the invoice number?'

           lowerCamelCase  : Tuple     					=   dqa_pipeline(image=_UpperCAmelCase	,    question=_UpperCAmelCase	,    top_k=2	)
           self.assertEqual(
               nested_simplify(_UpperCAmelCase	,    decimals=4	)	,    [
                   {"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
                   {"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
               ]	,    )

           lowerCamelCase  : Union[str, Any]     					=   dqa_pipeline({"image": image, "question": question}	,    top_k=2	)
           self.assertEqual(
               nested_simplify(_UpperCAmelCase	,    decimals=4	)	,    [
                   {"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
                   {"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
               ]	,    )

           lowerCamelCase  : Optional[Any]     					=   dqa_pipeline(
               [{"image": image, "question": question}, {"image": image, "question": question}]	,    top_k=2	)
           self.assertEqual(
               nested_simplify(_UpperCAmelCase	,    decimals=4	)	,    [
                   [
                       {"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
                       {"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
                   ],
               ]
               * 2	,    )







    @slow
    @require_torch
    @require_detectrona
    @require_pytesseract
    def        _lowercase	(       self	)	-> Tuple:
           lowerCamelCase  : str     					=   pipeline(
               "document-question-answering"	,    model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"	,    revision="9977165"	,    max_seq_len=50	,    )
           lowerCamelCase  : Optional[int]     					=   INVOICE_URL
           lowerCamelCase  : List[str]     					=   'What is the invoice number?'

           lowerCamelCase  : List[str]     					=   dqa_pipeline(image=_UpperCAmelCase	,    question=_UpperCAmelCase	,    top_k=2	)
           self.assertEqual(
               nested_simplify(_UpperCAmelCase	,    decimals=4	)	,    [
                   {"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
                   {"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
               ]	,    )

           lowerCamelCase  : Tuple     					=   dqa_pipeline({"image": image, "question": question}	,    top_k=2	)
           self.assertEqual(
               nested_simplify(_UpperCAmelCase	,    decimals=4	)	,    [
                   {"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
                   {"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
               ]	,    )

           lowerCamelCase  : List[str]     					=   dqa_pipeline(
               [{"image": image, "question": question}, {"image": image, "question": question}]	,    top_k=2	)
           self.assertEqual(
               nested_simplify(_UpperCAmelCase	,    decimals=4	)	,    [
                   [
                       {"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
                       {"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
                   ]
               ]
               * 2	,    )







    @slow
    @require_torch
    @require_pytesseract
    @require_vision
    def        _lowercase	(       self	)	-> Tuple:
           lowerCamelCase  : Tuple     					=   AutoTokenizer.from_pretrained(
               "impira/layoutlm-document-qa"	,    revision="3dc6de3"	,    add_prefix_space=_UpperCAmelCase	)
           lowerCamelCase  : List[Any]     					=   pipeline(
               "document-question-answering"	,    model="impira/layoutlm-document-qa"	,    tokenizer=_UpperCAmelCase	,    revision="3dc6de3"	,    )
           lowerCamelCase  : Tuple     					=   INVOICE_URL
           lowerCamelCase  : List[Any]     					=   'What is the invoice number?'

           lowerCamelCase  : Dict     					=   dqa_pipeline(image=_UpperCAmelCase	,    question=_UpperCAmelCase	,    top_k=2	)
           self.assertEqual(
               nested_simplify(_UpperCAmelCase	,    decimals=4	)	,    [
                   {"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
                   {"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
               ]	,    )

           lowerCamelCase  : List[Any]     					=   dqa_pipeline({"image": image, "question": question}	,    top_k=2	)
           self.assertEqual(
               nested_simplify(_UpperCAmelCase	,    decimals=4	)	,    [
                   {"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
                   {"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
               ]	,    )

           lowerCamelCase  : Optional[int]     					=   dqa_pipeline(
               [{"image": image, "question": question}, {"image": image, "question": question}]	,    top_k=2	)
           self.assertEqual(
               nested_simplify(_UpperCAmelCase	,    decimals=4	)	,    [
                   [
                       {"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
                       {"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
                   ]
               ]
               * 2	,    )

           lowerCamelCase  : Tuple     					=   list(zip(*apply_tesseract(load_image(_UpperCAmelCase	)	,    _UpperCAmelCase	,    ""	)	)	)

           # This model should also work if `image` is set to None
           lowerCamelCase  : Union[str, Any]     					=   dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}	,    top_k=2	)
           self.assertEqual(
               nested_simplify(_UpperCAmelCase	,    decimals=4	)	,    [
                   {"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
                   {"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
               ]	,    )







    @slow
    @require_torch
    @require_pytesseract
    @require_vision
    def        _lowercase	(       self	)	-> List[str]:
           lowerCamelCase  : int     					=   AutoTokenizer.from_pretrained(
               "impira/layoutlm-document-qa"	,    revision="3dc6de3"	,    add_prefix_space=_UpperCAmelCase	)
           lowerCamelCase  : Dict     					=   pipeline(
               "document-question-answering"	,    model="impira/layoutlm-document-qa"	,    tokenizer=_UpperCAmelCase	,    revision="3dc6de3"	,    max_seq_len=50	,    )
           lowerCamelCase  : str     					=   INVOICE_URL
           lowerCamelCase  : List[Any]     					=   'What is the invoice number?'

           lowerCamelCase  : Tuple     					=   dqa_pipeline(image=_UpperCAmelCase	,    question=_UpperCAmelCase	,    top_k=2	)
           self.assertEqual(
               nested_simplify(_UpperCAmelCase	,    decimals=4	)	,    [
                   {"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
                   {"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
               ]	,    )

           lowerCamelCase  : Any     					=   dqa_pipeline(
               [{"image": image, "question": question}, {"image": image, "question": question}]	,    top_k=2	)
           self.assertEqual(
               nested_simplify(_UpperCAmelCase	,    decimals=4	)	,    [
                   [
                       {"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
                       {"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
                   ]
               ]
               * 2	,    )

           lowerCamelCase  : Tuple     					=   list(zip(*apply_tesseract(load_image(_UpperCAmelCase	)	,    _UpperCAmelCase	,    ""	)	)	)

           # This model should also work if `image` is set to None
           lowerCamelCase  : Any     					=   dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}	,    top_k=2	)
           self.assertEqual(
               nested_simplify(_UpperCAmelCase	,    decimals=4	)	,    [
                   {"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
                   {"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
               ]	,    )







    @slow
    @require_torch
    def        _lowercase	(       self	)	-> Tuple:
           lowerCamelCase  : Optional[Any]     					=   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  : int     					=   INVOICE_URL
           lowerCamelCase  : Union[str, Any]     					=   'What is the invoice number?'
           lowerCamelCase  : List[Any]     					=   dqa_pipeline(image=_UpperCAmelCase	,    question=_UpperCAmelCase	,    top_k=2	)
           self.assertEqual(nested_simplify(_UpperCAmelCase	,    decimals=4	)	,    [{"answer": "us-001"}]	)







    @require_tf
    @unittest.skip("Document question answering not implemented in TF"	)
    def        _lowercase	(       self	)	-> Optional[int]:
           pass













'''simple docstring'''




def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					str     )	->	int:
     if len(__snake_case     ) != len(__snake_case     ):
          raise ValueError('String lengths must match!'     )

     __A		:					Optional[Any]						=			0

     for chara, chara in zip(__snake_case					,				__snake_case     ):
          if chara != chara:
               count += 1

     return count


if __name__ == "__main__":
  import doctest

  doctest.testmod()