





'''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 	SCREAMING_SNAKE_CASE	:




          @staticmethod
          def 					a_					(					*A__      : Dict	,    **A__      : Optional[Any]					):


               """simple docstring"""







               pass

   def     __lowercase  (_lowercase     )   ->     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.
UpperCAmelCase__						:Union[str, Any]          =			(
    """https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"""
)







@is_pipeline_test
@require_torch
@require_vision
class 	SCREAMING_SNAKE_CASE	(      unittest.TestCase  ):
       snake_case__    :     List[Any]   		=			MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING




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


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				pipeline(
                """document-question-answering"""	,    model=A__	,    tokenizer=A__	,    image_processor=A__					)

            __lowerCamelCase						:       Optional[int]				   =				INVOICE_URL
            __lowerCamelCase						:       Optional[Any]				   =				list(zip(*apply_tesseract(load_image(A__					)	,    A__	,    """"""					)					)					)
            __lowerCamelCase						:       List[str]				   =				"""What is the placebo?"""
            __lowerCamelCase						:       List[str]				   =				[
                {
                    """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__      : List[str]	,    A__      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				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      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				pipeline("""document-question-answering"""	,    model="""hf-internal-testing/tiny-random-layoutlmv2"""					)
            __lowerCamelCase						:       Union[str, Any]				   =				INVOICE_URL
            __lowerCamelCase						:       Optional[Any]				   =				"""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						:       List[str]				   =				dqa_pipeline(image=A__	,    question=A__	,    top_k=2					)
            self.assertEqual(nested_simplify(A__	,    decimals=4					)	,    A__					)

            __lowerCamelCase						:       int				   =				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
            __lowerCamelCase						:       List[str]				   =				"""./tests/fixtures/tests_samples/COCO/000000039769.png"""
            __lowerCamelCase						:       int				   =				dqa_pipeline(image=A__	,    question=A__	,    top_k=2					)
            self.assertEqual(A__	,    []					)

            # We can optionnally pass directly the words and bounding boxes
            __lowerCamelCase						:       int				   =				"""./tests/fixtures/tests_samples/COCO/000000039769.png"""
            __lowerCamelCase						:       Optional[Any]				   =				[]
            __lowerCamelCase						:       List[str]				   =				[]
            __lowerCamelCase						:       int				   =				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      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				pipeline(
                """document-question-answering"""	,    model="""tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"""	,    revision="""9977165"""	,    )
            __lowerCamelCase						:       int				   =				INVOICE_URL
            __lowerCamelCase						:       Union[str, Any]				   =				"""What is the invoice number?"""

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

            __lowerCamelCase						:       List[Any]				   =				dqa_pipeline({"""image""": image, """question""": question}	,    top_k=2					)
            self.assertEqual(
                nested_simplify(A__	,    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(A__	,    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 					a_					(					self      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				pipeline(
                """document-question-answering"""	,    model="""tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"""	,    revision="""9977165"""	,    max_seq_len=50	,    )
            __lowerCamelCase						:       Union[str, Any]				   =				INVOICE_URL
            __lowerCamelCase						:       List[str]				   =				"""What is the invoice number?"""

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

            __lowerCamelCase						:       str				   =				dqa_pipeline({"""image""": image, """question""": question}	,    top_k=2					)
            self.assertEqual(
                nested_simplify(A__	,    decimals=4					)	,    [
                    {"""score""": 0.9974, """answer""": """1110212019""", """start""": 23, """end""": 23},
                    {"""score""": 0.9948, """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(A__	,    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 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				AutoTokenizer.from_pretrained(
                """impira/layoutlm-document-qa"""	,    revision="""3dc6de3"""	,    add_prefix_space=A__					)
            __lowerCamelCase						:       int				   =				pipeline(
                """document-question-answering"""	,    model="""impira/layoutlm-document-qa"""	,    tokenizer=A__	,    revision="""3dc6de3"""	,    )
            __lowerCamelCase						:       Optional[Any]				   =				INVOICE_URL
            __lowerCamelCase						:       Union[str, Any]				   =				"""What is the invoice number?"""

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

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

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

            __lowerCamelCase						:       List[Any]				   =				list(zip(*apply_tesseract(load_image(A__					)	,    A__	,    """"""					)					)					)

            # This model should also work if `image` is set to None
            __lowerCamelCase						:       Dict				   =				dqa_pipeline({"""image""": None, """word_boxes""": word_boxes, """question""": question}	,    top_k=2					)
            self.assertEqual(
                nested_simplify(A__	,    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 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				AutoTokenizer.from_pretrained(
                """impira/layoutlm-document-qa"""	,    revision="""3dc6de3"""	,    add_prefix_space=A__					)
            __lowerCamelCase						:       Tuple				   =				pipeline(
                """document-question-answering"""	,    model="""impira/layoutlm-document-qa"""	,    tokenizer=A__	,    revision="""3dc6de3"""	,    max_seq_len=50	,    )
            __lowerCamelCase						:       Optional[int]				   =				INVOICE_URL
            __lowerCamelCase						:       str				   =				"""What is the invoice number?"""

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

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

            __lowerCamelCase						:       Dict				   =				list(zip(*apply_tesseract(load_image(A__					)	,    A__	,    """"""					)					)					)

            # 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(A__	,    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 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				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						:       str				   =				INVOICE_URL
            __lowerCamelCase						:       str				   =				"""What is the invoice number?"""
            __lowerCamelCase						:       List[Any]				   =				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      : Any					):


            """simple docstring"""







            pass











'''simple docstring'''

import argparse
import json

from tqdm import tqdm
def     __lowercase  ()   ->     Tuple:


     """simple docstring"""
     __lowerCamelCase						:       Union[str, Any]				   =				argparse.ArgumentParser()

     # Required parameters
     parser.add_argument(
         """--src_path""",						type=_lowercase,						default="""biencoder-nq-dev.json""",						help="""Path to raw DPR training data""",						)
     parser.add_argument(
         """--evaluation_set""",						type=_lowercase,						help="""where to store parsed evaluation_set file""",						)
     parser.add_argument(
         """--gold_data_path""",						type=_lowercase,						help="""where to store parsed gold_data_path file""",						)
     __lowerCamelCase						:       Tuple				   =				parser.parse_args()

     with open(args.src_path,						"""r"""     ) as src_file, open(args.evaluation_set,						"""w"""     ) as eval_file, open(
         args.gold_data_path,						"""w"""     ) as gold_file:
          __lowerCamelCase						:       int				   =				json.load(_lowercase     )
          for dpr_record in tqdm(_lowercase     ):
               __lowerCamelCase						:       List[Any]				   =				dpr_record["""question"""]
               __lowerCamelCase						:       List[str]				   =				[context["""title"""] for context in dpr_record["""positive_ctxs"""]]
               eval_file.write(question + """\n"""     )
               gold_file.write("""\t""".join(_lowercase     ) + """\n"""     )


if __name__ == "__main__":
   main()



