






"""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        _lowerCamelCase      :


												@staticmethod
												def     snake_case_   (*__a   ,							**__a		) ->   Optional[int]:
														pass



						def       a__					(					_SCREAMING_SNAKE_CASE    ):




								"""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.
lowerCAmelCase__     			=			(
    '''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'''
)





@is_pipeline_test
@require_torch
@require_vision
class        _lowerCamelCase      (       unittest.TestCase ):
						UpperCAmelCase_ =			MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING


						@require_pytesseract
						@require_vision
						def     snake_case_   (self   ,							__a   ,							__a   ,							__a		) ->   List[str]:
								UpperCamelCase   =	pipeline(
								    "document-question-answering"   ,							model=__a   ,							tokenizer=__a   ,							image_processor=__a		)

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


						def     snake_case_   (self   ,							__a   ,							__a		) ->   Optional[int]:
								UpperCamelCase   =	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     snake_case_   (self		) ->   int:
								UpperCamelCase   =	pipeline("document-question-answering"   ,							model="hf-internal-testing/tiny-random-layoutlmv2"		)
								UpperCamelCase   =	INVOICE_URL
								UpperCamelCase   =	"How many cats are there?"

								UpperCamelCase   =	[
								    {"score": 0.0001, "answer": "oy 2312/2019", "start": 38, "end": 39},
								    {"score": 0.0001, "answer": "oy 2312/2019 DUE", "start": 38, "end": 40},
								]
								UpperCamelCase   =	dqa_pipeline(image=__a   ,							question=__a   ,							top_k=2		)
								self.assertEqual(nested_simplify(__a   ,							decimals=4		)   ,							__a		)

								UpperCamelCase   =	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
								UpperCamelCase   =	"./tests/fixtures/tests_samples/COCO/000000039769.png"
								UpperCamelCase   =	dqa_pipeline(image=__a   ,							question=__a   ,							top_k=2		)
								self.assertEqual(__a   ,							[]		)

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


						@slow
						@require_torch
						@require_detectrona
						@require_pytesseract
						def     snake_case_   (self		) ->   Optional[Any]:
								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=__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},
								    ]   ,							)

								UpperCamelCase   =	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},
								    ]   ,							)

								UpperCamelCase   =	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     snake_case_   (self		) ->   List[str]:
								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=__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},
								    ]   ,							)

								UpperCamelCase   =	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},
								    ]   ,							)

								UpperCamelCase   =	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     snake_case_   (self		) ->   Any:
								UpperCamelCase   =	AutoTokenizer.from_pretrained(
								    "impira/layoutlm-document-qa"   ,							revision="3dc6de3"   ,							add_prefix_space=__a		)
								UpperCamelCase   =	pipeline(
								    "document-question-answering"   ,							model="impira/layoutlm-document-qa"   ,							tokenizer=__a   ,							revision="3dc6de3"   ,							)
								UpperCamelCase   =	INVOICE_URL
								UpperCamelCase   =	"What is the invoice number?"

								UpperCamelCase   =	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},
								    ]   ,							)

								UpperCamelCase   =	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},
								    ]   ,							)

								UpperCamelCase   =	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   ,							)

								UpperCamelCase   =	list(zip(*apply_tesseract(load_image(__a		)   ,							__a   ,							""		)		)		)

								# 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(__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     snake_case_   (self		) ->   int:
								UpperCamelCase   =	AutoTokenizer.from_pretrained(
								    "impira/layoutlm-document-qa"   ,							revision="3dc6de3"   ,							add_prefix_space=__a		)
								UpperCamelCase   =	pipeline(
								    "document-question-answering"   ,							model="impira/layoutlm-document-qa"   ,							tokenizer=__a   ,							revision="3dc6de3"   ,							max_seq_len=50   ,							)
								UpperCamelCase   =	INVOICE_URL
								UpperCamelCase   =	"What is the invoice number?"

								UpperCamelCase   =	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},
								    ]   ,							)

								UpperCamelCase   =	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   ,							)

								UpperCamelCase   =	list(zip(*apply_tesseract(load_image(__a		)   ,							__a   ,							""		)		)		)

								# 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(__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     snake_case_   (self		) ->   Optional[Any]:
								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=__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     snake_case_   (self		) ->   List[str]:
								pass













"""simple docstring"""





def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		return number & 1 == 0


if __name__ == "__main__":
						import doctest

						doctest.testmod()






