
"""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				:




					"""simple docstring"""







					@staticmethod
					def      lowerCamelCase__     (				*_snake_case						:					List[str]       ,   **_snake_case						:					Dict	)      -> Tuple:







										"""simple docstring"""
										pass





		def 							A_     (__a				):

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



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




			"""simple docstring"""







			snake_case	      =				MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING




			@require_pytesseract
			@require_vision
			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					int       ,   _snake_case						:					Union[str, Any]       ,   _snake_case						:					Tuple	)      -> Dict:







								"""simple docstring"""
								A_				 =						pipeline(
								    "document-question-answering"       ,   model=_snake_case       ,   tokenizer=_snake_case       ,   image_processor=_snake_case	)

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




			def      lowerCamelCase__     (				self						:					Any       ,   _snake_case						:					List[Any]       ,   _snake_case						:					Dict	)      -> Dict:







								"""simple docstring"""
								A_				 =						dqa_pipeline(_snake_case       ,   top_k=2	)
								self.assertEqual(
								    _snake_case       ,   [
								        [
								            {"score": ANY(_snake_case	), "answer": ANY(_snake_case	), "start": ANY(_snake_case	), "end": ANY(_snake_case	)},
								            {"score": ANY(_snake_case	), "answer": ANY(_snake_case	), "start": ANY(_snake_case	), "end": ANY(_snake_case	)},
								        ]
								    ]
								    * 3       ,   )




			@require_torch
			@require_detectrona
			@require_pytesseract
			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> Tuple:







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

								A_				 =						[
								    {"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},
								]
								A_				 =						dqa_pipeline(image=_snake_case       ,   question=_snake_case       ,   top_k=2	)
								self.assertEqual(nested_simplify(_snake_case       ,   decimals=4	)       ,   _snake_case	)

								A_				 =						dqa_pipeline({"image": image, "question": question}       ,   top_k=2	)
								self.assertEqual(nested_simplify(_snake_case       ,   decimals=4	)       ,   _snake_case	)

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

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




			@slow
			@require_torch
			@require_detectrona
			@require_pytesseract
			def      lowerCamelCase__     (				self						:					Dict	)      -> Optional[Any]:







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

								A_				 =						dqa_pipeline(image=_snake_case       ,   question=_snake_case       ,   top_k=2	)
								self.assertEqual(
								    nested_simplify(_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},
								    ]       ,   )

								A_				 =						dqa_pipeline({"image": image, "question": question}       ,   top_k=2	)
								self.assertEqual(
								    nested_simplify(_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},
								    ]       ,   )

								A_				 =						dqa_pipeline(
								    [{"image": image, "question": question}, {"image": image, "question": question}]       ,   top_k=2	)
								self.assertEqual(
								    nested_simplify(_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      lowerCamelCase__     (				self						:					int	)      -> Any:







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

								A_				 =						dqa_pipeline(image=_snake_case       ,   question=_snake_case       ,   top_k=2	)
								self.assertEqual(
								    nested_simplify(_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},
								    ]       ,   )

								A_				 =						dqa_pipeline({"image": image, "question": question}       ,   top_k=2	)
								self.assertEqual(
								    nested_simplify(_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},
								    ]       ,   )

								A_				 =						dqa_pipeline(
								    [{"image": image, "question": question}, {"image": image, "question": question}]       ,   top_k=2	)
								self.assertEqual(
								    nested_simplify(_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      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						AutoTokenizer.from_pretrained(
								    "impira/layoutlm-document-qa"       ,   revision="3dc6de3"       ,   add_prefix_space=_snake_case	)
								A_				 =						pipeline(
								    "document-question-answering"       ,   model="impira/layoutlm-document-qa"       ,   tokenizer=_snake_case       ,   revision="3dc6de3"       ,   )
								A_				 =						INVOICE_URL
								A_				 =						"What is the invoice number?"

								A_				 =						dqa_pipeline(image=_snake_case       ,   question=_snake_case       ,   top_k=2	)
								self.assertEqual(
								    nested_simplify(_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},
								    ]       ,   )

								A_				 =						dqa_pipeline({"image": image, "question": question}       ,   top_k=2	)
								self.assertEqual(
								    nested_simplify(_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},
								    ]       ,   )

								A_				 =						dqa_pipeline(
								    [{"image": image, "question": question}, {"image": image, "question": question}]       ,   top_k=2	)
								self.assertEqual(
								    nested_simplify(_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       ,   )

								A_				 =						list(zip(*apply_tesseract(load_image(_snake_case	)       ,   _snake_case       ,   ""	)	)	)

								# This model should also work if `image` is set to None
								A_				 =						dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}       ,   top_k=2	)
								self.assertEqual(
								    nested_simplify(_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      lowerCamelCase__     (				self						:					List[Any]	)      -> Dict:







								"""simple docstring"""
								A_				 =						AutoTokenizer.from_pretrained(
								    "impira/layoutlm-document-qa"       ,   revision="3dc6de3"       ,   add_prefix_space=_snake_case	)
								A_				 =						pipeline(
								    "document-question-answering"       ,   model="impira/layoutlm-document-qa"       ,   tokenizer=_snake_case       ,   revision="3dc6de3"       ,   max_seq_len=50       ,   )
								A_				 =						INVOICE_URL
								A_				 =						"What is the invoice number?"

								A_				 =						dqa_pipeline(image=_snake_case       ,   question=_snake_case       ,   top_k=2	)
								self.assertEqual(
								    nested_simplify(_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},
								    ]       ,   )

								A_				 =						dqa_pipeline(
								    [{"image": image, "question": question}, {"image": image, "question": question}]       ,   top_k=2	)
								self.assertEqual(
								    nested_simplify(_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       ,   )

								A_				 =						list(zip(*apply_tesseract(load_image(_snake_case	)       ,   _snake_case       ,   ""	)	)	)

								# This model should also work if `image` is set to None
								A_				 =						dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}       ,   top_k=2	)
								self.assertEqual(
								    nested_simplify(_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      lowerCamelCase__     (				self						:					Dict	)      -> int:







								"""simple docstring"""
								A_				 =						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"       ,   )

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






			@require_tf
			@unittest.skip("Document question answering not implemented in TF"	)
			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Union[str, Any]:







								"""simple docstring"""
								pass






"""simple docstring"""





def 							A_     (__a				,   __a				,   __a				):

					'''simple docstring'''





					A_				 =						len(__a				)
					A_				 =						[[0] * n for i in range(__a				)]
					for i in range(__a				):
										A_				 =						y_points[i]

					for i in range(2				,   __a				):
										for j in range(__a				,   __a				):
															A_				 =						(
															    (xa - x_points[j - i + 1]) * q[j][i - 1]
															    - (xa - x_points[j]) * q[j - 1][i - 1]
															) / (x_points[j] - x_points[j - i + 1])

					return [q[n - 1][n - 1], q]


if __name__ == "__main__":
		import doctest

		doctest.testmod()




