
"""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    :       Tuple							, **_snake_case    :       str						)   ->			Optional[int]:







										"""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_       :    List[Any]						      =			(
    '''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    :       Union[str, Any]							, _snake_case    :       Union[str, Any]							, _snake_case    :       Union[str, Any]							, _snake_case    :       Tuple						)   ->			List[Any]:







								"""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    :       Optional[Any]							, _snake_case    :       List[str]							, _snake_case    :       int						)   ->			int:







								"""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]						)   ->			Union[str, Any]:







								"""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    :       str						)   ->			Union[str, 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    :       List[str]						)   ->			List[str]:







								"""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    :       Tuple						)   ->			str:







								"""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    :       Optional[int]						)   ->			Dict:







								"""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    :       List[str]						)   ->			Tuple:







								"""simple docstring"""
								pass






"""simple docstring"""





from __future__ import annotations
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    0.00
					A_								=    0
					for resistor in resistors:
										if resistor <= 0:
															A_								=    f'Resistor at index {index} has a negative or zero value!'
															raise ValueError(__a					)
										first_sum += 1 / float(__a					)
										index += 1
					return 1 / first_sum
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    0.00
					A_								=    0
					for resistor in resistors:
										sum_r += resistor
										if resistor < 0:
															A_								=    f'Resistor at index {index} has a negative value!'
															raise ValueError(__a					)
										index += 1
					return sum_r


if __name__ == "__main__":
		import doctest

		doctest.testmod()




