




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       lowerCamelCase__							(						*__magic_name__						:Optional[Any]		,      **__magic_name__						:List[str]				):







										'''simple docstring'''







										pass







		def 		__A		(	__lowerCamelCase    )      ->       List[str]:
			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					:       Dict    					=      (
    "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       lowerCamelCase__							(						self						:Any		,      __magic_name__						:Dict		,      __magic_name__						:Union[str, Any]		,      __magic_name__						:str				):







								'''simple docstring'''







								a										=							pipeline(
								    """document-question-answering"""		,      model=__magic_name__		,      tokenizer=__magic_name__		,      image_processor=__magic_name__				)

								a										=							INVOICE_URL
								a										=							list(zip(*apply_tesseract(load_image(__magic_name__				)		,      __magic_name__		,      """"""				)				)				)
								a										=							"""What is the placebo?"""
								a										=							[
								    {
								        """image""": load_image(__magic_name__				),
								        """question""": question,
								    },
								    {
								        """image""": image,
								        """question""": question,
								    },
								    {
								        """image""": image,
								        """question""": question,
								        """word_boxes""": word_boxes,
								    },
								]
								return dqa_pipeline, examples
							def       lowerCamelCase__							(						self						:List[Any]		,      __magic_name__						:List[Any]		,      __magic_name__						:Tuple				):







								'''simple docstring'''







								a										=							dqa_pipeline(__magic_name__		,      top_k=2				)
								self.assertEqual(
								    __magic_name__		,      [
								        [
								            {"""score""": ANY(__magic_name__				), """answer""": ANY(__magic_name__				), """start""": ANY(__magic_name__				), """end""": ANY(__magic_name__				)},
								            {"""score""": ANY(__magic_name__				), """answer""": ANY(__magic_name__				), """start""": ANY(__magic_name__				), """end""": ANY(__magic_name__				)},
								        ]
								    ]
								    * 3		,      )
							@require_torch
							@require_detectrona
							@require_pytesseract
							def       lowerCamelCase__							(						self						: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""": 0.0001, """answer""": """oy 2312/2019""", """start""": 38, """end""": 39},
								    {"""score""": 0.0001, """answer""": """oy 2312/2019 DUE""", """start""": 38, """end""": 40},
								]
								a										=							dqa_pipeline(image=__magic_name__		,      question=__magic_name__		,      top_k=2				)
								self.assertEqual(nested_simplify(__magic_name__		,      decimals=4				)		,      __magic_name__				)

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

								# 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=__magic_name__		,      question=__magic_name__		,      top_k=2				)
								self.assertEqual(__magic_name__		,      []				)

								# We can optionnally pass directly the words and bounding boxes
								a										=							"""./tests/fixtures/tests_samples/COCO/000000039769.png"""
								a										=							[]
								a										=							[]
								a										=							dqa_pipeline(image=__magic_name__		,      question=__magic_name__		,      words=__magic_name__		,      boxes=__magic_name__		,      top_k=2				)
								self.assertEqual(__magic_name__		,      []				)
							@slow
							@require_torch
							@require_detectrona
							@require_pytesseract
							def       lowerCamelCase__							(						self						: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=__magic_name__		,      question=__magic_name__		,      top_k=2				)
								self.assertEqual(
								    nested_simplify(__magic_name__		,      decimals=4				)		,      [
								        {"""score""": 0.9944, """answer""": """us-001""", """start""": 16, """end""": 16},
								        {"""score""": 0.0009, """answer""": """us-001""", """start""": 16, """end""": 16},
								    ]		,      )

								a										=							dqa_pipeline({"""image""": image, """question""": question}		,      top_k=2				)
								self.assertEqual(
								    nested_simplify(__magic_name__		,      decimals=4				)		,      [
								        {"""score""": 0.9944, """answer""": """us-001""", """start""": 16, """end""": 16},
								        {"""score""": 0.0009, """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(__magic_name__		,      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       lowerCamelCase__							(						self						: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=__magic_name__		,      question=__magic_name__		,      top_k=2				)
								self.assertEqual(
								    nested_simplify(__magic_name__		,      decimals=4				)		,      [
								        {"""score""": 0.9974, """answer""": """1110212019""", """start""": 23, """end""": 23},
								        {"""score""": 0.9948, """answer""": """us-001""", """start""": 16, """end""": 16},
								    ]		,      )

								a										=							dqa_pipeline({"""image""": image, """question""": question}		,      top_k=2				)
								self.assertEqual(
								    nested_simplify(__magic_name__		,      decimals=4				)		,      [
								        {"""score""": 0.9974, """answer""": """1110212019""", """start""": 23, """end""": 23},
								        {"""score""": 0.9948, """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(__magic_name__		,      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       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







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

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

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

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

								a										=							list(zip(*apply_tesseract(load_image(__magic_name__				)		,      __magic_name__		,      """"""				)				)				)

								# 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(__magic_name__		,      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       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







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

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

								a										=							list(zip(*apply_tesseract(load_image(__magic_name__				)		,      __magic_name__		,      """"""				)				)				)

								# 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(__magic_name__		,      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       lowerCamelCase__							(						self						:Optional[Any]				):







								'''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=__magic_name__		,      question=__magic_name__		,      top_k=2				)
								self.assertEqual(nested_simplify(__magic_name__		,      decimals=4				)		,      [{"""answer""": """us-001"""}]				)





							@require_tf
							@unittest.skip("""Document question answering not implemented in TF"""				)
							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								pass







def 		__A		(	__lowerCamelCase    )      ->       int:
	a										=							0
	while num > 0:
		digit_sum += num % 10
		num //= 10
	return digit_sum





def 		__A		(	__lowerCamelCase = 100    )      ->       int:
	a										=							1
	a										=							2

	for i in range(2							,			max_n + 1    ):
		a										=							pre_numerator
		a										=							2 * i // 3 if i % 3 == 0 else 1
		a										=							cur_numerator
		a										=							e_cont * pre_numerator + temp

	return sum_digits(__lowerCamelCase    )


if __name__ == "__main__":
		print(F'{solution() = }')

