


'''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 		a__    :





									'''simple docstring'''






									@staticmethod
									def   __SCREAMING_SNAKE_CASE							(		*lowerCamelCase_ ,   **lowerCamelCase_       )	->							Dict:
																pass


		def  _snake_case       (					A	)						->   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       =							(
    '''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'''
)




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





							'''simple docstring'''






							lowercase__      :       Optional[Any]     						=				MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING







							@require_pytesseract
							@require_vision
							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							List[str]:
														lowerCAmelCase__	       =     pipeline(
														    '''document-question-answering''' ,   model=lowerCamelCase_ ,   tokenizer=lowerCamelCase_ ,   image_processor=lowerCamelCase_       )

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







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Optional[Any]:
														lowerCAmelCase__	       =     dqa_pipeline(lowerCamelCase_ ,   top_k=2       )
														self.assertEqual(
														    lowerCamelCase_ ,   [
														        [
														            {'''score''': ANY(lowerCamelCase_       ), '''answer''': ANY(lowerCamelCase_       ), '''start''': ANY(lowerCamelCase_       ), '''end''': ANY(lowerCamelCase_       )},
														            {'''score''': ANY(lowerCamelCase_       ), '''answer''': ANY(lowerCamelCase_       ), '''start''': ANY(lowerCamelCase_       ), '''end''': ANY(lowerCamelCase_       )},
														        ]
														    ]
														    * 3 ,   )







							@require_torch
							@require_detectrona
							@require_pytesseract
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Tuple:
														lowerCAmelCase__	       =     pipeline('''document-question-answering''' ,   model='''hf-internal-testing/tiny-random-layoutlmv2'''       )
														lowerCAmelCase__	       =     INVOICE_URL
														lowerCAmelCase__	       =     '''How many cats are there?'''

														lowerCAmelCase__	       =     [
														    {'''score''': 0.0_001, '''answer''': '''oy 2312/2019''', '''start''': 38, '''end''': 39},
														    {'''score''': 0.0_001, '''answer''': '''oy 2312/2019 DUE''', '''start''': 38, '''end''': 40},
														]
														lowerCAmelCase__	       =     dqa_pipeline(image=lowerCamelCase_ ,   question=lowerCamelCase_ ,   top_k=2       )
														self.assertEqual(nested_simplify(lowerCamelCase_ ,   decimals=4       ) ,   lowerCamelCase_       )

														lowerCAmelCase__	       =     dqa_pipeline({'''image''': image, '''question''': question} ,   top_k=2       )
														self.assertEqual(nested_simplify(lowerCamelCase_ ,   decimals=4       ) ,   lowerCamelCase_       )

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

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







							@slow
							@require_torch
							@require_detectrona
							@require_pytesseract
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														lowerCAmelCase__	       =     pipeline(
														    '''document-question-answering''' ,   model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa''' ,   revision='''9977165''' ,   )
														lowerCAmelCase__	       =     INVOICE_URL
														lowerCAmelCase__	       =     '''What is the invoice number?'''

														lowerCAmelCase__	       =     dqa_pipeline(image=lowerCamelCase_ ,   question=lowerCamelCase_ ,   top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_ ,   decimals=4       ) ,   [
														        {'''score''': 0.9_944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
														        {'''score''': 0.0_009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
														    ] ,   )

														lowerCAmelCase__	       =     dqa_pipeline({'''image''': image, '''question''': question} ,   top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_ ,   decimals=4       ) ,   [
														        {'''score''': 0.9_944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
														        {'''score''': 0.0_009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
														    ] ,   )

														lowerCAmelCase__	       =     dqa_pipeline(
														    [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] ,   top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_ ,   decimals=4       ) ,   [
														        [
														            {'''score''': 0.9_944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
														            {'''score''': 0.0_009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
														        ],
														    ]
														    * 2 ,   )







							@slow
							@require_torch
							@require_detectrona
							@require_pytesseract
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														lowerCAmelCase__	       =     pipeline(
														    '''document-question-answering''' ,   model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa''' ,   revision='''9977165''' ,   max_seq_len=50 ,   )
														lowerCAmelCase__	       =     INVOICE_URL
														lowerCAmelCase__	       =     '''What is the invoice number?'''

														lowerCAmelCase__	       =     dqa_pipeline(image=lowerCamelCase_ ,   question=lowerCamelCase_ ,   top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_ ,   decimals=4       ) ,   [
														        {'''score''': 0.9_974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
														        {'''score''': 0.9_948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
														    ] ,   )

														lowerCAmelCase__	       =     dqa_pipeline({'''image''': image, '''question''': question} ,   top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_ ,   decimals=4       ) ,   [
														        {'''score''': 0.9_974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
														        {'''score''': 0.9_948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
														    ] ,   )

														lowerCAmelCase__	       =     dqa_pipeline(
														    [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] ,   top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_ ,   decimals=4       ) ,   [
														        [
														            {'''score''': 0.9_974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
														            {'''score''': 0.9_948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
														        ]
														    ]
														    * 2 ,   )







							@slow
							@require_torch
							@require_pytesseract
							@require_vision
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Tuple:
														lowerCAmelCase__	       =     AutoTokenizer.from_pretrained(
														    '''impira/layoutlm-document-qa''' ,   revision='''3dc6de3''' ,   add_prefix_space=lowerCamelCase_       )
														lowerCAmelCase__	       =     pipeline(
														    '''document-question-answering''' ,   model='''impira/layoutlm-document-qa''' ,   tokenizer=lowerCamelCase_ ,   revision='''3dc6de3''' ,   )
														lowerCAmelCase__	       =     INVOICE_URL
														lowerCAmelCase__	       =     '''What is the invoice number?'''

														lowerCAmelCase__	       =     dqa_pipeline(image=lowerCamelCase_ ,   question=lowerCamelCase_ ,   top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_ ,   decimals=4       ) ,   [
														        {'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
														        {'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
														    ] ,   )

														lowerCAmelCase__	       =     dqa_pipeline({'''image''': image, '''question''': question} ,   top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_ ,   decimals=4       ) ,   [
														        {'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
														        {'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
														    ] ,   )

														lowerCAmelCase__	       =     dqa_pipeline(
														    [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] ,   top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_ ,   decimals=4       ) ,   [
														        [
														            {'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
														            {'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
														        ]
														    ]
														    * 2 ,   )

														lowerCAmelCase__	       =     list(zip(*apply_tesseract(load_image(lowerCamelCase_       ) ,   lowerCamelCase_ ,   ''''''       )       )       )

														# This model should also work if `image` is set to None
														lowerCAmelCase__	       =     dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question} ,   top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_ ,   decimals=4       ) ,   [
														        {'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
														        {'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
														    ] ,   )







							@slow
							@require_torch
							@require_pytesseract
							@require_vision
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														lowerCAmelCase__	       =     AutoTokenizer.from_pretrained(
														    '''impira/layoutlm-document-qa''' ,   revision='''3dc6de3''' ,   add_prefix_space=lowerCamelCase_       )
														lowerCAmelCase__	       =     pipeline(
														    '''document-question-answering''' ,   model='''impira/layoutlm-document-qa''' ,   tokenizer=lowerCamelCase_ ,   revision='''3dc6de3''' ,   max_seq_len=50 ,   )
														lowerCAmelCase__	       =     INVOICE_URL
														lowerCAmelCase__	       =     '''What is the invoice number?'''

														lowerCAmelCase__	       =     dqa_pipeline(image=lowerCamelCase_ ,   question=lowerCamelCase_ ,   top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_ ,   decimals=4       ) ,   [
														        {'''score''': 0.9_999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
														        {'''score''': 0.9_998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
														    ] ,   )

														lowerCAmelCase__	       =     dqa_pipeline(
														    [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] ,   top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_ ,   decimals=4       ) ,   [
														        [
														            {'''score''': 0.9_999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
														            {'''score''': 0.9_998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
														        ]
														    ]
														    * 2 ,   )

														lowerCAmelCase__	       =     list(zip(*apply_tesseract(load_image(lowerCamelCase_       ) ,   lowerCamelCase_ ,   ''''''       )       )       )

														# This model should also work if `image` is set to None
														lowerCAmelCase__	       =     dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question} ,   top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_ ,   decimals=4       ) ,   [
														        {'''score''': 0.9_999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
														        {'''score''': 0.9_998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
														    ] ,   )







							@slow
							@require_torch
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:
														lowerCAmelCase__	       =     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__	       =     INVOICE_URL
														lowerCAmelCase__	       =     '''What is the invoice number?'''
														lowerCAmelCase__	       =     dqa_pipeline(image=lowerCamelCase_ ,   question=lowerCamelCase_ ,   top_k=2       )
														self.assertEqual(nested_simplify(lowerCamelCase_ ,   decimals=4       ) ,   [{'''answer''': '''us-001'''}]       )







							@require_tf
							@unittest.skip('''Document question answering not implemented in TF'''       )
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														pass



'''simple docstring'''






from __future__ import annotations

import math
from collections.abc import Callable


def  _snake_case       (					A			,   A			,   A			,   A = 100			,   )						->   float:
							lowerCAmelCase__	       =     x_start
							lowerCAmelCase__	       =     fnc(A	)
							lowerCAmelCase__	       =     0.0

							for _ in range(A	):
														# Approximates curve as a sequence of linear lines and sums their length
														lowerCAmelCase__	       =     (x_end - x_start) / steps + xa
														lowerCAmelCase__	       =     fnc(A	)
														length += math.hypot(xa - xa			,   fxa - fxa	)

														# Increment step
														lowerCAmelCase__	       =     xa
														lowerCAmelCase__	       =     fxa

							return length


if __name__ == "__main__":


		def  _snake_case       (					A	)						->   List[Any]:
									return math.sin(10 * x	)

		print('''f(x) = sin(10 * x)''')
		print('''The length of the curve from x = -10 to x = 10 is:''')
		__UpperCAmelCase       =							10
		while i <= 100_000:
				print(f"""With {i} steps: {line_length(f, -10, 10, i)}""")
				i *= 10