



"""simple docstring"""


def  lowercase	(      _SCREAMING_SNAKE_CASE   :						list[int]					,     _SCREAMING_SNAKE_CASE   :						list[int]				):






				'''simple docstring'''



				if not len(_SCREAMING_SNAKE_CASE				) == len(_SCREAMING_SNAKE_CASE				) == 3:
								raise ValueError('''Please enter a valid equation.'''				)
				if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
								raise ValueError('''Both a & b of two equations can\'t be zero.'''				)

				# Extract the coefficients
				_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase					=    equationa
				_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase					=    equationa

				# Calculate the determinants of the matrices
				_UpperCAmelCase					=    aa * ba - aa * ba
				_UpperCAmelCase					=    ca * ba - ca * ba
				_UpperCAmelCase					=    aa * ca - aa * ca

				# Check if the system of linear equations has a solution (using Cramer's rule)
				if determinant == 0:
								if determinant_x == determinant_y == 0:
												raise ValueError('''Infinite solutions. (Consistent system)'''				)
								else:
												raise ValueError('''No solution. (Inconsistent system)'''				)
				else:
								if determinant_x == determinant_y == 0:
												# Trivial solution (Inconsistent system)
												return (0.0, 0.0)
								else:
												_UpperCAmelCase					=    determinant_x / determinant
												_UpperCAmelCase					=    determinant_y / determinant
												# Non-Trivial Solution (Consistent system)
												return (x, y)








"""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  lowercase__						(		*__UpperCamelCase     :    Dict				,   **__UpperCamelCase     :    Optional[Any]   )->List[str]:
												pass







					def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Union[str, Any]				):






									'''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.
__A   :							Dict										=   (
    "https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"
)
@is_pipeline_test
@require_torch
@require_vision
class 			_a (					unittest.TestCase):
			"""simple docstring"""
			UpperCamelCase__			      =	MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING



			@require_pytesseract
			@require_vision
			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Tuple				,   __UpperCamelCase     :    Any				,   __UpperCamelCase     :    str   )->Tuple:
							_UpperCAmelCase					=    pipeline(
							    '''document-question-answering'''				,   model=__UpperCamelCase				,   tokenizer=__UpperCamelCase				,   image_processor=__UpperCamelCase   )

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



			def  lowercase__						(		self     :    str				,   __UpperCamelCase     :    List[Any]				,   __UpperCamelCase     :    Optional[int]   )->Any:
							_UpperCAmelCase					=    dqa_pipeline(__UpperCamelCase				,   top_k=2   )
							self.assertEqual(
							    __UpperCamelCase				,   [
							        [
							            {'''score''': ANY(__UpperCamelCase   ), '''answer''': ANY(__UpperCamelCase   ), '''start''': ANY(__UpperCamelCase   ), '''end''': ANY(__UpperCamelCase   )},
							            {'''score''': ANY(__UpperCamelCase   ), '''answer''': ANY(__UpperCamelCase   ), '''start''': ANY(__UpperCamelCase   ), '''end''': ANY(__UpperCamelCase   )},
							        ]
							    ]
							    * 3				,   )



			@require_torch
			@require_detectrona
			@require_pytesseract
			def  lowercase__						(		self     :    str   )->Union[str, Any]:
							_UpperCAmelCase					=    pipeline('''document-question-answering'''				,   model='''hf-internal-testing/tiny-random-layoutlmv2'''   )
							_UpperCAmelCase					=    INVOICE_URL
							_UpperCAmelCase					=    '''How many cats are there?'''

							_UpperCAmelCase					=    [
							    {'''score''': 0.0_0_0_1, '''answer''': '''oy 2312/2019''', '''start''': 3_8, '''end''': 3_9},
							    {'''score''': 0.0_0_0_1, '''answer''': '''oy 2312/2019 DUE''', '''start''': 3_8, '''end''': 4_0},
							]
							_UpperCAmelCase					=    dqa_pipeline(image=__UpperCamelCase				,   question=__UpperCamelCase				,   top_k=2   )
							self.assertEqual(nested_simplify(__UpperCamelCase				,   decimals=4   )				,   __UpperCamelCase   )

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

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

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



			@slow
			@require_torch
			@require_detectrona
			@require_pytesseract
			def  lowercase__						(		self     :    Union[str, Any]   )->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=__UpperCamelCase				,   question=__UpperCamelCase				,   top_k=2   )
							self.assertEqual(
							    nested_simplify(__UpperCamelCase				,   decimals=4   )				,   [
							        {'''score''': 0.9_9_4_4, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
							        {'''score''': 0.0_0_0_9, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
							    ]				,   )

							_UpperCAmelCase					=    dqa_pipeline({'''image''': image, '''question''': question}				,   top_k=2   )
							self.assertEqual(
							    nested_simplify(__UpperCamelCase				,   decimals=4   )				,   [
							        {'''score''': 0.9_9_4_4, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
							        {'''score''': 0.0_0_0_9, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
							    ]				,   )

							_UpperCAmelCase					=    dqa_pipeline(
							    [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,   top_k=2   )
							self.assertEqual(
							    nested_simplify(__UpperCamelCase				,   decimals=4   )				,   [
							        [
							            {'''score''': 0.9_9_4_4, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
							            {'''score''': 0.0_0_0_9, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
							        ],
							    ]
							    * 2				,   )



			@slow
			@require_torch
			@require_detectrona
			@require_pytesseract
			def  lowercase__						(		self     :    int   )->Optional[int]:
							_UpperCAmelCase					=    pipeline(
							    '''document-question-answering'''				,   model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'''				,   revision='''9977165'''				,   max_seq_len=5_0				,   )
							_UpperCAmelCase					=    INVOICE_URL
							_UpperCAmelCase					=    '''What is the invoice number?'''

							_UpperCAmelCase					=    dqa_pipeline(image=__UpperCamelCase				,   question=__UpperCamelCase				,   top_k=2   )
							self.assertEqual(
							    nested_simplify(__UpperCamelCase				,   decimals=4   )				,   [
							        {'''score''': 0.9_9_7_4, '''answer''': '''1110212019''', '''start''': 2_3, '''end''': 2_3},
							        {'''score''': 0.9_9_4_8, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
							    ]				,   )

							_UpperCAmelCase					=    dqa_pipeline({'''image''': image, '''question''': question}				,   top_k=2   )
							self.assertEqual(
							    nested_simplify(__UpperCamelCase				,   decimals=4   )				,   [
							        {'''score''': 0.9_9_7_4, '''answer''': '''1110212019''', '''start''': 2_3, '''end''': 2_3},
							        {'''score''': 0.9_9_4_8, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
							    ]				,   )

							_UpperCAmelCase					=    dqa_pipeline(
							    [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,   top_k=2   )
							self.assertEqual(
							    nested_simplify(__UpperCamelCase				,   decimals=4   )				,   [
							        [
							            {'''score''': 0.9_9_7_4, '''answer''': '''1110212019''', '''start''': 2_3, '''end''': 2_3},
							            {'''score''': 0.9_9_4_8, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
							        ]
							    ]
							    * 2				,   )



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

							_UpperCAmelCase					=    dqa_pipeline(image=__UpperCamelCase				,   question=__UpperCamelCase				,   top_k=2   )
							self.assertEqual(
							    nested_simplify(__UpperCamelCase				,   decimals=4   )				,   [
							        {'''score''': 0.4_2_5_1, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
							        {'''score''': 0.0_8_1_9, '''answer''': '''1110212019''', '''start''': 2_3, '''end''': 2_3},
							    ]				,   )

							_UpperCAmelCase					=    dqa_pipeline({'''image''': image, '''question''': question}				,   top_k=2   )
							self.assertEqual(
							    nested_simplify(__UpperCamelCase				,   decimals=4   )				,   [
							        {'''score''': 0.4_2_5_1, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
							        {'''score''': 0.0_8_1_9, '''answer''': '''1110212019''', '''start''': 2_3, '''end''': 2_3},
							    ]				,   )

							_UpperCAmelCase					=    dqa_pipeline(
							    [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,   top_k=2   )
							self.assertEqual(
							    nested_simplify(__UpperCamelCase				,   decimals=4   )				,   [
							        [
							            {'''score''': 0.4_2_5_1, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
							            {'''score''': 0.0_8_1_9, '''answer''': '''1110212019''', '''start''': 2_3, '''end''': 2_3},
							        ]
							    ]
							    * 2				,   )

							_UpperCAmelCase					=    list(zip(*apply_tesseract(load_image(__UpperCamelCase   )				,   __UpperCamelCase				,   ''''''   )   )   )

							# 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(__UpperCamelCase				,   decimals=4   )				,   [
							        {'''score''': 0.4_2_5_1, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
							        {'''score''': 0.0_8_1_9, '''answer''': '''1110212019''', '''start''': 2_3, '''end''': 2_3},
							    ]				,   )



			@slow
			@require_torch
			@require_pytesseract
			@require_vision
			def  lowercase__						(		self     :    List[str]   )->Any:
							_UpperCAmelCase					=    AutoTokenizer.from_pretrained(
							    '''impira/layoutlm-document-qa'''				,   revision='''3dc6de3'''				,   add_prefix_space=__UpperCamelCase   )
							_UpperCAmelCase					=    pipeline(
							    '''document-question-answering'''				,   model='''impira/layoutlm-document-qa'''				,   tokenizer=__UpperCamelCase				,   revision='''3dc6de3'''				,   max_seq_len=5_0				,   )
							_UpperCAmelCase					=    INVOICE_URL
							_UpperCAmelCase					=    '''What is the invoice number?'''

							_UpperCAmelCase					=    dqa_pipeline(image=__UpperCamelCase				,   question=__UpperCamelCase				,   top_k=2   )
							self.assertEqual(
							    nested_simplify(__UpperCamelCase				,   decimals=4   )				,   [
							        {'''score''': 0.9_9_9_9, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
							        {'''score''': 0.9_9_9_8, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
							    ]				,   )

							_UpperCAmelCase					=    dqa_pipeline(
							    [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,   top_k=2   )
							self.assertEqual(
							    nested_simplify(__UpperCamelCase				,   decimals=4   )				,   [
							        [
							            {'''score''': 0.9_9_9_9, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
							            {'''score''': 0.9_9_9_8, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
							        ]
							    ]
							    * 2				,   )

							_UpperCAmelCase					=    list(zip(*apply_tesseract(load_image(__UpperCamelCase   )				,   __UpperCamelCase				,   ''''''   )   )   )

							# 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(__UpperCamelCase				,   decimals=4   )				,   [
							        {'''score''': 0.9_9_9_9, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
							        {'''score''': 0.9_9_9_8, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
							    ]				,   )



			@slow
			@require_torch
			def  lowercase__						(		self     :    int   )->str:
							_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=__UpperCamelCase				,   question=__UpperCamelCase				,   top_k=2   )
							self.assertEqual(nested_simplify(__UpperCamelCase				,   decimals=4   )				,   [{'''answer''': '''us-001'''}]   )



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



