
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        SCREAMING_SNAKE_CASE__					:





				'''simple docstring'''



				@staticmethod
				def 			A							(  *lowercase     :   Optional[Any]      ,  **lowercase     :   Optional[Any]     ):


							'''simple docstring'''


							pass






			def 	a_		(	__lowercase       :     str			)			->  Any:
						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.
_lowerCamelCase	:    Dict          =      (
    '''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'''
)




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





	'''simple docstring'''



	_UpperCAmelCase	:		List[str]          = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING



	@require_pytesseract
	@require_vision
	def 			A							(  self     :   List[Any]      ,  lowercase     :   int      ,  lowercase     :   List[str]      ,  lowercase     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      pipeline(
				    'document-question-answering'      ,  model=lowercase      ,  tokenizer=lowercase      ,  image_processor=lowercase     )

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



	def 			A							(  self     :   List[Any]      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      dqa_pipeline(lowercase      ,  top_k=2     )
				self.assertEqual(
				    lowercase      ,  [
				        [
				            {'score': ANY(lowercase     ), 'answer': ANY(lowercase     ), 'start': ANY(lowercase     ), 'end': ANY(lowercase     )},
				            {'score': ANY(lowercase     ), 'answer': ANY(lowercase     ), 'start': ANY(lowercase     ), 'end': ANY(lowercase     )},
				        ]
				    ]
				    * 3      ,  )



	@require_torch
	@require_detectrona
	@require_pytesseract
	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


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

				_snake_case          =      [
				    {'score': 0.0001, 'answer': 'oy 2312/2019', 'start': 38, 'end': 39},
				    {'score': 0.0001, 'answer': 'oy 2312/2019 DUE', 'start': 38, 'end': 40},
				]
				_snake_case          =      dqa_pipeline(image=lowercase      ,  question=lowercase      ,  top_k=2     )
				self.assertEqual(nested_simplify(lowercase      ,  decimals=4     )      ,  lowercase     )

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

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

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



	@slow
	@require_torch
	@require_detectrona
	@require_pytesseract
	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


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

				_snake_case          =      dqa_pipeline(image=lowercase      ,  question=lowercase      ,  top_k=2     )
				self.assertEqual(
				    nested_simplify(lowercase      ,  decimals=4     )      ,  [
				        {'score': 0.9944, 'answer': 'us-001', 'start': 16, 'end': 16},
				        {'score': 0.0009, 'answer': 'us-001', 'start': 16, 'end': 16},
				    ]      ,  )

				_snake_case          =      dqa_pipeline({'image': image, 'question': question}      ,  top_k=2     )
				self.assertEqual(
				    nested_simplify(lowercase      ,  decimals=4     )      ,  [
				        {'score': 0.9944, 'answer': 'us-001', 'start': 16, 'end': 16},
				        {'score': 0.0009, 'answer': 'us-001', 'start': 16, 'end': 16},
				    ]      ,  )

				_snake_case          =      dqa_pipeline(
				    [{'image': image, 'question': question}, {'image': image, 'question': question}]      ,  top_k=2     )
				self.assertEqual(
				    nested_simplify(lowercase      ,  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 			A							(  self     :   Tuple     ):


				'''simple docstring'''


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

				_snake_case          =      dqa_pipeline(image=lowercase      ,  question=lowercase      ,  top_k=2     )
				self.assertEqual(
				    nested_simplify(lowercase      ,  decimals=4     )      ,  [
				        {'score': 0.9974, 'answer': '1110212019', 'start': 23, 'end': 23},
				        {'score': 0.9948, 'answer': 'us-001', 'start': 16, 'end': 16},
				    ]      ,  )

				_snake_case          =      dqa_pipeline({'image': image, 'question': question}      ,  top_k=2     )
				self.assertEqual(
				    nested_simplify(lowercase      ,  decimals=4     )      ,  [
				        {'score': 0.9974, 'answer': '1110212019', 'start': 23, 'end': 23},
				        {'score': 0.9948, 'answer': 'us-001', 'start': 16, 'end': 16},
				    ]      ,  )

				_snake_case          =      dqa_pipeline(
				    [{'image': image, 'question': question}, {'image': image, 'question': question}]      ,  top_k=2     )
				self.assertEqual(
				    nested_simplify(lowercase      ,  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 			A							(  self     :   str     ):


				'''simple docstring'''


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

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

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

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

				_snake_case          =      list(zip(*apply_tesseract(load_image(lowercase     )      ,  lowercase      ,  ''     )     )     )

				# This model should also work if `image` is set to None
				_snake_case          =      dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question}      ,  top_k=2     )
				self.assertEqual(
				    nested_simplify(lowercase      ,  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 			A							(  self     :   Any     ):


				'''simple docstring'''


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

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

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

				_snake_case          =      list(zip(*apply_tesseract(load_image(lowercase     )      ,  lowercase      ,  ''     )     )     )

				# This model should also work if `image` is set to None
				_snake_case          =      dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question}      ,  top_k=2     )
				self.assertEqual(
				    nested_simplify(lowercase      ,  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 			A							(  self     :   Dict     ):


				'''simple docstring'''


				_snake_case          =      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'      ,  )

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





	@require_tf
	@unittest.skip('Document question answering not implemented in TF'     )
	def 			A							(  self     :   int     ):


				'''simple docstring'''


				pass

def 	a_		(	__lowercase       :     list[int]	, __lowercase       :     list[int]			)			->  tuple[float, float]:

			# Check if the input is valid
			if not len(__lowercase			) == len(__lowercase			) == 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
			_snake_case     , _snake_case     , _snake_case          =      equationa
			_snake_case     , _snake_case     , _snake_case          =      equationa

			# Calculate the determinants of the matrices
			_snake_case          =      aa * ba - aa * ba
			_snake_case          =      ca * ba - ca * ba
			_snake_case          =      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:
									_snake_case          =      determinant_x / determinant
									_snake_case          =      determinant_y / determinant
									# Non-Trivial Solution (Consistent system)
									return (x, y)