





'''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  :



						@staticmethod
						def   _lowerCamelCase    (   *a_		,      **a_	):
								pass






					def      __A						(							a_						:  Optional[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       		=      (
    """https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"""
)
@is_pipeline_test
@require_torch
@require_vision
class 		lowerCamelCase  (      unittest.TestCase       ):
	snake_case_      =   MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING



	@require_pytesseract
	@require_vision
	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_	):
			lowerCAmelCase     :    Optional[Any]						=   pipeline(
			    "document-question-answering"		,      model=a_		,      tokenizer=a_		,      image_processor=a_	)

			lowerCAmelCase     :    List[str]						=   INVOICE_URL
			lowerCAmelCase     :    Union[str, Any]						=   list(zip(*apply_tesseract(load_image(a_	)		,      a_		,      ""	)	)	)
			lowerCAmelCase     :    Optional[int]						=   "What is the placebo?"
			lowerCAmelCase     :    Any						=   [
			    {
			        "image": load_image(a_	),
			        "question": question,
			    },
			    {
			        "image": image,
			        "question": question,
			    },
			    {
			        "image": image,
			        "question": question,
			        "word_boxes": word_boxes,
			    },
			]
			return dqa_pipeline, examples



	def   _lowerCamelCase    (   self		,      a_		,      a_	):
			lowerCAmelCase     :    Union[str, Any]						=   dqa_pipeline(a_		,      top_k=2	)
			self.assertEqual(
			    a_		,      [
			        [
			            {"score": ANY(a_	), "answer": ANY(a_	), "start": ANY(a_	), "end": ANY(a_	)},
			            {"score": ANY(a_	), "answer": ANY(a_	), "start": ANY(a_	), "end": ANY(a_	)},
			        ]
			    ]
			    * 3		,      )



	@require_torch
	@require_detectrona
	@require_pytesseract
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[Any]						=   pipeline("document-question-answering"		,      model="hf-internal-testing/tiny-random-layoutlmv2"	)
			lowerCAmelCase     :    Dict						=   INVOICE_URL
			lowerCAmelCase     :    Optional[Any]						=   "How many cats are there?"

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

			lowerCAmelCase     :    List[str]						=   dqa_pipeline({"image": image, "question": question}		,      top_k=2	)
			self.assertEqual(nested_simplify(a_		,      decimals=4	)		,      a_	)

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

			# We can optionnally pass directly the words and bounding boxes
			lowerCAmelCase     :    List[Any]						=   "./tests/fixtures/tests_samples/COCO/000000039769.png"
			lowerCAmelCase     :    Optional[int]						=   []
			lowerCAmelCase     :    Any						=   []
			lowerCAmelCase     :    Optional[int]						=   dqa_pipeline(image=a_		,      question=a_		,      words=a_		,      boxes=a_		,      top_k=2	)
			self.assertEqual(a_		,      []	)



	@slow
	@require_torch
	@require_detectrona
	@require_pytesseract
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   pipeline(
			    "document-question-answering"		,      model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"		,      revision="9977165"		,      )
			lowerCAmelCase     :    Dict						=   INVOICE_URL
			lowerCAmelCase     :    List[str]						=   "What is the invoice number?"

			lowerCAmelCase     :    Optional[int]						=   dqa_pipeline(image=a_		,      question=a_		,      top_k=2	)
			self.assertEqual(
			    nested_simplify(a_		,      decimals=4	)		,      [
			        {"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
			        {"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
			    ]		,      )

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

			lowerCAmelCase     :    Dict						=   dqa_pipeline(
			    [{"image": image, "question": question}, {"image": image, "question": question}]		,      top_k=2	)
			self.assertEqual(
			    nested_simplify(a_		,      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	):
			lowerCAmelCase     :    Optional[int]						=   pipeline(
			    "document-question-answering"		,      model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"		,      revision="9977165"		,      max_seq_len=50		,      )
			lowerCAmelCase     :    int						=   INVOICE_URL
			lowerCAmelCase     :    Any						=   "What is the invoice number?"

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

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

			lowerCAmelCase     :    int						=   dqa_pipeline(
			    [{"image": image, "question": question}, {"image": image, "question": question}]		,      top_k=2	)
			self.assertEqual(
			    nested_simplify(a_		,      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	):
			lowerCAmelCase     :    Tuple						=   AutoTokenizer.from_pretrained(
			    "impira/layoutlm-document-qa"		,      revision="3dc6de3"		,      add_prefix_space=a_	)
			lowerCAmelCase     :    Tuple						=   pipeline(
			    "document-question-answering"		,      model="impira/layoutlm-document-qa"		,      tokenizer=a_		,      revision="3dc6de3"		,      )
			lowerCAmelCase     :    Optional[Any]						=   INVOICE_URL
			lowerCAmelCase     :    Any						=   "What is the invoice number?"

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

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

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

			lowerCAmelCase     :    List[str]						=   list(zip(*apply_tesseract(load_image(a_	)		,      a_		,      ""	)	)	)

			# This model should also work if `image` is set to None
			lowerCAmelCase     :    int						=   dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}		,      top_k=2	)
			self.assertEqual(
			    nested_simplify(a_		,      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	):
			lowerCAmelCase     :    Tuple						=   AutoTokenizer.from_pretrained(
			    "impira/layoutlm-document-qa"		,      revision="3dc6de3"		,      add_prefix_space=a_	)
			lowerCAmelCase     :    Optional[Any]						=   pipeline(
			    "document-question-answering"		,      model="impira/layoutlm-document-qa"		,      tokenizer=a_		,      revision="3dc6de3"		,      max_seq_len=50		,      )
			lowerCAmelCase     :    int						=   INVOICE_URL
			lowerCAmelCase     :    str						=   "What is the invoice number?"

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

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

			lowerCAmelCase     :    Any						=   list(zip(*apply_tesseract(load_image(a_	)		,      a_		,      ""	)	)	)

			# This model should also work if `image` is set to None
			lowerCAmelCase     :    List[Any]						=   dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}		,      top_k=2	)
			self.assertEqual(
			    nested_simplify(a_		,      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	):
			lowerCAmelCase     :    Any						=   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     :    Union[str, Any]						=   INVOICE_URL
			lowerCAmelCase     :    Tuple						=   "What is the invoice number?"
			lowerCAmelCase     :    Any						=   dqa_pipeline(image=a_		,      question=a_		,      top_k=2	)
			self.assertEqual(nested_simplify(a_		,      decimals=4	)		,      [{"answer": "us-001"}]	)





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










'''simple docstring'''






import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


lowerCAmelCase       		=      """\
@article{hendrycksmath2021,
  title={Measuring Mathematical Problem Solving With the MATH Dataset},
  author={Dan Hendrycks
    and Collin Burns
    and Saurav Kadavath
    and Akul Arora
    and Steven Basart
    and Eric Tang
    and Dawn Song
    and Jacob Steinhardt},
  journal={arXiv preprint arXiv:2103.03874},
  year={2021}
}
"""


lowerCAmelCase       		=      """\
This metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.
It first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.
"""


lowerCAmelCase       		=      R"""
Calculates accuracy after canonicalizing inputs.

Args:
    predictions: list of predictions to score. Each prediction
        is a string that contains natural language and LaTex.
    references: list of reference for each prediction. Each
        reference is a string that contains natural language
        and LaTex.
Returns:
    accuracy: accuracy after canonicalizing inputs
        (e.g., converting \"1/2\" to \"\\frac{1}{2}\")

Examples:
    >>> metric = datasets.load_metric(\"competition_math\")
    >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])
    >>> print(results)
    {'accuracy': 1.0}
"""
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION      ,   _KWARGS_DESCRIPTION       )
class 		lowerCamelCase  (      datasets.Metric       ):



	def   _lowerCamelCase    (   self	):
			return datasets.MetricInfo(
			    description=_DESCRIPTION		,      citation=_CITATION		,      inputs_description=_KWARGS_DESCRIPTION		,      features=datasets.Features(
			        {
			            "predictions": datasets.Value("string"	),
			            "references": datasets.Value("string"	),
			        }	)		,      homepage="https://github.com/hendrycks/math"		,      codebase_urls=["https://github.com/hendrycks/math"]		,      )





	def   _lowerCamelCase    (   self		,      a_		,      a_	):
			lowerCAmelCase     :    Any						=   0.0
			for i, j in zip(a_		,      a_	):
					n_correct += 1.0 if math_equivalence.is_equiv(a_		,      a_	) else 0.0
			lowerCAmelCase     :    List[str]						=   n_correct / len(a_	)
			return {
			    "accuracy": accuracy,
			}



