


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     lowercase__  :






      @staticmethod
      def 			_UpperCAmelCase  (					*_lowercase      :					str		,						**_lowercase      :					Tuple ):




             """simple docstring"""







             pass





    def __UpperCAmelCase       (		__A			)						->					Optional[int]:



           '''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      = (
    "https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"
)


@is_pipeline_test
@require_torch
@require_vision
class     lowercase__  (					unittest.TestCase		):
  A__=  MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING






  @require_pytesseract
  @require_vision
  def 			_UpperCAmelCase  (					self      :					int		,						_lowercase      :					Tuple		,						_lowercase      :					Optional[int]		,						_lowercase      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     pipeline(
             "document-question-answering"		,						model=_lowercase		,						tokenizer=_lowercase		,						image_processor=_lowercase )

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






  def 			_UpperCAmelCase  (					self      :					Optional[int]		,						_lowercase      :					List[Any]		,						_lowercase      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     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 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         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": 38, "end": 39},
             {"score": 0.0_0_0_1, "answer": "oy 2312/2019 DUE", "start": 38, "end": 40},
         ]
         UpperCAmelCase__										=     dqa_pipeline(image=_lowercase		,						question=_lowercase		,						top_k=2 )
         self.assertEqual(nested_simplify(_lowercase		,						decimals=4 )		,						_lowercase )

         UpperCAmelCase__										=     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
         UpperCAmelCase__										=     "./tests/fixtures/tests_samples/COCO/000000039769.png"
         UpperCAmelCase__										=     dqa_pipeline(image=_lowercase		,						question=_lowercase		,						top_k=2 )
         self.assertEqual(_lowercase		,						[] )

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






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




         """simple docstring"""







         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=_lowercase		,						question=_lowercase		,						top_k=2 )
         self.assertEqual(
             nested_simplify(_lowercase		,						decimals=4 )		,						[
                 {"score": 0.9_9_4_4, "answer": "us-001", "start": 16, "end": 16},
                 {"score": 0.0_0_0_9, "answer": "us-001", "start": 16, "end": 16},
             ]		,						)

         UpperCAmelCase__										=     dqa_pipeline({"image": image, "question": question}		,						top_k=2 )
         self.assertEqual(
             nested_simplify(_lowercase		,						decimals=4 )		,						[
                 {"score": 0.9_9_4_4, "answer": "us-001", "start": 16, "end": 16},
                 {"score": 0.0_0_0_9, "answer": "us-001", "start": 16, "end": 16},
             ]		,						)

         UpperCAmelCase__										=     dqa_pipeline(
             [{"image": image, "question": question}, {"image": image, "question": question}]		,						top_k=2 )
         self.assertEqual(
             nested_simplify(_lowercase		,						decimals=4 )		,						[
                 [
                     {"score": 0.9_9_4_4, "answer": "us-001", "start": 16, "end": 16},
                     {"score": 0.0_0_0_9, "answer": "us-001", "start": 16, "end": 16},
                 ],
             ]
             * 2		,						)






  @slow
  @require_torch
  @require_detectrona
  @require_pytesseract
  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







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

         UpperCAmelCase__										=     dqa_pipeline(image=_lowercase		,						question=_lowercase		,						top_k=2 )
         self.assertEqual(
             nested_simplify(_lowercase		,						decimals=4 )		,						[
                 {"score": 0.9_9_7_4, "answer": "1110212019", "start": 23, "end": 23},
                 {"score": 0.9_9_4_8, "answer": "us-001", "start": 16, "end": 16},
             ]		,						)

         UpperCAmelCase__										=     dqa_pipeline({"image": image, "question": question}		,						top_k=2 )
         self.assertEqual(
             nested_simplify(_lowercase		,						decimals=4 )		,						[
                 {"score": 0.9_9_7_4, "answer": "1110212019", "start": 23, "end": 23},
                 {"score": 0.9_9_4_8, "answer": "us-001", "start": 16, "end": 16},
             ]		,						)

         UpperCAmelCase__										=     dqa_pipeline(
             [{"image": image, "question": question}, {"image": image, "question": question}]		,						top_k=2 )
         self.assertEqual(
             nested_simplify(_lowercase		,						decimals=4 )		,						[
                 [
                     {"score": 0.9_9_7_4, "answer": "1110212019", "start": 23, "end": 23},
                     {"score": 0.9_9_4_8, "answer": "us-001", "start": 16, "end": 16},
                 ]
             ]
             * 2		,						)






  @slow
  @require_torch
  @require_pytesseract
  @require_vision
  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







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

         UpperCAmelCase__										=     dqa_pipeline(image=_lowercase		,						question=_lowercase		,						top_k=2 )
         self.assertEqual(
             nested_simplify(_lowercase		,						decimals=4 )		,						[
                 {"score": 0.4_2_5_1, "answer": "us-001", "start": 16, "end": 16},
                 {"score": 0.0_8_1_9, "answer": "1110212019", "start": 23, "end": 23},
             ]		,						)

         UpperCAmelCase__										=     dqa_pipeline({"image": image, "question": question}		,						top_k=2 )
         self.assertEqual(
             nested_simplify(_lowercase		,						decimals=4 )		,						[
                 {"score": 0.4_2_5_1, "answer": "us-001", "start": 16, "end": 16},
                 {"score": 0.0_8_1_9, "answer": "1110212019", "start": 23, "end": 23},
             ]		,						)

         UpperCAmelCase__										=     dqa_pipeline(
             [{"image": image, "question": question}, {"image": image, "question": question}]		,						top_k=2 )
         self.assertEqual(
             nested_simplify(_lowercase		,						decimals=4 )		,						[
                 [
                     {"score": 0.4_2_5_1, "answer": "us-001", "start": 16, "end": 16},
                     {"score": 0.0_8_1_9, "answer": "1110212019", "start": 23, "end": 23},
                 ]
             ]
             * 2		,						)

         UpperCAmelCase__										=     list(zip(*apply_tesseract(load_image(_lowercase )		,						_lowercase		,						"" ) ) )

         # 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(_lowercase		,						decimals=4 )		,						[
                 {"score": 0.4_2_5_1, "answer": "us-001", "start": 16, "end": 16},
                 {"score": 0.0_8_1_9, "answer": "1110212019", "start": 23, "end": 23},
             ]		,						)






  @slow
  @require_torch
  @require_pytesseract
  @require_vision
  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







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

         UpperCAmelCase__										=     dqa_pipeline(image=_lowercase		,						question=_lowercase		,						top_k=2 )
         self.assertEqual(
             nested_simplify(_lowercase		,						decimals=4 )		,						[
                 {"score": 0.9_9_9_9, "answer": "us-001", "start": 16, "end": 16},
                 {"score": 0.9_9_9_8, "answer": "us-001", "start": 16, "end": 16},
             ]		,						)

         UpperCAmelCase__										=     dqa_pipeline(
             [{"image": image, "question": question}, {"image": image, "question": question}]		,						top_k=2 )
         self.assertEqual(
             nested_simplify(_lowercase		,						decimals=4 )		,						[
                 [
                     {"score": 0.9_9_9_9, "answer": "us-001", "start": 16, "end": 16},
                     {"score": 0.9_9_9_8, "answer": "us-001", "start": 16, "end": 16},
                 ]
             ]
             * 2		,						)

         UpperCAmelCase__										=     list(zip(*apply_tesseract(load_image(_lowercase )		,						_lowercase		,						"" ) ) )

         # 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(_lowercase		,						decimals=4 )		,						[
                 {"score": 0.9_9_9_9, "answer": "us-001", "start": 16, "end": 16},
                 {"score": 0.9_9_9_8, "answer": "us-001", "start": 16, "end": 16},
             ]		,						)






  @slow
  @require_torch
  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         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=_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 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         pass









from __future__ import annotations

import math





def __UpperCAmelCase       (		__A ,						__A			)						->					float:



       '''simple docstring'''





       UpperCAmelCase__										=     u
       for i in range(1 ,						__A			):
              UpperCAmelCase__										=     temp * (u - i)
       return temp





def __UpperCAmelCase       (		)						->					None:



       '''simple docstring'''





       UpperCAmelCase__										=     int(input("enter the numbers of values: "			)			)
       UpperCAmelCase__										=     []
       for _ in range(__A			):
              y.append([]			)
       for i in range(__A			):
              for j in range(__A			):
                     y[i].append(__A			)
                     UpperCAmelCase__										=     0

       print("enter the values of parameters in a list: "			)
       UpperCAmelCase__										=     list(map(__A ,						input().split()			)			)

       print("enter the values of corresponding parameters: "			)
       for i in range(__A			):
              UpperCAmelCase__										=     float(input()			)

       UpperCAmelCase__										=     int(input("enter the value to interpolate: "			)			)
       UpperCAmelCase__										=     (value - x[0]) / (x[1] - x[0])

       # for calculating forward difference table

       for i in range(1 ,						__A			):
              for j in range(n - i			):
                     UpperCAmelCase__										=     y[j + 1][i - 1] - y[j][i - 1]

       UpperCAmelCase__										=     y[0][0]
       for i in range(1 ,						__A			):
              summ += (ucal(__A ,						__A			) * y[0][i]) / math.factorial(__A			)

       print(F"""the value at {value} is {summ}"""			)


if __name__ == "__main__":
    main()





