
'''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    UpperCAmelCase_				:





   @staticmethod
   def        snake_case__		(    *lowercase_ ,       **lowercase_):
       pass


  def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
      """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    UpperCAmelCase_				(	unittest.TestCase					):
 UpperCAmelCase_		    =						MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING





 @require_pytesseract
 @require_vision
 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_):
     snake_case_ :    str     			=						pipeline(
         "document-question-answering" ,       model=lowercase_ ,       tokenizer=lowercase_ ,       image_processor=lowercase_)

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





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_):
     snake_case_ :    Any     			=						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        snake_case__		(    self):
     snake_case_ :    Tuple     			=						pipeline("document-question-answering" ,       model="hf-internal-testing/tiny-random-layoutlmv2")
     snake_case_ :    Optional[int]     			=						INVOICE_URL
     snake_case_ :    Union[str, Any]     			=						"How many cats are there?"

     snake_case_ :    Union[str, Any]     			=						[
         {"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},
     ]
     snake_case_ :    Union[str, Any]     			=						dqa_pipeline(image=lowercase_ ,       question=lowercase_ ,       top_k=2)
     self.assertEqual(nested_simplify(lowercase_ ,       decimals=4) ,       lowercase_)

     snake_case_ :    Tuple     			=						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_ :    str     			=						"./tests/fixtures/tests_samples/COCO/000000039769.png"
     snake_case_ :    List[str]     			=						dqa_pipeline(image=lowercase_ ,       question=lowercase_ ,       top_k=2)
     self.assertEqual(lowercase_ ,       [])

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





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

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

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

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

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

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

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

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

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

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

     snake_case_ :    int     			=						list(zip(*apply_tesseract(load_image(lowercase_) ,       lowercase_ ,       "")))

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

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

     snake_case_ :    Any     			=						dqa_pipeline(
         [{"image": image, "question": question}, {"image": image, "question": question}] ,       top_k=2)
     self.assertEqual(
         nested_simplify(lowercase_ ,       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 ,       )

     snake_case_ :    Tuple     			=						list(zip(*apply_tesseract(load_image(lowercase_) ,       lowercase_ ,       "")))

     # This model should also work if `image` is set to None
     snake_case_ :    Any     			=						dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} ,       top_k=2)
     self.assertEqual(
         nested_simplify(lowercase_ ,       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        snake_case__		(    self):
     snake_case_ :    Dict     			=						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_ :    Tuple     			=						INVOICE_URL
     snake_case_ :    Any     			=						"What is the invoice number?"
     snake_case_ :    int     			=						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        snake_case__		(    self):
     pass






'''simple docstring'''
import doctest
from collections import deque

import numpy as np






class    UpperCAmelCase_				:





 def __init__(    self):
     snake_case_ :    List[Any]     			=						[2, 1, 2, -1]
     snake_case_ :    int     			=						[1, 2, 3, 4]





 def        snake_case__		(    self):
     snake_case_ :    str     			=						len(self.first_signal)
     snake_case_ :    Any     			=						len(self.second_signal)

     snake_case_ :    List[Any]     			=						max(lowercase_ ,       lowercase_)

     # create a zero matrix of max_length x max_length
     snake_case_ :    Dict     			=						[[0] * max_length for i in range(lowercase_)]

     # fills the smaller signal with zeros to make both signals of same length
     if length_first_signal < length_second_signal:
         self.first_signal += [0] * (max_length - length_first_signal)
     elif length_first_signal > length_second_signal:
         self.second_signal += [0] * (max_length - length_second_signal)
     for i in range(lowercase_):
         snake_case_ :    List[str]     			=						deque(self.second_signal)
         rotated_signal.rotate(lowercase_)
         for j, item in enumerate(lowercase_):
             matrix[i][j] += item

        # multiply the matrix with the first signal
     snake_case_ :    Any     			=						np.matmul(np.transpose(lowercase_) ,       np.transpose(self.first_signal))

     # rounding-off to two decimal places
     return [round(lowercase_ ,       2) for i in final_signal]


if __name__ == "__main__":
  doctest.testmod()




