





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			:



           """simple docstring"""






           @staticmethod
           def     UpperCAmelCase		(			*UpperCAmelCase					,    **UpperCAmelCase )							-> Optional[int]:






             '''simple docstring'''






             pass







     def    lowerCAmelCase__(						lowercase							:     Tuple					)			->     List[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.
_UpperCamelCase							=			(
    'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)




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



      """simple docstring"""






      UpperCAmelCase_    :   Optional[int]      =MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING







      @require_pytesseract
      @require_vision
      def     UpperCAmelCase		(			self					,    UpperCAmelCase					,    UpperCAmelCase					,    UpperCAmelCase )							-> Optional[Any]:






        '''simple docstring'''






        __snake_case :					List[str]          =							pipeline(
            "document-question-answering"					,    model=__lowercase					,    tokenizer=__lowercase					,    image_processor=__lowercase )

        __snake_case :					str          =							INVOICE_URL
        __snake_case :					int          =							list(zip(*apply_tesseract(load_image(__lowercase )					,    __lowercase					,    "" ) ) )
        __snake_case :					Optional[int]          =							'What is the placebo?'
        __snake_case :					List[str]          =							[
            {
                '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					,    UpperCAmelCase					,    UpperCAmelCase )							-> Tuple:






        '''simple docstring'''






        __snake_case :					int          =							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 )							-> Optional[int]:






        '''simple docstring'''






        __snake_case :					Dict          =							pipeline("document-question-answering"					,    model="hf-internal-testing/tiny-random-layoutlmv2" )
        __snake_case :					Optional[int]          =							INVOICE_URL
        __snake_case :					List[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 :					int          =							dqa_pipeline(image=__lowercase					,    question=__lowercase					,    top_k=2 )
        self.assertEqual(nested_simplify(__lowercase					,    decimals=4 )					,    __lowercase )

        __snake_case :					Union[str, Any]          =							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 :					int          =							'./tests/fixtures/tests_samples/COCO/000000039769.png'
        __snake_case :					Tuple          =							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 :					List[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     UpperCAmelCase		(			self )							-> Union[str, Any]:






        '''simple docstring'''






        __snake_case :					Dict          =							pipeline(
            "document-question-answering"					,    model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"					,    revision="9977165"					,    )
        __snake_case :					Optional[Any]          =							INVOICE_URL
        __snake_case :					int          =							'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": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
                {"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
            ]					,    )

        __snake_case :					Tuple          =							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 :					Dict          =							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     UpperCAmelCase		(			self )							-> Optional[int]:






        '''simple docstring'''






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

        __snake_case :					Union[str, Any]          =							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 :					Union[str, Any]          =							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 :					Union[str, Any]          =							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     UpperCAmelCase		(			self )							-> List[str]:






        '''simple docstring'''






        __snake_case :					Tuple          =							AutoTokenizer.from_pretrained(
            "impira/layoutlm-document-qa"					,    revision="3dc6de3"					,    add_prefix_space=__lowercase )
        __snake_case :					Dict          =							pipeline(
            "document-question-answering"					,    model="impira/layoutlm-document-qa"					,    tokenizer=__lowercase					,    revision="3dc6de3"					,    )
        __snake_case :					Any          =							INVOICE_URL
        __snake_case :					Any          =							'What is the invoice number?'

        __snake_case :					List[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 :					List[Any]          =							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     UpperCAmelCase		(			self )							-> List[Any]:






        '''simple docstring'''






        __snake_case :					Optional[Any]          =							AutoTokenizer.from_pretrained(
            "impira/layoutlm-document-qa"					,    revision="3dc6de3"					,    add_prefix_space=__lowercase )
        __snake_case :					int          =							pipeline(
            "document-question-answering"					,    model="impira/layoutlm-document-qa"					,    tokenizer=__lowercase					,    revision="3dc6de3"					,    max_seq_len=50					,    )
        __snake_case :					Optional[int]          =							INVOICE_URL
        __snake_case :					List[Any]          =							'What is the invoice number?'

        __snake_case :					List[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 :					Optional[int]          =							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 :					Dict          =							list(zip(*apply_tesseract(load_image(__lowercase )					,    __lowercase					,    "" ) ) )

        # This model should also work if `image` is set to None
        __snake_case :					Union[str, 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     UpperCAmelCase		(			self )							-> Optional[Any]:






        '''simple docstring'''






        __snake_case :					int          =							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 :					str          =							INVOICE_URL
        __snake_case :					List[str]          =							'What is the invoice number?'
        __snake_case :					Union[str, Any]          =							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 )							-> Union[str, Any]:






        '''simple docstring'''






        pass





'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
 from PIL import Image

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		int     				=    '''dandelin/vilt-b32-finetuned-vqa'''
 __lowercase					:		int     				=    (
     '''This is a tool that answers a question about an image. It takes an input named `image` which should be the '''
     '''image containing the information, as well as a `question` which should be the question in English. It '''
     '''returns a text that is the answer to the question.'''
 )
 __lowercase					:		Union[str, Any]     				=    '''image_qa'''
 __lowercase					:		Optional[Any]     				=    AutoProcessor
 __lowercase					:		Tuple     				=    AutoModelForVisualQuestionAnswering

 __lowercase					:		Tuple     				=    ['''image''', '''text''']
 __lowercase					:		List[str]     				=    ['''text''']


 def __init__(   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  requires_backends(self   ,   ['vision']    )
  super().__init__(*__lowercase   ,   **__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  return self.pre_processor(__lowercase   ,   __lowercase   ,   return_tensors='pt'    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  with torch.no_grad():
   return self.model(**__lowercase    ).logits







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		List[str]	  =      outputs.argmax(-1    ).item()
  return self.model.config.idalabel[idx]






