



import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING





def 		a__			(		_UpperCamelCase			: int							):
   return 1.0 / (1.0 + np.exp(-_outputs							))





def 		a__			(		_UpperCamelCase			: Dict							):
   __lowerCamelCase													=    np.max(_outputs ,axis=-1 ,keepdims=_lowercase							)
   __lowerCamelCase													=    np.exp(_outputs - maxes							)
   return shifted_exp / shifted_exp.sum(axis=-1 ,keepdims=_lowercase							)



class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  """sigmoid"""
 lowerCAmelCase__       					=  """softmax"""
 lowerCAmelCase__       					=  """none"""



@add_end_docstrings(
    lowerCAmelCase__  ,	r"""
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `\"default\"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `\"sigmoid\"`: Applies the sigmoid function on the output.
            - `\"softmax\"`: Applies the softmax function on the output.
            - `\"none\"`: Does not apply any function on the output.
    """  ,	)
class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  False
 lowerCAmelCase__       					=  ClassificationFunction.NONE




 def __init__(						self	,				**__UpperCAmelCase				):



    '''simple docstring'''
    super().__init__(**UpperCamelCase_				)

    self.check_model_type(
        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
        if self.framework == '''tf'''
        else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase=None	,				__UpperCAmelCase=None	,				__UpperCAmelCase=""	,				**__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    tokenizer_kwargs

    __lowerCamelCase													=    {}
    if hasattr(self.model.config	,				'''return_all_scores'''				) and return_all_scores is None:
       __lowerCamelCase													=    self.model.config.return_all_scores

    if isinstance(UpperCamelCase_	,				UpperCamelCase_				) or top_k is None:
       __lowerCamelCase													=    top_k
       __lowerCamelCase													=    False
    elif return_all_scores is not None:
       warnings.warn(
           '''`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'''
           ''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'''	,				UpperCamelCase_	,				)
       if return_all_scores:
          __lowerCamelCase													=    None
       else:
          __lowerCamelCase													=    1

    if isinstance(UpperCamelCase_	,				UpperCamelCase_				):
       __lowerCamelCase													=    ClassificationFunction[function_to_apply.upper()]

    if function_to_apply is not None:
       __lowerCamelCase													=    function_to_apply
    return preprocess_params, {}, postprocess_params




 def __call__(						self	,				*__UpperCAmelCase	,				**__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    super().__call__(*UpperCamelCase_	,				**UpperCamelCase_				)
    # TODO try and retrieve it in a nicer way from _sanitize_parameters.
    __lowerCamelCase													=    'top_k' not in kwargs
    if isinstance(args[0]	,				UpperCamelCase_				) and _legacy:
       # This pipeline is odd, and return a list when single item is run
       return [result]
    else:
       return result




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				**__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    self.framework
    if isinstance(UpperCamelCase_	,				UpperCamelCase_				):
       return self.tokenizer(**UpperCamelCase_	,				return_tensors=UpperCamelCase_	,				**UpperCamelCase_				)
    elif isinstance(UpperCamelCase_	,				UpperCamelCase_				) and len(UpperCamelCase_				) == 1 and isinstance(inputs[0]	,				UpperCamelCase_				) and len(inputs[0]				) == 2:
       # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
       return self.tokenizer(
           text=inputs[0][0]	,				text_pair=inputs[0][1]	,				return_tensors=UpperCamelCase_	,				**UpperCamelCase_				)
    elif isinstance(UpperCamelCase_	,				UpperCamelCase_				):
       # This is likely an invalid usage of the pipeline attempting to pass text pairs.
       raise ValueError(
           '''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
           ''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'''				)
    return self.tokenizer(UpperCamelCase_	,				return_tensors=UpperCamelCase_	,				**UpperCamelCase_				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    return self.model(**UpperCamelCase_				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase=None	,				__UpperCAmelCase=1	,				__UpperCAmelCase=True				):



    '''simple docstring'''





    if function_to_apply is None:
       if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
          __lowerCamelCase													=    ClassificationFunction.SIGMOID
       elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
          __lowerCamelCase													=    ClassificationFunction.SOFTMAX
       elif hasattr(self.model.config	,				'''function_to_apply'''				) and function_to_apply is None:
          __lowerCamelCase													=    self.model.config.function_to_apply
       else:
          __lowerCamelCase													=    ClassificationFunction.NONE

    __lowerCamelCase													=    model_outputs['logits'][0]
    __lowerCamelCase													=    outputs.numpy()

    if function_to_apply == ClassificationFunction.SIGMOID:
       __lowerCamelCase													=    sigmoid(UpperCamelCase_				)
    elif function_to_apply == ClassificationFunction.SOFTMAX:
       __lowerCamelCase													=    softmax(UpperCamelCase_				)
    elif function_to_apply == ClassificationFunction.NONE:
       __lowerCamelCase													=    outputs
    else:
       raise ValueError(F"""Unrecognized `function_to_apply` argument: {function_to_apply}"""				)

    if top_k == 1 and _legacy:
       return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

    __lowerCamelCase													=    [
        {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(UpperCamelCase_				)
    ]
    if not _legacy:
       dict_scores.sort(key=lambda __UpperCAmelCase				: x["score"]	,				reverse=UpperCamelCase_				)
       if top_k is not None:
          __lowerCamelCase													=    dict_scores[:top_k]
    return dict_scores











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      (						*__UpperCAmelCase	,				**__UpperCAmelCase				):



      '''simple docstring'''
      pass





  def 		a__			(		_UpperCamelCase			: List[str]							):
     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  __lowerCAmelCase     (	unittest.TestCase   ):
 lowerCAmelCase__       					=  MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING




 @require_pytesseract
 @require_vision
 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    pipeline(
        '''document-question-answering'''	,				model=__UpperCAmelCase	,				tokenizer=__UpperCAmelCase	,				image_processor=__UpperCAmelCase				)

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




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    dqa_pipeline(__UpperCAmelCase	,				top_k=2				)
    self.assertEqual(
        __UpperCAmelCase	,				[
            [
                {'''score''': ANY(__UpperCAmelCase				), '''answer''': ANY(__UpperCAmelCase				), '''start''': ANY(__UpperCAmelCase				), '''end''': ANY(__UpperCAmelCase				)},
                {'''score''': ANY(__UpperCAmelCase				), '''answer''': ANY(__UpperCAmelCase				), '''start''': ANY(__UpperCAmelCase				), '''end''': ANY(__UpperCAmelCase				)},
            ]
        ]
        * 3	,				)




 @require_torch
 @require_detectrona
 @require_pytesseract
 def        lowerCamelCase      (						self				):



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

    __lowerCamelCase													=    [
        {'''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},
    ]
    __lowerCamelCase													=    dqa_pipeline(image=__UpperCAmelCase	,				question=__UpperCAmelCase	,				top_k=2				)
    self.assertEqual(nested_simplify(__UpperCAmelCase	,				decimals=4				)	,				__UpperCAmelCase				)

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

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

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




 @slow
 @require_torch
 @require_detectrona
 @require_pytesseract
 def        lowerCamelCase      (						self				):



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

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

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

    __lowerCamelCase													=    dqa_pipeline(
        [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]	,				top_k=2				)
    self.assertEqual(
        nested_simplify(__UpperCAmelCase	,				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        lowerCamelCase      (						self				):



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

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

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

    __lowerCamelCase													=    dqa_pipeline(
        [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]	,				top_k=2				)
    self.assertEqual(
        nested_simplify(__UpperCAmelCase	,				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        lowerCamelCase      (						self				):



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

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

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

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

    __lowerCamelCase													=    list(zip(*apply_tesseract(load_image(__UpperCAmelCase				)	,				__UpperCAmelCase	,				''''''				)				)				)

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



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

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

    __lowerCamelCase													=    dqa_pipeline(
        [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]	,				top_k=2				)
    self.assertEqual(
        nested_simplify(__UpperCAmelCase	,				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	,				)

    __lowerCamelCase													=    list(zip(*apply_tesseract(load_image(__UpperCAmelCase				)	,				__UpperCAmelCase	,				''''''				)				)				)

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



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




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



    '''simple docstring'''
    pass






