



from __future__ import annotations

import unittest

from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TFFunnelBaseModel,
      TFFunnelForMaskedLM,
      TFFunnelForMultipleChoice,
      TFFunnelForPreTraining,
      TFFunnelForQuestionAnswering,
      TFFunnelForSequenceClassification,
      TFFunnelForTokenClassification,
      TFFunnelModel,
  )



class  __lowerCAmelCase     :




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase=13	,				__UpperCAmelCase=7	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=99	,				__UpperCAmelCase=[1, 1, 2]	,				__UpperCAmelCase=1	,				__UpperCAmelCase=32	,				__UpperCAmelCase=4	,				__UpperCAmelCase=8	,				__UpperCAmelCase=37	,				__UpperCAmelCase="gelu_new"	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=0.0	,				__UpperCAmelCase=512	,				__UpperCAmelCase=3	,				__UpperCAmelCase=0.02	,				__UpperCAmelCase=3	,				__UpperCAmelCase=4	,				__UpperCAmelCase=None	,				__UpperCAmelCase=False	,				):



    '''simple docstring'''
    __lowerCamelCase													=    parent
    __lowerCamelCase													=    batch_size
    __lowerCamelCase													=    seq_length
    __lowerCamelCase													=    is_training
    __lowerCamelCase													=    use_input_mask
    __lowerCamelCase													=    use_token_type_ids
    __lowerCamelCase													=    use_labels
    __lowerCamelCase													=    vocab_size
    __lowerCamelCase													=    block_sizes
    __lowerCamelCase													=    num_decoder_layers
    __lowerCamelCase													=    d_model
    __lowerCamelCase													=    n_head
    __lowerCamelCase													=    d_head
    __lowerCamelCase													=    d_inner
    __lowerCamelCase													=    hidden_act
    __lowerCamelCase													=    hidden_dropout
    __lowerCamelCase													=    attention_dropout
    __lowerCamelCase													=    activation_dropout
    __lowerCamelCase													=    max_position_embeddings
    __lowerCamelCase													=    type_vocab_size
    __lowerCamelCase													=    2
    __lowerCamelCase													=    num_labels
    __lowerCamelCase													=    num_choices
    __lowerCamelCase													=    scope
    __lowerCamelCase													=    initializer_std

    # Used in the tests to check the size of the first attention layer
    __lowerCamelCase													=    n_head
    # Used in the tests to check the size of the first hidden state
    __lowerCamelCase													=    self.d_model
    # Used in the tests to check the number of output hidden states/attentions
    __lowerCamelCase													=    sum(self.block_sizes				) + (0 if base else self.num_decoder_layers)
    # FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
    # the last hidden state of the first block (which is the first hidden state of the decoder).
    if not base:
       __lowerCamelCase													=    self.num_hidden_layers + 2




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size				)

    __lowerCamelCase													=    None
    if self.use_input_mask:
       __lowerCamelCase													=    random_attention_mask([self.batch_size, self.seq_length]				)

    __lowerCamelCase													=    None
    if self.use_token_type_ids:
       __lowerCamelCase													=    ids_tensor([self.batch_size, self.seq_length]	,				self.type_vocab_size				)

    __lowerCamelCase													=    None
    __lowerCamelCase													=    None
    __lowerCamelCase													=    None
    if self.use_labels:
       __lowerCamelCase													=    ids_tensor([self.batch_size]	,				self.type_sequence_label_size				)
       __lowerCamelCase													=    ids_tensor([self.batch_size, self.seq_length]	,				self.num_labels				)
       __lowerCamelCase													=    ids_tensor([self.batch_size]	,				self.num_choices				)

    __lowerCamelCase													=    FunnelConfig(
        vocab_size=self.vocab_size	,				block_sizes=self.block_sizes	,				num_decoder_layers=self.num_decoder_layers	,				d_model=self.d_model	,				n_head=self.n_head	,				d_head=self.d_head	,				d_inner=self.d_inner	,				hidden_act=self.hidden_act	,				hidden_dropout=self.hidden_dropout	,				attention_dropout=self.attention_dropout	,				activation_dropout=self.activation_dropout	,				max_position_embeddings=self.max_position_embeddings	,				type_vocab_size=self.type_vocab_size	,				initializer_std=self.initializer_std	,				)

    return (
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    )




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



    '''simple docstring'''
    __lowerCamelCase													=    TFFunnelModel(config=__UpperCAmelCase				)
    __lowerCamelCase													=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
    __lowerCamelCase													=    model(__UpperCAmelCase				)

    __lowerCamelCase													=    [input_ids, input_mask]
    __lowerCamelCase													=    model(__UpperCAmelCase				)

    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.d_model)				)

    __lowerCamelCase													=    False
    __lowerCamelCase													=    TFFunnelModel(config=__UpperCAmelCase				)
    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.d_model)				)

    __lowerCamelCase													=    False
    __lowerCamelCase													=    TFFunnelModel(config=__UpperCAmelCase				)
    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.d_model)				)




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



    '''simple docstring'''
    __lowerCamelCase													=    TFFunnelBaseModel(config=__UpperCAmelCase				)
    __lowerCamelCase													=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
    __lowerCamelCase													=    model(__UpperCAmelCase				)

    __lowerCamelCase													=    [input_ids, input_mask]
    __lowerCamelCase													=    model(__UpperCAmelCase				)

    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, 2, self.d_model)				)

    __lowerCamelCase													=    False
    __lowerCamelCase													=    TFFunnelBaseModel(config=__UpperCAmelCase				)
    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, 3, self.d_model)				)

    __lowerCamelCase													=    False
    __lowerCamelCase													=    TFFunnelBaseModel(config=__UpperCAmelCase				)
    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, 2, self.d_model)				)




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



    '''simple docstring'''
    __lowerCamelCase													=    TFFunnelForPreTraining(config=__UpperCAmelCase				)
    __lowerCamelCase													=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length)				)




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



    '''simple docstring'''
    __lowerCamelCase													=    TFFunnelForMaskedLM(config=__UpperCAmelCase				)
    __lowerCamelCase													=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.vocab_size)				)




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



    '''simple docstring'''
    __lowerCamelCase													=    self.num_labels
    __lowerCamelCase													=    TFFunnelForSequenceClassification(config=__UpperCAmelCase				)
    __lowerCamelCase													=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.num_labels)				)




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



    '''simple docstring'''
    __lowerCamelCase													=    self.num_choices
    __lowerCamelCase													=    TFFunnelForMultipleChoice(config=__UpperCAmelCase				)
    __lowerCamelCase													=    tf.tile(tf.expand_dims(__UpperCAmelCase	,				1				)	,				(1, self.num_choices, 1)				)
    __lowerCamelCase													=    tf.tile(tf.expand_dims(__UpperCAmelCase	,				1				)	,				(1, self.num_choices, 1)				)
    __lowerCamelCase													=    tf.tile(tf.expand_dims(__UpperCAmelCase	,				1				)	,				(1, self.num_choices, 1)				)
    __lowerCamelCase													=    {
        '''input_ids''': multiple_choice_inputs_ids,
        '''attention_mask''': multiple_choice_input_mask,
        '''token_type_ids''': multiple_choice_token_type_ids,
    }
    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.num_choices)				)




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



    '''simple docstring'''
    __lowerCamelCase													=    self.num_labels
    __lowerCamelCase													=    TFFunnelForTokenClassification(config=__UpperCAmelCase				)
    __lowerCamelCase													=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.num_labels)				)




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



    '''simple docstring'''
    __lowerCamelCase													=    TFFunnelForQuestionAnswering(config=__UpperCAmelCase				)
    __lowerCamelCase													=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.start_logits.shape	,				(self.batch_size, self.seq_length)				)
    self.parent.assertEqual(result.end_logits.shape	,				(self.batch_size, self.seq_length)				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.prepare_config_and_inputs()
    (
        (
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,
    )													=    config_and_inputs
    __lowerCamelCase													=    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
    return config, inputs_dict



@require_tf
class  __lowerCAmelCase     (	lowerCAmelCase__  ,	lowerCAmelCase__  ,	unittest.TestCase   ):
 lowerCAmelCase__       					=  (
     (
         TFFunnelModel,
         TFFunnelForMaskedLM,
         TFFunnelForPreTraining,
         TFFunnelForQuestionAnswering,
         TFFunnelForTokenClassification,
     )
     if is_tf_available()
     else ()
 )
 lowerCAmelCase__       					=  (
     {
         """feature-extraction""": (TFFunnelBaseModel, TFFunnelModel),
         """fill-mask""": TFFunnelForMaskedLM,
         """question-answering""": TFFunnelForQuestionAnswering,
         """text-classification""": TFFunnelForSequenceClassification,
         """token-classification""": TFFunnelForTokenClassification,
         """zero-shot""": TFFunnelForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 lowerCAmelCase__       					=  False
 lowerCAmelCase__       					=  False




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    TFFunnelModelTester(self				)
    __lowerCamelCase													=    ConfigTester(self	,				config_class=__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    self.config_tester.run_common_tests()




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_pretraining(*__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase				)



@require_tf
class  __lowerCAmelCase     (	lowerCAmelCase__  ,	unittest.TestCase   ):
 lowerCAmelCase__       					=  (
     (TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
 )
 lowerCAmelCase__       					=  False
 lowerCAmelCase__       					=  False




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    TFFunnelModelTester(self	,				base=__UpperCAmelCase				)
    __lowerCamelCase													=    ConfigTester(self	,				config_class=__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    self.config_tester.run_common_tests()




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_base_model(*__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase				)











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






