






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 			__a  :







  @staticmethod
  def 					__lowercase					(		*SCREAMING_SNAKE_CASE					:   Optional[int]							,   **SCREAMING_SNAKE_CASE					:   Tuple						):


       '''simple docstring'''






       pass







 def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Tuple:
      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.
lowerCamelCase	:							Any				=(
    '''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'''
)





@is_pipeline_test
@require_torch
@require_vision
class 			__a  (    unittest.TestCase       ):
 _lowerCAmelCase       :       str        = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING







 @require_pytesseract
 @require_vision
 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Dict							,   SCREAMING_SNAKE_CASE					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Optional[int]						):


      '''simple docstring'''






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

      UpperCamelCase__			:   Dict       				=  INVOICE_URL
      UpperCamelCase__			:   Union[str, Any]       				=  list(zip(*apply_tesseract(load_image(SCREAMING_SNAKE_CASE						)							,   SCREAMING_SNAKE_CASE							,   ""						)						)						)
      UpperCamelCase__			:   Optional[Any]       				=  "What is the placebo?"
      UpperCamelCase__			:   List[Any]       				=  [
          {
              "image": load_image(SCREAMING_SNAKE_CASE						),
              "question": question,
          },
          {
              "image": image,
              "question": question,
          },
          {
              "image": image,
              "question": question,
              "word_boxes": word_boxes,
          },
      ]
      return dqa_pipeline, examples







 def 					__lowercase					(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  dqa_pipeline(SCREAMING_SNAKE_CASE							,   top_k=2						)
      self.assertEqual(
          SCREAMING_SNAKE_CASE							,   [
              [
                  {"score": ANY(SCREAMING_SNAKE_CASE						), "answer": ANY(SCREAMING_SNAKE_CASE						), "start": ANY(SCREAMING_SNAKE_CASE						), "end": ANY(SCREAMING_SNAKE_CASE						)},
                  {"score": ANY(SCREAMING_SNAKE_CASE						), "answer": ANY(SCREAMING_SNAKE_CASE						), "start": ANY(SCREAMING_SNAKE_CASE						), "end": ANY(SCREAMING_SNAKE_CASE						)},
              ]
          ]
          * 3							,   )







 @require_torch
 @require_detectrona
 @require_pytesseract
 def 					__lowercase					(		self					:   Optional[int]						):


      '''simple docstring'''






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

      UpperCamelCase__			:   Optional[Any]       				=  [
          {"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__			:   Any       				=  dqa_pipeline(image=SCREAMING_SNAKE_CASE							,   question=SCREAMING_SNAKE_CASE							,   top_k=2						)
      self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE							,   decimals=4						)							,   SCREAMING_SNAKE_CASE						)

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

      # This image does not detect ANY text in it, meaning layoutlmv2 should fail.
      # Empty answer probably
      UpperCamelCase__			:   Union[str, Any]       				=  "./tests/fixtures/tests_samples/COCO/000000039769.png"
      UpperCamelCase__			:   List[str]       				=  dqa_pipeline(image=SCREAMING_SNAKE_CASE							,   question=SCREAMING_SNAKE_CASE							,   top_k=2						)
      self.assertEqual(SCREAMING_SNAKE_CASE							,   []						)

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







 @slow
 @require_torch
 @require_detectrona
 @require_pytesseract
 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  pipeline(
          "document-question-answering"							,   model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"							,   revision="9977165"							,   )
      UpperCamelCase__			:   Tuple       				=  INVOICE_URL
      UpperCamelCase__			:   Optional[int]       				=  "What is the invoice number?"

      UpperCamelCase__			:   Dict       				=  dqa_pipeline(image=SCREAMING_SNAKE_CASE							,   question=SCREAMING_SNAKE_CASE							,   top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,   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__			:   Any       				=  dqa_pipeline({"image": image, "question": question}							,   top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,   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__			:   str       				=  dqa_pipeline(
          [{"image": image, "question": question}, {"image": image, "question": question}]							,   top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,   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 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






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

      UpperCamelCase__			:   str       				=  dqa_pipeline(image=SCREAMING_SNAKE_CASE							,   question=SCREAMING_SNAKE_CASE							,   top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,   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__			:   Any       				=  dqa_pipeline({"image": image, "question": question}							,   top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,   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__			:   List[str]       				=  dqa_pipeline(
          [{"image": image, "question": question}, {"image": image, "question": question}]							,   top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,   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 					__lowercase					(		self					:   Union[str, Any]						):


      '''simple docstring'''






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

      UpperCamelCase__			:   Tuple       				=  dqa_pipeline(image=SCREAMING_SNAKE_CASE							,   question=SCREAMING_SNAKE_CASE							,   top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,   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__			:   str       				=  dqa_pipeline({"image": image, "question": question}							,   top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,   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__			:   List[Any]       				=  dqa_pipeline(
          [{"image": image, "question": question}, {"image": image, "question": question}]							,   top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,   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__			:   Dict       				=  list(zip(*apply_tesseract(load_image(SCREAMING_SNAKE_CASE						)							,   SCREAMING_SNAKE_CASE							,   ""						)						)						)

      # This model should also work if `image` is set to None
      UpperCamelCase__			:   Any       				=  dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}							,   top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,   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 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






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

      UpperCamelCase__			:   Optional[Any]       				=  dqa_pipeline(image=SCREAMING_SNAKE_CASE							,   question=SCREAMING_SNAKE_CASE							,   top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,   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__			:   List[str]       				=  dqa_pipeline(
          [{"image": image, "question": question}, {"image": image, "question": question}]							,   top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,   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[str]       				=  list(zip(*apply_tesseract(load_image(SCREAMING_SNAKE_CASE						)							,   SCREAMING_SNAKE_CASE							,   ""						)						)						)

      # This model should also work if `image` is set to None
      UpperCamelCase__			:   Tuple       				=  dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}							,   top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,   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 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[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"							,   )

      UpperCamelCase__			:   List[str]       				=  INVOICE_URL
      UpperCamelCase__			:   List[Any]       				=  "What is the invoice number?"
      UpperCamelCase__			:   Optional[Any]       				=  dqa_pipeline(image=SCREAMING_SNAKE_CASE							,   question=SCREAMING_SNAKE_CASE							,   top_k=2						)
      self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE							,   decimals=4						)							,   [{"answer": "us-001"}]						)







 @require_tf
 @unittest.skip("Document question answering not implemented in TF"						)
 def 					__lowercase					(		self					:   Optional[int]						):


      '''simple docstring'''






      pass







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase	:							Tuple				={
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase	:							Union[str, Any]				=[
     '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LukeForEntityClassification''',
     '''LukeForEntityPairClassification''',
     '''LukeForEntitySpanClassification''',
     '''LukeForMultipleChoice''',
     '''LukeForQuestionAnswering''',
     '''LukeForSequenceClassification''',
     '''LukeForTokenClassification''',
     '''LukeForMaskedLM''',
     '''LukeModel''',
     '''LukePreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
 from .tokenization_luke import LukeTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_luke import (
      LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
      LukeForEntityClassification,
      LukeForEntityPairClassification,
      LukeForEntitySpanClassification,
      LukeForMaskedLM,
      LukeForMultipleChoice,
      LukeForQuestionAnswering,
      LukeForSequenceClassification,
      LukeForTokenClassification,
      LukeModel,
      LukePreTrainedModel,
  )

else:
 import sys

 lowerCamelCase	:							Dict				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)