





from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_					:   Optional[int] 							=						{"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Union[str, Any] 							=						[
          """FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """FocalNetForImageClassification""",
          """FocalNetForMaskedImageModeling""",
          """FocalNetBackbone""",
          """FocalNetModel""",
          """FocalNetPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_focalnet import (
                FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                FocalNetBackbone,
                FocalNetForImageClassification,
                FocalNetForMaskedImageModeling,
                FocalNetModel,
                FocalNetPreTrainedModel,
            )

else:
      import sys

      UpperCAmelCase_					:   int 							=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)












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      lowercase__    :







        @staticmethod
        def 	UpperCAmelCase__		(	*_lowercase    ,						**_lowercase						):
             pass






      def 							_lowerCAmelCase (  _a   :					List[str]			)	->			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_					:   Optional[Any] 							=						(
    """https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"""
)





@is_pipeline_test
@require_torch
@require_vision
class      lowercase__    (							unittest.TestCase  ):
  __UpperCamelCase	     =  MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING







  @require_pytesseract
  @require_vision
  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase						):
       lowerCAmelCase_					:       Optional[int]    =		pipeline(
           """document-question-answering"""    ,						model=_lowercase    ,						tokenizer=_lowercase    ,						image_processor=_lowercase						)

       lowerCAmelCase_					:       List[str]    =		INVOICE_URL
       lowerCAmelCase_					:       str    =		list(zip(*apply_tesseract(load_image(_lowercase						)    ,						_lowercase    ,						""""""						)						)						)
       lowerCAmelCase_					:       int    =		"""What is the placebo?"""
       lowerCAmelCase_					:       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    ,						_lowercase    ,						_lowercase						):
       lowerCAmelCase_					:       List[str]    =		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						):
       lowerCAmelCase_					:       Tuple    =		pipeline("""document-question-answering"""    ,						model="""hf-internal-testing/tiny-random-layoutlmv2"""						)
       lowerCAmelCase_					:       str    =		INVOICE_URL
       lowerCAmelCase_					:       int    =		"""How many cats are there?"""

       lowerCAmelCase_					:       List[Any]    =		[
           {"""score""": 0.0001, """answer""": """oy 2312/2019""", """start""": 38, """end""": 39},
           {"""score""": 0.0001, """answer""": """oy 2312/2019 DUE""", """start""": 38, """end""": 40},
       ]
       lowerCAmelCase_					:       Optional[Any]    =		dqa_pipeline(image=_lowercase    ,						question=_lowercase    ,						top_k=2						)
       self.assertEqual(nested_simplify(_lowercase    ,						decimals=4						)    ,						_lowercase						)

       lowerCAmelCase_					:       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
       lowerCAmelCase_					:       List[Any]    =		"""./tests/fixtures/tests_samples/COCO/000000039769.png"""
       lowerCAmelCase_					:       Any    =		dqa_pipeline(image=_lowercase    ,						question=_lowercase    ,						top_k=2						)
       self.assertEqual(_lowercase    ,						[]						)

       # We can optionnally pass directly the words and bounding boxes
       lowerCAmelCase_					:       int    =		"""./tests/fixtures/tests_samples/COCO/000000039769.png"""
       lowerCAmelCase_					:       int    =		[]
       lowerCAmelCase_					:       List[Any]    =		[]
       lowerCAmelCase_					:       List[Any]    =		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						):
       lowerCAmelCase_					:       Optional[Any]    =		pipeline(
           """document-question-answering"""    ,						model="""tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"""    ,						revision="""9977165"""    ,						)
       lowerCAmelCase_					:       Dict    =		INVOICE_URL
       lowerCAmelCase_					:       int    =		"""What is the invoice number?"""

       lowerCAmelCase_					:       Union[str, Any]    =		dqa_pipeline(image=_lowercase    ,						question=_lowercase    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               {"""score""": 0.9944, """answer""": """us-001""", """start""": 16, """end""": 16},
               {"""score""": 0.0009, """answer""": """us-001""", """start""": 16, """end""": 16},
           ]    ,						)

       lowerCAmelCase_					:       List[Any]    =		dqa_pipeline({"""image""": image, """question""": question}    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               {"""score""": 0.9944, """answer""": """us-001""", """start""": 16, """end""": 16},
               {"""score""": 0.0009, """answer""": """us-001""", """start""": 16, """end""": 16},
           ]    ,						)

       lowerCAmelCase_					:       List[str]    =		dqa_pipeline(
           [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               [
                   {"""score""": 0.9944, """answer""": """us-001""", """start""": 16, """end""": 16},
                   {"""score""": 0.0009, """answer""": """us-001""", """start""": 16, """end""": 16},
               ],
           ]
           * 2    ,						)







  @slow
  @require_torch
  @require_detectrona
  @require_pytesseract
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Optional[int]    =		pipeline(
           """document-question-answering"""    ,						model="""tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"""    ,						revision="""9977165"""    ,						max_seq_len=50    ,						)
       lowerCAmelCase_					:       List[str]    =		INVOICE_URL
       lowerCAmelCase_					:       Optional[int]    =		"""What is the invoice number?"""

       lowerCAmelCase_					:       List[Any]    =		dqa_pipeline(image=_lowercase    ,						question=_lowercase    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               {"""score""": 0.9974, """answer""": """1110212019""", """start""": 23, """end""": 23},
               {"""score""": 0.9948, """answer""": """us-001""", """start""": 16, """end""": 16},
           ]    ,						)

       lowerCAmelCase_					:       Optional[Any]    =		dqa_pipeline({"""image""": image, """question""": question}    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               {"""score""": 0.9974, """answer""": """1110212019""", """start""": 23, """end""": 23},
               {"""score""": 0.9948, """answer""": """us-001""", """start""": 16, """end""": 16},
           ]    ,						)

       lowerCAmelCase_					:       List[Any]    =		dqa_pipeline(
           [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               [
                   {"""score""": 0.9974, """answer""": """1110212019""", """start""": 23, """end""": 23},
                   {"""score""": 0.9948, """answer""": """us-001""", """start""": 16, """end""": 16},
               ]
           ]
           * 2    ,						)







  @slow
  @require_torch
  @require_pytesseract
  @require_vision
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       int    =		AutoTokenizer.from_pretrained(
           """impira/layoutlm-document-qa"""    ,						revision="""3dc6de3"""    ,						add_prefix_space=_lowercase						)
       lowerCAmelCase_					:       Any    =		pipeline(
           """document-question-answering"""    ,						model="""impira/layoutlm-document-qa"""    ,						tokenizer=_lowercase    ,						revision="""3dc6de3"""    ,						)
       lowerCAmelCase_					:       int    =		INVOICE_URL
       lowerCAmelCase_					:       Union[str, Any]    =		"""What is the invoice number?"""

       lowerCAmelCase_					:       Optional[Any]    =		dqa_pipeline(image=_lowercase    ,						question=_lowercase    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               {"""score""": 0.4251, """answer""": """us-001""", """start""": 16, """end""": 16},
               {"""score""": 0.0819, """answer""": """1110212019""", """start""": 23, """end""": 23},
           ]    ,						)

       lowerCAmelCase_					:       int    =		dqa_pipeline({"""image""": image, """question""": question}    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               {"""score""": 0.4251, """answer""": """us-001""", """start""": 16, """end""": 16},
               {"""score""": 0.0819, """answer""": """1110212019""", """start""": 23, """end""": 23},
           ]    ,						)

       lowerCAmelCase_					:       Union[str, Any]    =		dqa_pipeline(
           [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               [
                   {"""score""": 0.4251, """answer""": """us-001""", """start""": 16, """end""": 16},
                   {"""score""": 0.0819, """answer""": """1110212019""", """start""": 23, """end""": 23},
               ]
           ]
           * 2    ,						)

       lowerCAmelCase_					:       int    =		list(zip(*apply_tesseract(load_image(_lowercase						)    ,						_lowercase    ,						""""""						)						)						)

       # This model should also work if `image` is set to None
       lowerCAmelCase_					:       List[Any]    =		dqa_pipeline({"""image""": None, """word_boxes""": word_boxes, """question""": question}    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               {"""score""": 0.4251, """answer""": """us-001""", """start""": 16, """end""": 16},
               {"""score""": 0.0819, """answer""": """1110212019""", """start""": 23, """end""": 23},
           ]    ,						)







  @slow
  @require_torch
  @require_pytesseract
  @require_vision
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       int    =		AutoTokenizer.from_pretrained(
           """impira/layoutlm-document-qa"""    ,						revision="""3dc6de3"""    ,						add_prefix_space=_lowercase						)
       lowerCAmelCase_					:       Optional[Any]    =		pipeline(
           """document-question-answering"""    ,						model="""impira/layoutlm-document-qa"""    ,						tokenizer=_lowercase    ,						revision="""3dc6de3"""    ,						max_seq_len=50    ,						)
       lowerCAmelCase_					:       Tuple    =		INVOICE_URL
       lowerCAmelCase_					:       Tuple    =		"""What is the invoice number?"""

       lowerCAmelCase_					:       Optional[int]    =		dqa_pipeline(image=_lowercase    ,						question=_lowercase    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               {"""score""": 0.9999, """answer""": """us-001""", """start""": 16, """end""": 16},
               {"""score""": 0.9998, """answer""": """us-001""", """start""": 16, """end""": 16},
           ]    ,						)

       lowerCAmelCase_					:       int    =		dqa_pipeline(
           [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               [
                   {"""score""": 0.9999, """answer""": """us-001""", """start""": 16, """end""": 16},
                   {"""score""": 0.9998, """answer""": """us-001""", """start""": 16, """end""": 16},
               ]
           ]
           * 2    ,						)

       lowerCAmelCase_					:       Union[str, Any]    =		list(zip(*apply_tesseract(load_image(_lowercase						)    ,						_lowercase    ,						""""""						)						)						)

       # This model should also work if `image` is set to None
       lowerCAmelCase_					:       List[str]    =		dqa_pipeline({"""image""": None, """word_boxes""": word_boxes, """question""": question}    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               {"""score""": 0.9999, """answer""": """us-001""", """start""": 16, """end""": 16},
               {"""score""": 0.9998, """answer""": """us-001""", """start""": 16, """end""": 16},
           ]    ,						)







  @slow
  @require_torch
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       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"""    ,						)

       lowerCAmelCase_					:       Union[str, Any]    =		INVOICE_URL
       lowerCAmelCase_					:       int    =		"""What is the invoice number?"""
       lowerCAmelCase_					:       str    =		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						):
       pass
