


import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
       from PIL import Image
else:






       class 				__A    :






        """simple docstring"""




        @staticmethod
        def 				SCREAMING_SNAKE_CASE							(		*__A			,   **__A						) ->					Tuple:
            pass






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 				__A    (	unittest.TestCase      ):






 """simple docstring"""




 __lowerCAmelCase			 =  MODEL_FOR_OBJECT_DETECTION_MAPPING


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					Union[str, Any]:
     a					=ObjectDetectionPipeline(model=_a			,   image_processor=_a						)
     return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					Optional[Any]:
     a					=object_detector('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			,   threshold=0.0						)

     self.assertGreater(len(_a						)			,   0						)
     for detected_object in outputs:
         self.assertEqual(
             _a			,   {
                 '''score''': ANY(_a						),
                 '''label''': ANY(_a						),
                 '''box''': {'''xmin''': ANY(_a						), '''ymin''': ANY(_a						), '''xmax''': ANY(_a						), '''ymax''': ANY(_a						)},
             }			,   )

     import datasets

     a					=datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''			,   '''image'''			,   split='''test'''						)

     a					=[
         Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						),
         '''http://images.cocodataset.org/val2017/000000039769.jpg''',
         # RGBA
         dataset[0]['''file'''],
         # LA
         dataset[1]['''file'''],
         # L
         dataset[2]['''file'''],
     ]
     a					=object_detector(_a			,   threshold=0.0						)

     self.assertEqual(len(_a						)			,   len(_a						)						)
     for outputs in batch_outputs:
         self.assertGreater(len(_a						)			,   0						)
         for detected_object in outputs:
             self.assertEqual(
                 _a			,   {
                     '''score''': ANY(_a						),
                     '''label''': ANY(_a						),
                     '''box''': {'''xmin''': ANY(_a						), '''ymin''': ANY(_a						), '''xmax''': ANY(_a						), '''ymax''': ANY(_a						)},
                 }			,   )


 @require_tf
 @unittest.skip('''Object detection not implemented in TF'''						)
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     pass


 @require_torch
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     a					='''hf-internal-testing/tiny-detr-mobilenetsv3'''

     a					=AutoModelForObjectDetection.from_pretrained(_a						)
     a					=AutoFeatureExtractor.from_pretrained(_a						)
     a					=ObjectDetectionPipeline(model=_a			,   feature_extractor=_a						)

     a					=object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''			,   threshold=0.0						)

     self.assertEqual(
         nested_simplify(_a			,   decimals=4						)			,   [
             {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
             {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
         ]			,   )

     a					=object_detector(
         [
             '''http://images.cocodataset.org/val2017/000000039769.jpg''',
             '''http://images.cocodataset.org/val2017/000000039769.jpg''',
         ]			,   threshold=0.0			,   )

     self.assertEqual(
         nested_simplify(_a			,   decimals=4						)			,   [
             [
                 {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
                 {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
             ],
             [
                 {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
                 {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
             ],
         ]			,   )


 @require_torch
 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     a					='''facebook/detr-resnet-50'''

     a					=AutoModelForObjectDetection.from_pretrained(_a						)
     a					=AutoFeatureExtractor.from_pretrained(_a						)
     a					=ObjectDetectionPipeline(model=_a			,   feature_extractor=_a						)

     a					=object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''						)
     self.assertEqual(
         nested_simplify(_a			,   decimals=4						)			,   [
             {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
             {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
             {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
             {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
             {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
         ]			,   )

     a					=object_detector(
         [
             '''http://images.cocodataset.org/val2017/000000039769.jpg''',
             '''http://images.cocodataset.org/val2017/000000039769.jpg''',
         ]						)
     self.assertEqual(
         nested_simplify(_a			,   decimals=4						)			,   [
             [
                 {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
                 {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
                 {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
                 {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
                 {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
             ],
             [
                 {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
                 {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
                 {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
                 {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
                 {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
             ],
         ]			,   )


 @require_torch
 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     a					='''facebook/detr-resnet-50'''

     a					=pipeline('''object-detection'''			,   model=_a						)

     a					=object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''						)
     self.assertEqual(
         nested_simplify(_a			,   decimals=4						)			,   [
             {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
             {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
             {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
             {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
             {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
         ]			,   )

     a					=object_detector(
         [
             '''http://images.cocodataset.org/val2017/000000039769.jpg''',
             '''http://images.cocodataset.org/val2017/000000039769.jpg''',
         ]						)
     self.assertEqual(
         nested_simplify(_a			,   decimals=4						)			,   [
             [
                 {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
                 {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
                 {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
                 {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
                 {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
             ],
             [
                 {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
                 {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
                 {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
                 {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
                 {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
             ],
         ]			,   )


 @require_torch
 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     a					=0.9_985
     a					='''facebook/detr-resnet-50'''

     a					=pipeline('''object-detection'''			,   model=_a						)

     a					=object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''			,   threshold=_a						)
     self.assertEqual(
         nested_simplify(_a			,   decimals=4						)			,   [
             {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
             {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
         ]			,   )





 @require_torch
 @require_pytesseract
 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     a					='''Narsil/layoutlmv3-finetuned-funsd'''
     a					=0.9_993

     a					=pipeline('''object-detection'''			,   model=_a			,   threshold=_a						)

     a					=object_detector(
         '''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'''						)
     self.assertEqual(
         nested_simplify(_a			,   decimals=4						)			,   [
             {'''score''': 0.9_993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
             {'''score''': 0.9_993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
         ]			,   )



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase_       :       Union[str, Any]      						=  {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       Optional[int]      						=  ["""LayoutXLMTokenizer"""]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       Dict      						=  ["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
       from .processing_layoutxlm import LayoutXLMProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm import LayoutXLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
       import sys

       lowerCamelCase_       :       Union[str, Any]      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)