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        __SCREAMING_SNAKE_CASE      :







              @staticmethod
              def 						__lowerCamelCase   (				*A					:     str		, **A					:     List[str]      )       ->Tuple:
                 pass


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):
       _UpperCAmelCase				:  Any			   =   MODEL_FOR_OBJECT_DETECTION_MAPPING







       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     Dict		, A					:     Optional[int]		, A					:     List[Any]      )       ->Union[str, Any]:
          lowerCamelCase__       :						Union[str, Any]        =							ObjectDetectionPipeline(model=_a		, image_processor=_a      )
          return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]







       def 						__lowerCamelCase   (				self					:     Dict		, A					:     str		, A					:     Optional[int]      )       ->Optional[Any]:
          lowerCamelCase__       :						str        =							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

          lowerCamelCase__       :						Optional[Any]        =							datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''		, '''image'''		, split='''test'''      )

          lowerCamelCase__       :						Tuple        =							[
              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'''],
          ]
          lowerCamelCase__       :						int        =							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 						__lowerCamelCase   (				self					:     List[str]      )       ->Union[str, Any]:
          pass







       @require_torch
       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Union[str, Any]:
          lowerCamelCase__       :						Tuple        =							'''hf-internal-testing/tiny-detr-mobilenetsv3'''

          lowerCamelCase__       :						List[Any]        =							AutoModelForObjectDetection.from_pretrained(_a      )
          lowerCamelCase__       :						Optional[int]        =							AutoFeatureExtractor.from_pretrained(_a      )
          lowerCamelCase__       :						List[str]        =							ObjectDetectionPipeline(model=_a		, feature_extractor=_a      )

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

          self.assertEqual(
              nested_simplify(_a		, decimals=4      )		, [
                  {'''score''': 0.33_76, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
                  {'''score''': 0.33_76, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
              ]		, )

          lowerCamelCase__       :						List[Any]        =							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''': 0.33_76, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
                      {'''score''': 0.33_76, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
                  ],
                  [
                      {'''score''': 0.33_76, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
                      {'''score''': 0.33_76, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
                  ],
              ]		, )







       @require_torch
       @slow
       def 						__lowerCamelCase   (				self					:     str      )       ->int:
          lowerCamelCase__       :						Optional[int]        =							'''facebook/detr-resnet-50'''

          lowerCamelCase__       :						Dict        =							AutoModelForObjectDetection.from_pretrained(_a      )
          lowerCamelCase__       :						List[str]        =							AutoFeatureExtractor.from_pretrained(_a      )
          lowerCamelCase__       :						Union[str, Any]        =							ObjectDetectionPipeline(model=_a		, feature_extractor=_a      )

          lowerCamelCase__       :						Optional[int]        =							object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''      )
          self.assertEqual(
              nested_simplify(_a		, decimals=4      )		, [
                  {'''score''': 0.99_82, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
                  {'''score''': 0.99_60, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
                  {'''score''': 0.99_55, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
                  {'''score''': 0.99_88, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
                  {'''score''': 0.99_87, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
              ]		, )

          lowerCamelCase__       :						List[str]        =							object_detector(
              [
                  '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                  '''http://images.cocodataset.org/val2017/000000039769.jpg''',
              ]      )
          self.assertEqual(
              nested_simplify(_a		, decimals=4      )		, [
                  [
                      {'''score''': 0.99_82, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
                      {'''score''': 0.99_60, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
                      {'''score''': 0.99_55, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
                      {'''score''': 0.99_88, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
                      {'''score''': 0.99_87, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
                  ],
                  [
                      {'''score''': 0.99_82, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
                      {'''score''': 0.99_60, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
                      {'''score''': 0.99_55, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
                      {'''score''': 0.99_88, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
                      {'''score''': 0.99_87, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
                  ],
              ]		, )







       @require_torch
       @slow
       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->Tuple:
          lowerCamelCase__       :						Any        =							'''facebook/detr-resnet-50'''

          lowerCamelCase__       :						Union[str, Any]        =							pipeline('''object-detection'''		, model=_a      )

          lowerCamelCase__       :						Optional[int]        =							object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''      )
          self.assertEqual(
              nested_simplify(_a		, decimals=4      )		, [
                  {'''score''': 0.99_82, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
                  {'''score''': 0.99_60, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
                  {'''score''': 0.99_55, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
                  {'''score''': 0.99_88, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
                  {'''score''': 0.99_87, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
              ]		, )

          lowerCamelCase__       :						Optional[Any]        =							object_detector(
              [
                  '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                  '''http://images.cocodataset.org/val2017/000000039769.jpg''',
              ]      )
          self.assertEqual(
              nested_simplify(_a		, decimals=4      )		, [
                  [
                      {'''score''': 0.99_82, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
                      {'''score''': 0.99_60, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
                      {'''score''': 0.99_55, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
                      {'''score''': 0.99_88, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
                      {'''score''': 0.99_87, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
                  ],
                  [
                      {'''score''': 0.99_82, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
                      {'''score''': 0.99_60, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
                      {'''score''': 0.99_55, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
                      {'''score''': 0.99_88, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
                      {'''score''': 0.99_87, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
                  ],
              ]		, )







       @require_torch
       @slow
       def 						__lowerCamelCase   (				self					:     Dict      )       ->List[str]:
          lowerCamelCase__       :						str        =							0.99_85
          lowerCamelCase__       :						List[str]        =							'''facebook/detr-resnet-50'''

          lowerCamelCase__       :						Optional[int]        =							pipeline('''object-detection'''		, model=_a      )

          lowerCamelCase__       :						Tuple        =							object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''		, threshold=_a      )
          self.assertEqual(
              nested_simplify(_a		, decimals=4      )		, [
                  {'''score''': 0.99_88, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
                  {'''score''': 0.99_87, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
              ]		, )







       @require_torch
       @require_pytesseract
       @slow
       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Tuple:
          lowerCamelCase__       :						Tuple        =							'''Narsil/layoutlmv3-finetuned-funsd'''
          lowerCamelCase__       :						Union[str, Any]        =							0.99_93

          lowerCamelCase__       :						int        =							pipeline('''object-detection'''		, model=_a		, threshold=_a      )

          lowerCamelCase__       :						int        =							object_detector(
              '''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'''      )
          self.assertEqual(
              nested_simplify(_a		, decimals=4      )		, [
                  {'''score''': 0.99_93, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 2_9_4, '''ymin''': 2_5_4, '''xmax''': 3_4_3, '''ymax''': 2_6_4}},
                  {'''score''': 0.99_93, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 2_9_4, '''ymin''': 2_5_4, '''xmax''': 3_4_3, '''ymax''': 2_6_4}},
              ]		, )










import pprint

import requests

_A :   Any  			=  'https://zenquotes.io/api'



def 	_a						(   )    ->    list:







   """simple docstring"""
   return requests.get(API_ENDPOINT_URL + '''/today'''					).json()



def 	_a						(   )    ->    list:







   """simple docstring"""
   return requests.get(API_ENDPOINT_URL + '''/random'''					).json()


if __name__ == "__main__":
       _A :   Optional[Any]  			=  random_quotes()
       pprint.pprint(response)



