
"""simple docstring"""




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   _lowercase						:


      '''simple docstring'''





      @staticmethod
      def  lowerCAmelCase__       (      *__UpperCamelCase ,			**__UpperCamelCase  )->					Any:
           pass







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


     '''simple docstring'''





     _A								=							MODEL_FOR_OBJECT_DETECTION_MAPPING



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					str:
          UpperCAmelCase__							: Dict 							=						ObjectDetectionPipeline(model=__UpperCamelCase ,			image_processor=__UpperCamelCase  )
          return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					Any:
          UpperCAmelCase__							: str 							=						object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png" ,			threshold=0.0  )

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

          import datasets

          UpperCAmelCase__							: Union[str, Any] 							=						datasets.load_dataset("hf-internal-testing/fixtures_image_utils" ,			"image" ,			split="test"  )

          UpperCAmelCase__							: List[Any] 							=						[
              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"],
          ]
          UpperCAmelCase__							: Optional[int] 							=						object_detector(__UpperCamelCase ,			threshold=0.0  )

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



     @require_tf
     @unittest.skip("Object detection not implemented in TF"  )
     def  lowerCAmelCase__       (      self  )->					Tuple:
          pass



     @require_torch
     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: int 							=						"hf-internal-testing/tiny-detr-mobilenetsv3"

          UpperCAmelCase__							: Any 							=						AutoModelForObjectDetection.from_pretrained(__UpperCamelCase  )
          UpperCAmelCase__							: List[str] 							=						AutoFeatureExtractor.from_pretrained(__UpperCamelCase  )
          UpperCAmelCase__							: Any 							=						ObjectDetectionPipeline(model=__UpperCamelCase ,			feature_extractor=__UpperCamelCase  )

          UpperCAmelCase__							: Any 							=						object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" ,			threshold=0.0  )

          self.assertEqual(
              nested_simplify(__UpperCamelCase ,			decimals=4  ) ,			[
                  {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
                  {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
              ] ,			)

          UpperCAmelCase__							: int 							=						object_detector(
              [
                  "http://images.cocodataset.org/val2017/000000039769.jpg",
                  "http://images.cocodataset.org/val2017/000000039769.jpg",
              ] ,			threshold=0.0 ,			)

          self.assertEqual(
              nested_simplify(__UpperCamelCase ,			decimals=4  ) ,			[
                  [
                      {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
                      {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
                  ],
                  [
                      {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
                      {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
                  ],
              ] ,			)



     @require_torch
     @slow
     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							: Any 							=						"facebook/detr-resnet-50"

          UpperCAmelCase__							: Optional[Any] 							=						AutoModelForObjectDetection.from_pretrained(__UpperCamelCase  )
          UpperCAmelCase__							: Dict 							=						AutoFeatureExtractor.from_pretrained(__UpperCamelCase  )
          UpperCAmelCase__							: Dict 							=						ObjectDetectionPipeline(model=__UpperCamelCase ,			feature_extractor=__UpperCamelCase  )

          UpperCAmelCase__							: Any 							=						object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"  )
          self.assertEqual(
              nested_simplify(__UpperCamelCase ,			decimals=4  ) ,			[
                  {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
                  {"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
                  {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
                  {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
                  {"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
              ] ,			)

          UpperCAmelCase__							: int 							=						object_detector(
              [
                  "http://images.cocodataset.org/val2017/000000039769.jpg",
                  "http://images.cocodataset.org/val2017/000000039769.jpg",
              ]  )
          self.assertEqual(
              nested_simplify(__UpperCamelCase ,			decimals=4  ) ,			[
                  [
                      {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
                      {"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
                      {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
                      {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
                      {"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
                  ],
                  [
                      {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
                      {"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
                      {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
                      {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
                      {"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
                  ],
              ] ,			)



     @require_torch
     @slow
     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: Optional[Any] 							=						"facebook/detr-resnet-50"

          UpperCAmelCase__							: Dict 							=						pipeline("object-detection" ,			model=__UpperCamelCase  )

          UpperCAmelCase__							: Union[str, Any] 							=						object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"  )
          self.assertEqual(
              nested_simplify(__UpperCamelCase ,			decimals=4  ) ,			[
                  {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
                  {"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
                  {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
                  {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
                  {"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
              ] ,			)

          UpperCAmelCase__							: Optional[int] 							=						object_detector(
              [
                  "http://images.cocodataset.org/val2017/000000039769.jpg",
                  "http://images.cocodataset.org/val2017/000000039769.jpg",
              ]  )
          self.assertEqual(
              nested_simplify(__UpperCamelCase ,			decimals=4  ) ,			[
                  [
                      {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
                      {"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
                      {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
                      {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
                      {"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
                  ],
                  [
                      {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
                      {"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
                      {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
                      {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
                      {"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
                  ],
              ] ,			)



     @require_torch
     @slow
     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: List[Any] 							=						0.9985
          UpperCAmelCase__							: Dict 							=						"facebook/detr-resnet-50"

          UpperCAmelCase__							: Optional[Any] 							=						pipeline("object-detection" ,			model=__UpperCamelCase  )

          UpperCAmelCase__							: Union[str, Any] 							=						object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" ,			threshold=__UpperCamelCase  )
          self.assertEqual(
              nested_simplify(__UpperCamelCase ,			decimals=4  ) ,			[
                  {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
                  {"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
              ] ,			)



     @require_torch
     @require_pytesseract
     @slow
     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: Union[str, Any] 							=						"Narsil/layoutlmv3-finetuned-funsd"
          UpperCAmelCase__							: Optional[Any] 							=						0.9993

          UpperCAmelCase__							: Optional[Any] 							=						pipeline("object-detection" ,			model=__UpperCamelCase ,			threshold=__UpperCamelCase  )

          UpperCAmelCase__							: List[str] 							=						object_detector(
              "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"  )
          self.assertEqual(
              nested_simplify(__UpperCamelCase ,			decimals=4  ) ,			[
                  {"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 2_94, "ymin": 2_54, "xmax": 3_43, "ymax": 2_64}},
                  {"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 2_94, "ymin": 2_54, "xmax": 3_43, "ymax": 2_64}},
              ] ,			)





"""simple docstring"""




import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase



def 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       List[Any]		):






     '''simple docstring'''
     if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
          UpperCAmelCase__							: Optional[int] 							=						np.full((len(lowerCAmelCase		), sequence_length, 2)       ,    lowerCAmelCase		)
     else:
          UpperCAmelCase__							: Optional[Any] 							=						np.full((len(lowerCAmelCase		), sequence_length)       ,    lowerCAmelCase		)

     for i, tensor in enumerate(lowerCAmelCase		):
          if padding_side == "right":
               if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: Dict 							=						tensor[:sequence_length]
               else:
                    UpperCAmelCase__							: Tuple 							=						tensor[:sequence_length]
          else:
               if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: Optional[Any] 							=						tensor[:sequence_length]
               else:
                    UpperCAmelCase__							: int 							=						tensor[:sequence_length]

     return out_tensor.tolist()



def 							a__ (				lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						ord(lowerCAmelCase		)
     if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
          return True
     UpperCAmelCase__							: Optional[Any] 							=						unicodedata.category(lowerCAmelCase		)
     if cat.startswith("P"		):
          return True
     return False







@dataclass
class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							42
     _A								=							True
     _A								=							None
     _A								=							None
     _A								=							-100
     _A								=							"pt"



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[str]:
          import torch

          UpperCAmelCase__							: Optional[Any] 							=						"label" if "label" in features[0].keys() else "labels"
          UpperCAmelCase__							: Dict 							=						[feature[label_name] for feature in features] if label_name in features[0].keys() else None
          UpperCAmelCase__							: str 							=						self.tokenizer.pad(
              __UpperCamelCase ,			padding=self.padding ,			max_length=self.max_length ,			pad_to_multiple_of=self.pad_to_multiple_of ,			return_tensors="pt" if labels is None else None ,			)

          if labels is None:
               return batch

          UpperCAmelCase__							: Optional[Any] 							=						torch.tensor(batch["entity_ids"]  ).shape[1]
          UpperCAmelCase__							: int 							=						self.tokenizer.padding_side
          if padding_side == "right":
               UpperCAmelCase__							: int 							=						[
                   list(__UpperCamelCase  ) + [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase  )) for label in labels
               ]
          else:
               UpperCAmelCase__							: List[Any] 							=						[
                   [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase  )) + list(__UpperCamelCase  ) for label in labels
               ]

          UpperCAmelCase__							: Optional[Any] 							=						[feature["ner_tags"] for feature in features]
          UpperCAmelCase__							: int 							=						padding_tensor(__UpperCamelCase ,			-1 ,			__UpperCamelCase ,			__UpperCamelCase  )
          UpperCAmelCase__							: List[Any] 							=						[feature["original_entity_spans"] for feature in features]
          UpperCAmelCase__							: int 							=						padding_tensor(__UpperCamelCase ,			(-1, -1) ,			__UpperCamelCase ,			__UpperCamelCase  )
          UpperCAmelCase__							: Optional[int] 							=						{k: torch.tensor(__UpperCamelCase ,			dtype=torch.intaa  ) for k, v in batch.items()}

          return batch



