
"""simple docstring"""





from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from ..image_utils import load_image


if is_torch_available():
							import torch

							from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__magic_name__       =			logging.get_logger(__name__)


__magic_name__       =			Dict[str, Any]
__magic_name__       =			List[Prediction]
@add_end_docstrings(__a )
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		def __init__(     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				super().__init__(*lowerCAmelCase__							,  **lowerCAmelCase__)

				if self.framework == "tf":
						raise ValueError(f"The {self.__class__} is only available in PyTorch.")

				requires_backends(self							,  """vision""")
				self.check_model_type(
				    dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()))




		def 	snake_case_     (     self							,  **lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = {}
				if "threshold" in kwargs:
						__SCREAMING_SNAKE_CASE            = kwargs["""threshold"""]
				return {}, {}, postprocess_kwargs




		def __call__(     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				return super().__call__(*lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = load_image(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = torch.IntTensor([[image.height, image.width]])
				__SCREAMING_SNAKE_CASE            = self.image_processor(images=[image]							,  return_tensors="""pt""")
				if self.tokenizer is not None:
						__SCREAMING_SNAKE_CASE            = self.tokenizer(text=inputs["""words"""]							,  boxes=inputs["""boxes"""]							,  return_tensors="""pt""")
				__SCREAMING_SNAKE_CASE            = target_size
				return inputs




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = model_inputs.pop("""target_size""")
				__SCREAMING_SNAKE_CASE            = self.model(**lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = outputs.__class__({"""target_size""": target_size, **outputs})
				if self.tokenizer is not None:
						__SCREAMING_SNAKE_CASE            = model_inputs["""bbox"""]
				return model_outputs




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__=0.9):
				__SCREAMING_SNAKE_CASE            = model_outputs["""target_size"""]
				if self.tokenizer is not None:
						# This is a LayoutLMForTokenClassification variant.
						# The OCR got the boxes and the model classified the words.
						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = target_size[0].tolist()

						def unnormalize(lowerCAmelCase__):
								return self._get_bounding_box(
								    torch.Tensor(
								        [
								            (width * bbox[0] / 1_0_0_0),
								            (height * bbox[1] / 1_0_0_0),
								            (width * bbox[2] / 1_0_0_0),
								            (height * bbox[3] / 1_0_0_0),
								        ]))

						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = model_outputs["""logits"""].squeeze(0).softmax(dim=-1).max(dim=-1)
						__SCREAMING_SNAKE_CASE            = [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
						__SCREAMING_SNAKE_CASE            = [unnormalize(lowerCAmelCase__) for bbox in model_outputs["""bbox"""].squeeze(0)]
						__SCREAMING_SNAKE_CASE            = ["""score""", """label""", """box"""]
						__SCREAMING_SNAKE_CASE            = [dict(zip(lowerCAmelCase__							,  lowerCAmelCase__)) for vals in zip(scores.tolist()							,  lowerCAmelCase__							,  lowerCAmelCase__) if vals[0] > threshold]
				else:
						# This is a regular ForObjectDetectionModel
						__SCREAMING_SNAKE_CASE            = self.image_processor.post_process_object_detection(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__)
						__SCREAMING_SNAKE_CASE            = raw_annotations[0]
						__SCREAMING_SNAKE_CASE            = raw_annotation["""scores"""]
						__SCREAMING_SNAKE_CASE            = raw_annotation["""labels"""]
						__SCREAMING_SNAKE_CASE            = raw_annotation["""boxes"""]

						__SCREAMING_SNAKE_CASE            = scores.tolist()
						__SCREAMING_SNAKE_CASE            = [self.model.config.idalabel[label.item()] for label in labels]
						__SCREAMING_SNAKE_CASE            = [self._get_bounding_box(lowerCAmelCase__) for box in boxes]

						# {"scores": [...], ...} --> [{"score":x, ...}, ...]
						__SCREAMING_SNAKE_CASE            = ["""score""", """label""", """box"""]
						__SCREAMING_SNAKE_CASE            = [
						    dict(zip(lowerCAmelCase__							,  lowerCAmelCase__))
						    for vals in zip(raw_annotation["""scores"""]							,  raw_annotation["""labels"""]							,  raw_annotation["""boxes"""])
						]

				return annotation




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				if self.framework != "pt":
						raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch.""")
				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = box.int().tolist()
				__SCREAMING_SNAKE_CASE            = {
				    """xmin""": xmin,
				    """ymin""": ymin,
				    """xmax""": xmax,
				    """ymax""": ymax,
				}
				return bbox






"""simple docstring"""





from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters







def 	__a     (		):
   UpperCAmelCase_					:					List[Any]			     =    {
       "repo_name": ["test_repo1", "test_repo2", "test_repo3"],
       "path": ["test_1.py", "test_2.py", "unit_test.py"],
       "content": ["a " * 20, "a " * 30, "b " * 7],
   }
   UpperCAmelCase_					:					Optional[int]			     =    Dataset.from_dict(__lowerCamelCase  )
   return dataset


class A_	(lowercase__      ):




  '''simple docstring'''







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    get_dataset()
     UpperCAmelCase_					:					Any			     =    make_duplicate_clusters(lowercase_				,   0.85  )
     self.assertEqual(len(duplicate_clusters[0]  )				,   2  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    get_dataset()
     UpperCAmelCase_     ,   UpperCAmelCase_					:					List[str]			     =    deduplicate_dataset(lowercase_  )
     self.assertEqual(len(lowercase_  )				,   2  )
     print(lowercase_  )
     self.assertEqual(duplicate_clusters[0][0]["copies"]				,   2  )
     self.assertEqual(duplicate_clusters[0][0]["is_extreme"]				,   lowercase_  )


