


"""simple docstring"""







from typing import List, Union

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


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_tf_available():
		import tensorflow as tf

		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
		from ..tf_utils import stable_softmax

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

a__				:							str						=     logging.get_logger(__name__)




@add_end_docstrings(_SCREAMING_SNAKE_CASE)
class  UpperCamelCase_	( _SCREAMING_SNAKE_CASE):

	"""simple docstring"""





	def __init__( self			:				str			,			*UpperCAmelCase__			:				Any			,			**UpperCAmelCase__			:				List[Any]   )       ->	Tuple:
						super().__init__(*A_			,			**A_   )
						requires_backends(self			,			"vision"   )
						self.check_model_type(
						    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
						    if self.framework == "tf"
						    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING   )




	def   UpperCAmelCase_      ( self			:				List[Any]			,			UpperCAmelCase__			:				str=None   )       ->	Optional[Any]:
						__SCREAMING_SNAKE_CASE						   =				{}
						if top_k is not None:
											__SCREAMING_SNAKE_CASE						   =				top_k
						return {}, {}, postprocess_params




	def __call__( self			:				int			,			UpperCAmelCase__			:				Tuple			,			**UpperCAmelCase__			:				Dict   )       ->	List[Any]:
						return super().__call__(A_			,			**A_   )




	def   UpperCAmelCase_      ( self			:				Tuple			,			UpperCAmelCase__			:				Union[str, Any]   )       ->	str:
						__SCREAMING_SNAKE_CASE						   =				load_image(A_   )
						__SCREAMING_SNAKE_CASE						   =				self.image_processor(images=A_			,			return_tensors=self.framework   )
						return model_inputs




	def   UpperCAmelCase_      ( self			:				List[str]			,			UpperCAmelCase__			:				Optional[int]   )       ->	Dict:
						__SCREAMING_SNAKE_CASE						   =				self.model(**A_   )
						return model_outputs




	def   UpperCAmelCase_      ( self			:				Any			,			UpperCAmelCase__			:				Tuple			,			UpperCAmelCase__			:				Any=5   )       ->	Optional[Any]:
						if top_k > self.model.config.num_labels:
											__SCREAMING_SNAKE_CASE						   =				self.model.config.num_labels

						if self.framework == "pt":
											__SCREAMING_SNAKE_CASE						   =				model_outputs.logits.softmax(-1   )[0]
											__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				probs.topk(A_   )
						elif self.framework == "tf":
											__SCREAMING_SNAKE_CASE						   =				stable_softmax(model_outputs.logits			,			axis=-1   )[0]
											__SCREAMING_SNAKE_CASE						   =				tf.math.top_k(A_			,			k=A_   )
											__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				topk.values.numpy(), topk.indices.numpy()
						else:
											raise ValueError(F"""Unsupported framework: {self.framework}"""   )

						__SCREAMING_SNAKE_CASE						   =				scores.tolist()
						__SCREAMING_SNAKE_CASE						   =				ids.tolist()
						return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(A_			,			A_   )]


from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters


def       A       (					)   ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase		=     {
		    '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		=     Dataset.from_dict(lowercase )
		return dataset


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     get_dataset()
				UpperCamelCase		=     make_duplicate_clusters(A_				,      0.85       )
				self.assertEqual(len(duplicate_clusters[0]       )				,      2       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     get_dataset()
				UpperCamelCase	,  UpperCamelCase		=     deduplicate_dataset(A_       )
				self.assertEqual(len(A_       )				,      2       )
				print(A_       )
				self.assertEqual(duplicate_clusters[0][0]['copies']				,      2       )
				self.assertEqual(duplicate_clusters[0][0]['is_extreme']				,      A_       )
