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, ChunkPipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    import torch

    from transformers.modeling_outputs import BaseModelOutput

    from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

__lowercase											=      logging.get_logger(__name__)


@add_end_docstrings(UpperCAmelCase_				)
class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    def __init__(			self				,				**__lowercase) ->			int:
        super().__init__(**__lowercase)

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

        requires_backends(self				,				'''vision''')
        self.check_model_type(__lowercase)







    def __call__(			self				,				__lowercase				,				__lowercase = None				,				**__lowercase				,				) ->			Union[str, Any]:
        if "text_queries" in kwargs:
            __UpperCamelCase  :List[str]    						=	kwargs.pop('''text_queries''')

        if isinstance(__lowercase				,				(str, Image.Image)):
            __UpperCamelCase  :Any    						=	{'''image''': image, '''candidate_labels''': candidate_labels}
        else:
            __UpperCamelCase  :Union[str, Any]    						=	image
        __UpperCamelCase  :Any    						=	super().__call__(__lowercase				,				**__lowercase)
        return results







    def     UpperCamelCase__     (			self				,				**__lowercase) ->			Any:
        __UpperCamelCase  :Optional[Any]    						=	{}
        if "threshold" in kwargs:
            __UpperCamelCase  :Tuple    						=	kwargs['''threshold''']
        if "top_k" in kwargs:
            __UpperCamelCase  :int    						=	kwargs['''top_k''']
        return {}, {}, postprocess_params







    def     UpperCamelCase__     (			self				,				__lowercase) ->			List[Any]:
        __UpperCamelCase  :List[str]    						=	load_image(inputs['''image'''])
        __UpperCamelCase  :Optional[int]    						=	inputs['''candidate_labels''']
        if isinstance(__lowercase				,				__lowercase):
            __UpperCamelCase  :Dict    						=	candidate_labels.split(''',''')

        __UpperCamelCase  :Optional[int]    						=	torch.tensor([[image.height, image.width]]				,				dtype=torch.intaa)
        for i, candidate_label in enumerate(__lowercase):
            __UpperCamelCase  :Dict    						=	self.tokenizer(__lowercase				,				return_tensors=self.framework)
            __UpperCamelCase  :Dict    						=	self.image_processor(__lowercase				,				return_tensors=self.framework)
            yield {
                "is_last": i == len(__lowercase) - 1,
                "target_size": target_size,
                "candidate_label": candidate_label,
                **text_inputs,
                **image_features,
            }







    def     UpperCamelCase__     (			self				,				__lowercase) ->			Optional[Any]:
        __UpperCamelCase  :int    						=	model_inputs.pop('''target_size''')
        __UpperCamelCase  :Optional[int]    						=	model_inputs.pop('''candidate_label''')
        __UpperCamelCase  :int    						=	model_inputs.pop('''is_last''')

        __UpperCamelCase  :int    						=	self.model(**__lowercase)

        __UpperCamelCase  :str    						=	{'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
        return model_outputs







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase=0.1				,				__lowercase=None) ->			Any:
        __UpperCamelCase  :Optional[Any]    						=	[]
        for model_output in model_outputs:
            __UpperCamelCase  :List[str]    						=	model_output['''candidate_label''']
            __UpperCamelCase  :Dict    						=	BaseModelOutput(__lowercase)
            __UpperCamelCase  :Any    						=	self.image_processor.post_process_object_detection(
                outputs=__lowercase				,				threshold=__lowercase				,				target_sizes=model_output['''target_size'''])[0]

            for index in outputs["scores"].nonzero():
                __UpperCamelCase  :Optional[Any]    						=	outputs['''scores'''][index].item()
                __UpperCamelCase  :List[Any]    						=	self._get_bounding_box(outputs['''boxes'''][index][0])

                __UpperCamelCase  :Any    						=	{'''score''': score, '''label''': label, '''box''': box}
                results.append(__lowercase)

        __UpperCamelCase  :Any    						=	sorted(__lowercase				,				key=lambda __lowercase: x["score"]				,				reverse=__lowercase)
        if top_k:
            __UpperCamelCase  :List[Any]    						=	results[:top_k]

        return results







    def     UpperCamelCase__     (			self				,				__lowercase) ->			Dict[str, int]:
        if self.framework != "pt":
            raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.''')
        __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :List[Any]    						=	box.int().tolist()
        __UpperCamelCase  :List[Any]    						=	{
            '''xmin''': xmin,
            '''ymin''': ymin,
            '''xmax''': xmax,
            '''ymax''': ymax,
        }
        return bbox






from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

__lowercase											=      datasets.load_iris()

__lowercase											=      np.array(data['''data'''])
__lowercase											=      np.array(data['''target'''])
__lowercase											=      data['''target_names''']

__lowercase				, __lowercase				, __lowercase				, __lowercase											=      train_test_split(X, y)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    return np.linalg.norm(np.array(SCREAMING_SNAKE_CASE			) - np.array(SCREAMING_SNAKE_CASE			)			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=5			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[int]    						=	zip(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    # List of distances of all points from the point to be classified
    __UpperCamelCase  :List[str]    						=	[]
    for data_point in data:
        __UpperCamelCase  :Optional[int]    						=	euclidean_distance(data_point[0]						,    SCREAMING_SNAKE_CASE			)
        distances.append((distance, data_point[1])			)
    # Choosing 'k' points with the least distances.
    __UpperCamelCase  :Any    						=	[i[1] for i in sorted(SCREAMING_SNAKE_CASE			)[:k]]
    # Most commonly occurring class among them
    # is the class into which the point is classified
    __UpperCamelCase  :Union[str, Any]    						=	Counter(SCREAMING_SNAKE_CASE			).most_common(1			)[0][0]
    return classes[result]


if __name__ == "__main__":
    print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))





