

from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase_			=  logging.get_logger(__name__)


class 			A						(							_UpperCAmelCase			):







				"""simple docstring"""





				lowerCamelCase		     =							['pixel_values']


				def __init__(				self			:	Optional[Any],lowercase_			:	bool = True,lowercase_			:	Dict[str, int] = None,lowercase_			:	PILImageResampling = PILImageResampling.BICUBIC,lowercase_			:	bool = True,lowercase_			:	Dict[str, int] = None,lowercase_			:	bool = True,lowercase_			:	Union[int, float] = 1 / 2_5_5,lowercase_			:	bool = True,lowercase_			:	Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN,lowercase_			:	Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD,**lowercase_			:	str,)->   None:





								'''simple docstring'''







								super().__init__(**lowercase_		)
								A__   				=       size if size is not None else {'shortest_edge': 2_2_4}
								A__   				=       get_size_dict(lowercase_,default_to_square=lowercase_		)
								A__   				=       crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
								A__   				=       get_size_dict(lowercase_,param_name='crop_size'		)

								A__   				=       do_resize
								A__   				=       size
								A__   				=       resample
								A__   				=       do_center_crop
								A__   				=       crop_size
								A__   				=       do_rescale
								A__   				=       rescale_factor
								A__   				=       do_normalize
								A__   				=       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
								A__   				=       image_std if image_std is not None else IMAGENET_DEFAULT_STD


				def 						snake_case__					(				self			:	List[str],lowercase_			:	np.ndarray,lowercase_			:	Dict[str, int],lowercase_			:	PILImageResampling = PILImageResampling.BICUBIC,lowercase_			:	Optional[Union[str, ChannelDimension]] = None,**lowercase_			:	str,)->   np.ndarray:





								'''simple docstring'''







								A__   				=       get_size_dict(lowercase_,default_to_square=lowercase_		)
								# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
								if "shortest_edge" in size:
												A__   				=       int((2_5_6 / 2_2_4) * size['shortest_edge']		)
												A__   				=       get_resize_output_image_size(lowercase_,size=lowercase_,default_to_square=lowercase_		)
												A__   				=       {'height': output_size[0], 'width': output_size[1]}
								if "height" not in size_dict or "width" not in size_dict:
												raise ValueError(
												    F'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'		)
								return resize(
								    lowercase_,size=(size_dict['height'], size_dict['width']),resample=lowercase_,data_format=lowercase_,**lowercase_		)


				def 						snake_case__					(				self			:	List[Any],lowercase_			:	np.ndarray,lowercase_			:	Dict[str, int],lowercase_			:	Optional[Union[str, ChannelDimension]] = None,**lowercase_			:	List[Any],)->   np.ndarray:





								'''simple docstring'''







								A__   				=       get_size_dict(lowercase_		)
								if "height" not in size or "width" not in size:
												raise ValueError(F'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'		)
								return center_crop(lowercase_,size=(size['height'], size['width']),data_format=lowercase_,**lowercase_		)


				def 						snake_case__					(				self			:	Dict,lowercase_			:	np.ndarray,lowercase_			:	Union[int, float],lowercase_			:	Optional[Union[str, ChannelDimension]] = None,**lowercase_			:	Tuple,)->   np.ndarray:





								'''simple docstring'''







								return rescale(lowercase_,scale=lowercase_,data_format=lowercase_,**lowercase_		)


				def 						snake_case__					(				self			:	Tuple,lowercase_			:	np.ndarray,lowercase_			:	Union[float, List[float]],lowercase_			:	Union[float, List[float]],lowercase_			:	Optional[Union[str, ChannelDimension]] = None,**lowercase_			:	Optional[Any],)->   np.ndarray:





								'''simple docstring'''







								return normalize(lowercase_,mean=lowercase_,std=lowercase_,data_format=lowercase_,**lowercase_		)


				def 						snake_case__					(				self			:	int,lowercase_			:	ImageInput,lowercase_			:	Optional[bool] = None,lowercase_			:	Optional[Dict[str, int]] = None,lowercase_			:	PILImageResampling = None,lowercase_			:	Optional[bool] = None,lowercase_			:	Optional[Dict[str, int]] = None,lowercase_			:	Optional[bool] = None,lowercase_			:	Optional[float] = None,lowercase_			:	Optional[bool] = None,lowercase_			:	Optional[Union[float, Iterable[float]]] = None,lowercase_			:	Optional[Union[float, Iterable[float]]] = None,lowercase_			:	Optional[TensorType] = None,lowercase_			:	ChannelDimension = ChannelDimension.FIRST,**lowercase_			:	Optional[Any],)->   BatchFeature:





								'''simple docstring'''







								A__   				=       do_resize if do_resize is not None else self.do_resize
								A__   				=       resample if resample is not None else self.resample
								A__   				=       do_center_crop if do_center_crop is not None else self.do_center_crop
								A__   				=       do_rescale if do_rescale is not None else self.do_rescale
								A__   				=       rescale_factor if rescale_factor is not None else self.rescale_factor
								A__   				=       do_normalize if do_normalize is not None else self.do_normalize
								A__   				=       image_mean if image_mean is not None else self.image_mean
								A__   				=       image_std if image_std is not None else self.image_std

								A__   				=       size if size is not None else self.size
								A__   				=       get_size_dict(lowercase_,default_to_square=lowercase_		)
								A__   				=       crop_size if crop_size is not None else self.crop_size
								A__   				=       get_size_dict(lowercase_,param_name='crop_size'		)

								A__   				=       make_list_of_images(lowercase_		)

								if not valid_images(lowercase_		):
												raise ValueError(
												    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
												    'torch.Tensor, tf.Tensor or jax.ndarray.'		)

								if do_resize and size is None:
												raise ValueError('Size must be specified if do_resize is True.'		)

								if do_center_crop and crop_size is None:
												raise ValueError('Crop size must be specified if do_center_crop is True.'		)

								if do_rescale and rescale_factor is None:
												raise ValueError('Rescale factor must be specified if do_rescale is True.'		)

								if do_normalize and (image_mean is None or image_std is None):
												raise ValueError('Image mean and std must be specified if do_normalize is True.'		)

								# All transformations expect numpy arrays.
								A__   				=       [to_numpy_array(lowercase_		) for image in images]

								if do_resize:
												A__   				=       [self.resize(lowercase_,lowercase_,lowercase_		) for image in images]

								if do_center_crop:
												A__   				=       [self.center_crop(lowercase_,lowercase_		) for image in images]

								if do_rescale:
												A__   				=       [self.rescale(lowercase_,lowercase_		) for image in images]

								if do_normalize:
												A__   				=       [self.normalize(lowercase_,lowercase_,lowercase_		) for image in images]

								A__   				=       [to_channel_dimension_format(lowercase_,lowercase_		) for image in images]

								A__   				=       {'pixel_values': images}
								return BatchFeature(data=lowercase_,tensor_type=lowercase_		)




import numpy as np

from transformers import Pipeline
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int				)							->				int:





    '''simple docstring'''






    A__   				=       np.max(SCREAMING_SNAKE_CASE__   ,      axis=-1   ,      keepdims=SCREAMING_SNAKE_CASE__				)
    A__   				=       np.exp(outputs - maxes				)
    return shifted_exp / shifted_exp.sum(axis=-1   ,      keepdims=SCREAMING_SNAKE_CASE__				)


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	Dict,**lowercase_			:	Tuple		)->   Tuple:





        '''simple docstring'''







        A__   				=       {}
        if "second_text" in kwargs:
            A__   				=       kwargs['second_text']
        return preprocess_kwargs, {}, {}


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	int,lowercase_			:	Optional[int]=None		)->   List[str]:





        '''simple docstring'''







        return self.tokenizer(lowercase_,text_pair=lowercase_,return_tensors=self.framework		)


    def 						snake_case__					(				self			:	str,lowercase_			:	Dict		)->   List[str]:





        '''simple docstring'''







        return self.model(**lowercase_		)


    def 						snake_case__					(				self			:	Dict,lowercase_			:	Optional[int]		)->   Dict:





        '''simple docstring'''







        A__   				=       model_outputs.logits[0].numpy()
        A__   				=       softmax(lowercase_		)

        A__   				=       np.argmax(lowercase_		)
        A__   				=       self.model.config.idalabel[best_class]
        A__   				=       probabilities[best_class].item()
        A__   				=       logits.tolist()
        return {"label": label, "score": score, "logits": logits}

