


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


UpperCAmelCase_		:				List[Any]									=     logging.get_logger(__name__)




class 		__A      (			a__			):
						UpperCamelCase     						=					["""pixel_values"""]

						def __init__(		self   :Dict    , __snake_case   :Union[str, Any] = True    , __snake_case   :Tuple = None    , __snake_case   :Any = PILImageResampling.BICUBIC    , __snake_case   :Optional[Any] = True    , __snake_case   :Optional[Any] = None    , __snake_case   :List[Any] = True    , __snake_case   :List[str] = 1 / 2_55    , __snake_case   :Tuple = True    , __snake_case   :int = IMAGENET_DEFAULT_MEAN    , __snake_case   :Tuple = IMAGENET_DEFAULT_STD    , **__snake_case   :Optional[int]    , ):
										'''simple docstring'''

										super().__init__(**lowercase__ )
										__magic_name__	:	Any								=size if size is not None else {'''shortest_edge''': 2_24}
										__magic_name__	:	Union[str, Any]								=get_size_dict(lowercase__    , default_to_square=lowercase__ )
										__magic_name__	:	Optional[int]								=crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
										__magic_name__	:	str								=get_size_dict(lowercase__    , param_name="""crop_size""" )

										__magic_name__	:	Optional[Any]								=do_resize
										__magic_name__	:	Tuple								=size
										__magic_name__	:	Dict								=resample
										__magic_name__	:	Optional[int]								=do_center_crop
										__magic_name__	:	List[str]								=crop_size
										__magic_name__	:	int								=do_rescale
										__magic_name__	:	Dict								=rescale_factor
										__magic_name__	:	List[str]								=do_normalize
										__magic_name__	:	Tuple								=image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
										__magic_name__	:	List[Any]								=image_std if image_std is not None else IMAGENET_DEFAULT_STD

						def     A__						(		self   :List[str]    , __snake_case   :Any    , __snake_case   :List[str]    , __snake_case   :Any = PILImageResampling.BICUBIC    , __snake_case   :Optional[Any] = None    , **__snake_case   :Union[str, Any]    , ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=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:
														__magic_name__	:	Union[str, Any]								=int((2_56 / 2_24) * size["""shortest_edge"""] )
														__magic_name__	:	Tuple								=get_resize_output_image_size(lowercase__    , size=lowercase__    , default_to_square=lowercase__ )
														__magic_name__	:	str								={'''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     A__						(		self   :Optional[int]    , __snake_case   :int    , __snake_case   :Optional[Any]    , __snake_case   :Tuple = None    , **__snake_case   :str    , ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=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     A__						(		self   :str    , __snake_case   :int    , __snake_case   :Union[str, Any]    , __snake_case   :Optional[int] = None    , **__snake_case   :Optional[Any]    , ):
										'''simple docstring'''

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

						def     A__						(		self   :List[Any]    , __snake_case   :Union[str, Any]    , __snake_case   :Any    , __snake_case   :List[str]    , __snake_case   :Optional[int] = None    , **__snake_case   :Tuple    , ):
										'''simple docstring'''

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


						def     A__						(		self   :Dict    , __snake_case   :Optional[int]    , __snake_case   :Tuple = None    , __snake_case   :Dict = None    , __snake_case   :Dict = None    , __snake_case   :Any = None    , __snake_case   :Dict = None    , __snake_case   :Union[str, Any] = None    , __snake_case   :Dict = None    , __snake_case   :int = None    , __snake_case   :Union[str, Any] = None    , __snake_case   :str = None    , __snake_case   :List[str] = None    , __snake_case   :List[str] = ChannelDimension.FIRST    , **__snake_case   :Optional[int]    , ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=do_resize if do_resize is not None else self.do_resize
										__magic_name__	:	Dict								=resample if resample is not None else self.resample
										__magic_name__	:	Any								=do_center_crop if do_center_crop is not None else self.do_center_crop
										__magic_name__	:	Optional[Any]								=do_rescale if do_rescale is not None else self.do_rescale
										__magic_name__	:	Optional[int]								=rescale_factor if rescale_factor is not None else self.rescale_factor
										__magic_name__	:	List[str]								=do_normalize if do_normalize is not None else self.do_normalize
										__magic_name__	:	List[Any]								=image_mean if image_mean is not None else self.image_mean
										__magic_name__	:	Union[str, Any]								=image_std if image_std is not None else self.image_std

										__magic_name__	:	Tuple								=size if size is not None else self.size
										__magic_name__	:	Optional[Any]								=get_size_dict(lowercase__    , default_to_square=lowercase__ )
										__magic_name__	:	Union[str, Any]								=crop_size if crop_size is not None else self.crop_size
										__magic_name__	:	str								=get_size_dict(lowercase__    , param_name="""crop_size""" )

										__magic_name__	:	str								=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.
										__magic_name__	:	List[Any]								=[to_numpy_array(lowercase__ ) for image in images]

										if do_resize:
														__magic_name__	:	Optional[int]								=[self.resize(lowercase__    , lowercase__    , lowercase__ ) for image in images]

										if do_center_crop:
														__magic_name__	:	Tuple								=[self.center_crop(lowercase__    , lowercase__ ) for image in images]

										if do_rescale:
														__magic_name__	:	Optional[Any]								=[self.rescale(lowercase__    , lowercase__ ) for image in images]

										if do_normalize:
														__magic_name__	:	str								=[self.normalize(lowercase__    , lowercase__    , lowercase__ ) for image in images]

										__magic_name__	:	int								=[to_channel_dimension_format(lowercase__    , lowercase__ ) for image in images]

										__magic_name__	:	Dict								={'''pixel_values''': images}
										return BatchFeature(data=lowercase__    , tensor_type=lowercase__ )










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

UpperCAmelCase_		:				str									=     logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase__			)
class 		__A      (			UpperCamelCase__			):

						def __init__(		self   :Any    , *__snake_case   :List[Any]    , **__snake_case   :Union[str, Any] ):
										'''simple docstring'''

										super().__init__(*__snake_case    , **__snake_case )
										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     A__						(		self   :int    , __snake_case   :Optional[Any]=None ):
										'''simple docstring'''

										__magic_name__	:	Any								={}
										if top_k is not None:
														__magic_name__	:	str								=top_k
										return {}, {}, postprocess_params

						def __call__(		self   :Tuple    , __snake_case   :Union[str, List[str], "Image.Image", List["Image.Image"]]    , **__snake_case   :Union[str, Any] ):
										'''simple docstring'''

										return super().__call__(__snake_case    , **__snake_case )

						def     A__						(		self   :str    , __snake_case   :Any ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=load_image(__snake_case )
										__magic_name__	:	Any								=self.image_processor(images=__snake_case    , return_tensors=self.framework )
										return model_inputs

						def     A__						(		self   :int    , __snake_case   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	str								=self.model(**__snake_case )
										return model_outputs


						def     A__						(		self   :List[str]    , __snake_case   :int    , __snake_case   :Union[str, Any]=5 ):
										'''simple docstring'''

										if top_k > self.model.config.num_labels:
														__magic_name__	:	Dict								=self.model.config.num_labels

										if self.framework == "pt":
														__magic_name__	:	int								=model_outputs.logits.softmax(-1 )[0]
														__magic_name__				, __magic_name__	:	str								=probs.topk(__snake_case )
										elif self.framework == "tf":
														__magic_name__	:	Union[str, Any]								=stable_softmax(model_outputs.logits    , axis=-1 )[0]
														__magic_name__	:	List[str]								=tf.math.top_k(__snake_case    , k=__snake_case )
														__magic_name__				, __magic_name__	:	str								=topk.values.numpy(), topk.indices.numpy()
										else:
														raise ValueError(f"Unsupported framework: {self.framework}" )

										__magic_name__	:	str								=scores.tolist()
										__magic_name__	:	Union[str, Any]								=ids.tolist()
										return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__snake_case    , __snake_case )]




