






"""simple docstring"""
from typing import Dict, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


lowerCamelCase_					=      logging.get_logger(__name__)







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				['''pixel_values''']





	def __init__(      self					: List[str]	,	lowerCAmelCase_					: bool = True	,	lowerCAmelCase_					: Dict[str, int] = None	,	lowerCAmelCase_					: float = None	,	lowerCAmelCase_					: PILImageResampling = PILImageResampling.BILINEAR	,	lowerCAmelCase_					: bool = True	,	lowerCAmelCase_					: Union[int, float] = 1 / 255	,	lowerCAmelCase_					: bool = True	,	lowerCAmelCase_					: Optional[Union[float, List[float]]] = None	,	lowerCAmelCase_					: Optional[Union[float, List[float]]] = None	,	**lowerCAmelCase_					: Optional[int]	,	)						->			None:
				super().__init__(**lowerCAmelCase_						)
				UpperCAmelCase_     : Union[str, Any]			     =					size if size is not None else {"shortest_edge": 384}
				UpperCAmelCase_     : str			     =					get_size_dict(lowerCAmelCase_	,	default_to_square=lowerCAmelCase_						)

				UpperCAmelCase_     : Tuple			     =					do_resize
				UpperCAmelCase_     : Union[str, Any]			     =					size
				# Default value set here for backwards compatibility where the value in config is None
				UpperCAmelCase_     : Union[str, Any]			     =					crop_pct if crop_pct is not None else 224 / 256
				UpperCAmelCase_     : List[Any]			     =					resample
				UpperCAmelCase_     : Dict			     =					do_rescale
				UpperCAmelCase_     : int			     =					rescale_factor
				UpperCAmelCase_     : Tuple			     =					do_normalize
				UpperCAmelCase_     : Union[str, Any]			     =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				UpperCAmelCase_     : str			     =					image_std if image_std is not None else IMAGENET_STANDARD_STD





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: np.ndarray	,	lowerCAmelCase_					: Dict[str, int]	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: PILImageResampling = PILImageResampling.BICUBIC	,	lowerCAmelCase_					: Optional[Union[str, ChannelDimension]] = None	,	**lowerCAmelCase_					: int	,	)						->			np.ndarray:
				UpperCAmelCase_     : int			     =					get_size_dict(lowerCAmelCase_	,	default_to_square=lowerCAmelCase_						)
				if "shortest_edge" not in size:
							raise ValueError(f"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""						)
				UpperCAmelCase_     : Tuple			     =					size["shortest_edge"]

				if shortest_edge < 384:
							# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
							UpperCAmelCase_     : Optional[int]			     =					int(shortest_edge / crop_pct						)
							UpperCAmelCase_     : Any			     =					get_resize_output_image_size(lowerCAmelCase_	,	size=lowerCAmelCase_	,	default_to_square=lowerCAmelCase_						)
							UpperCAmelCase_     : Tuple			     =					resize(image=lowerCAmelCase_	,	size=lowerCAmelCase_	,	resample=lowerCAmelCase_	,	data_format=lowerCAmelCase_	,	**lowerCAmelCase_						)
							# then crop to (shortest_edge, shortest_edge)
							return center_crop(image=lowerCAmelCase_	,	size=(shortest_edge, shortest_edge)	,	data_format=lowerCAmelCase_	,	**lowerCAmelCase_						)
				else:
							# warping (no cropping) when evaluated at 384 or larger
							return resize(
							    lowerCAmelCase_	,	size=(shortest_edge, shortest_edge)	,	resample=lowerCAmelCase_	,	data_format=lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict	,	lowerCAmelCase_					: np.ndarray	,	lowerCAmelCase_					: Union[int, float]	,	lowerCAmelCase_					: Optional[Union[str, ChannelDimension]] = None	,	**lowerCAmelCase_					: List[Any]	,	)						->			str:
				return rescale(lowerCAmelCase_	,	scale=lowerCAmelCase_	,	data_format=lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict	,	lowerCAmelCase_					: np.ndarray	,	lowerCAmelCase_					: Union[float, List[float]]	,	lowerCAmelCase_					: Union[float, List[float]]	,	lowerCAmelCase_					: Optional[Union[str, ChannelDimension]] = None	,	**lowerCAmelCase_					: List[Any]	,	)						->			np.ndarray:
				return normalize(lowerCAmelCase_	,	mean=lowerCAmelCase_	,	std=lowerCAmelCase_	,	data_format=lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: int	,	lowerCAmelCase_					: ImageInput	,	lowerCAmelCase_					: bool = None	,	lowerCAmelCase_					: Dict[str, int] = None	,	lowerCAmelCase_					: float = None	,	lowerCAmelCase_					: PILImageResampling = None	,	lowerCAmelCase_					: bool = None	,	lowerCAmelCase_					: float = None	,	lowerCAmelCase_					: bool = None	,	lowerCAmelCase_					: Optional[Union[float, List[float]]] = None	,	lowerCAmelCase_					: Optional[Union[float, List[float]]] = None	,	lowerCAmelCase_					: Optional[Union[str, TensorType]] = None	,	lowerCAmelCase_					: ChannelDimension = ChannelDimension.FIRST	,	**lowerCAmelCase_					: Optional[Any]	,	)						->			PIL.Image.Image:
				UpperCAmelCase_     : List[Any]			     =					do_resize if do_resize is not None else self.do_resize
				UpperCAmelCase_     : Tuple			     =					crop_pct if crop_pct is not None else self.crop_pct
				UpperCAmelCase_     : Optional[int]			     =					resample if resample is not None else self.resample
				UpperCAmelCase_     : List[str]			     =					do_rescale if do_rescale is not None else self.do_rescale
				UpperCAmelCase_     : List[str]			     =					rescale_factor if rescale_factor is not None else self.rescale_factor
				UpperCAmelCase_     : Union[str, Any]			     =					do_normalize if do_normalize is not None else self.do_normalize
				UpperCAmelCase_     : Any			     =					image_mean if image_mean is not None else self.image_mean
				UpperCAmelCase_     : List[str]			     =					image_std if image_std is not None else self.image_std

				UpperCAmelCase_     : str			     =					size if size is not None else self.size
				UpperCAmelCase_     : Dict			     =					get_size_dict(lowerCAmelCase_	,	default_to_square=lowerCAmelCase_						)

				UpperCAmelCase_     : Union[str, Any]			     =					make_list_of_images(lowerCAmelCase_						)

				if not valid_images(lowerCAmelCase_						):
							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 or resample is None:
							raise ValueError("Size and resample must be specified if do_resize is True."						)

				if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
							raise ValueError("crop_pct must be specified if size < 384."						)

				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.
				UpperCAmelCase_     : Union[str, Any]			     =					[to_numpy_array(lowerCAmelCase_						) for image in images]

				if do_resize:
							UpperCAmelCase_     : Tuple			     =					[self.resize(image=lowerCAmelCase_	,	size=lowerCAmelCase_	,	crop_pct=lowerCAmelCase_	,	resample=lowerCAmelCase_						) for image in images]

				if do_rescale:
							UpperCAmelCase_     : List[Any]			     =					[self.rescale(image=lowerCAmelCase_	,	scale=lowerCAmelCase_						) for image in images]

				if do_normalize:
							UpperCAmelCase_     : Any			     =					[self.normalize(image=lowerCAmelCase_	,	mean=lowerCAmelCase_	,	std=lowerCAmelCase_						) for image in images]

				UpperCAmelCase_     : Any			     =					[to_channel_dimension_format(lowerCAmelCase_	,	lowerCAmelCase_						) for image in images]

				UpperCAmelCase_     : Optional[Any]			     =					{"pixel_values": images}
				return BatchFeature(data=lowerCAmelCase_	,	tensor_type=lowerCAmelCase_						)











"""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

lowerCamelCase_					=      logging.get_logger(__name__)







@add_end_docstrings(__A     )
class 			UpperCamelCase_       (__A     ):





	def __init__(      self					: int	,	*lowerCAmelCase_					: Tuple	,	**lowerCAmelCase_					: List[str]						)						->			Optional[Any]:
				super().__init__(*lowerCAmelCase_	,	**lowerCAmelCase_						)
				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      _SCREAMING_SNAKE_CASE      (      self					: str	,	lowerCAmelCase_					: Optional[int]=None						)						->			List[Any]:
				UpperCAmelCase_     : str			     =					{}
				if top_k is not None:
							UpperCAmelCase_     : List[str]			     =					top_k
				return {}, {}, postprocess_params





	def __call__(      self					: str	,	lowerCAmelCase_					: Union[str, List[str], "Image.Image", List["Image.Image"]]	,	**lowerCAmelCase_					: Any						)						->			Tuple:
				return super().__call__(lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: str	,	lowerCAmelCase_					: str						)						->			Any:
				UpperCAmelCase_     : Tuple			     =					load_image(lowerCAmelCase_						)
				UpperCAmelCase_     : Dict			     =					self.image_processor(images=lowerCAmelCase_	,	return_tensors=self.framework						)
				return model_inputs





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]	,	lowerCAmelCase_					: Dict						)						->			str:
				UpperCAmelCase_     : Any			     =					self.model(**lowerCAmelCase_						)
				return model_outputs





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple	,	lowerCAmelCase_					: Union[str, Any]	,	lowerCAmelCase_					: Optional[int]=5						)						->			Any:
				if top_k > self.model.config.num_labels:
							UpperCAmelCase_     : int			     =					self.model.config.num_labels

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

				UpperCAmelCase_     : int			     =					scores.tolist()
				UpperCAmelCase_     : Optional[Any]			     =					ids.tolist()
				return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCAmelCase_	,	lowerCAmelCase_						)]



