
"""simple docstring"""





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, Pipeline


if is_vision_available():
							from ..image_utils import load_image


if is_torch_available():
							import torch

							from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__magic_name__       =			logging.get_logger(__name__)


__magic_name__       =			Dict[str, Any]
__magic_name__       =			List[Prediction]
@add_end_docstrings(__a )
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		def __init__(     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				super().__init__(*lowerCAmelCase__							,  **lowerCAmelCase__)

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

				requires_backends(self							,  """vision""")
				self.check_model_type(
				    dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()))




		def 	snake_case_     (     self							,  **lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = {}
				if "threshold" in kwargs:
						__SCREAMING_SNAKE_CASE            = kwargs["""threshold"""]
				return {}, {}, postprocess_kwargs




		def __call__(     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				return super().__call__(*lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = load_image(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = torch.IntTensor([[image.height, image.width]])
				__SCREAMING_SNAKE_CASE            = self.image_processor(images=[image]							,  return_tensors="""pt""")
				if self.tokenizer is not None:
						__SCREAMING_SNAKE_CASE            = self.tokenizer(text=inputs["""words"""]							,  boxes=inputs["""boxes"""]							,  return_tensors="""pt""")
				__SCREAMING_SNAKE_CASE            = target_size
				return inputs




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = model_inputs.pop("""target_size""")
				__SCREAMING_SNAKE_CASE            = self.model(**lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = outputs.__class__({"""target_size""": target_size, **outputs})
				if self.tokenizer is not None:
						__SCREAMING_SNAKE_CASE            = model_inputs["""bbox"""]
				return model_outputs




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__=0.9):
				__SCREAMING_SNAKE_CASE            = model_outputs["""target_size"""]
				if self.tokenizer is not None:
						# This is a LayoutLMForTokenClassification variant.
						# The OCR got the boxes and the model classified the words.
						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = target_size[0].tolist()

						def unnormalize(lowerCAmelCase__):
								return self._get_bounding_box(
								    torch.Tensor(
								        [
								            (width * bbox[0] / 1_0_0_0),
								            (height * bbox[1] / 1_0_0_0),
								            (width * bbox[2] / 1_0_0_0),
								            (height * bbox[3] / 1_0_0_0),
								        ]))

						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = model_outputs["""logits"""].squeeze(0).softmax(dim=-1).max(dim=-1)
						__SCREAMING_SNAKE_CASE            = [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
						__SCREAMING_SNAKE_CASE            = [unnormalize(lowerCAmelCase__) for bbox in model_outputs["""bbox"""].squeeze(0)]
						__SCREAMING_SNAKE_CASE            = ["""score""", """label""", """box"""]
						__SCREAMING_SNAKE_CASE            = [dict(zip(lowerCAmelCase__							,  lowerCAmelCase__)) for vals in zip(scores.tolist()							,  lowerCAmelCase__							,  lowerCAmelCase__) if vals[0] > threshold]
				else:
						# This is a regular ForObjectDetectionModel
						__SCREAMING_SNAKE_CASE            = self.image_processor.post_process_object_detection(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__)
						__SCREAMING_SNAKE_CASE            = raw_annotations[0]
						__SCREAMING_SNAKE_CASE            = raw_annotation["""scores"""]
						__SCREAMING_SNAKE_CASE            = raw_annotation["""labels"""]
						__SCREAMING_SNAKE_CASE            = raw_annotation["""boxes"""]

						__SCREAMING_SNAKE_CASE            = scores.tolist()
						__SCREAMING_SNAKE_CASE            = [self.model.config.idalabel[label.item()] for label in labels]
						__SCREAMING_SNAKE_CASE            = [self._get_bounding_box(lowerCAmelCase__) for box in boxes]

						# {"scores": [...], ...} --> [{"score":x, ...}, ...]
						__SCREAMING_SNAKE_CASE            = ["""score""", """label""", """box"""]
						__SCREAMING_SNAKE_CASE            = [
						    dict(zip(lowerCAmelCase__							,  lowerCAmelCase__))
						    for vals in zip(raw_annotation["""scores"""]							,  raw_annotation["""labels"""]							,  raw_annotation["""boxes"""])
						]

				return annotation




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				if self.framework != "pt":
						raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch.""")
				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = box.int().tolist()
				__SCREAMING_SNAKE_CASE            = {
				    """xmin""": xmin,
				    """ymin""": ymin,
				    """xmax""": xmax,
				    """ymax""": ymax,
				}
				return bbox








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, logging


UpperCAmelCase_      : Optional[int]  					=       logging.get_logger(__name__)
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Union[str, Any]        =						['''pixel_values''']
      def __init__(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       Optional[Dict[str, int]] = None     ,							SCREAMING_SNAKE_CASE__				:       PILImageResampling = PILImageResampling.BILINEAR     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int] = None     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       Union[int, float] = 1 / 2_5_5     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[float, List[float]]] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[float, List[float]]] = None     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							)		->			None:
             super().__init__(**SCREAMING_SNAKE_CASE__     )
             a_					:		str							=  size if size is not None else {'shortest_edge': 2_5_6}
             a_					:		Any							=  get_size_dict(SCREAMING_SNAKE_CASE__     ,							default_to_square=SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
             a_					:		Optional[int]							=  get_size_dict(SCREAMING_SNAKE_CASE__     )
             a_					:		List[str]							=  do_resize
             a_					:		Dict							=  size
             a_					:		Optional[Any]							=  resample
             a_					:		Optional[int]							=  do_center_crop
             a_					:		Dict							=  crop_size
             a_					:		int							=  do_rescale
             a_					:		int							=  rescale_factor
             a_					:		Tuple							=  do_normalize
             a_					:		int							=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             a_					:		Tuple							=  image_std if image_std is not None else IMAGENET_STANDARD_STD
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       np.ndarray     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int]     ,							SCREAMING_SNAKE_CASE__				:       PILImageResampling = PILImageResampling.BICUBIC     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, ChannelDimension]] = None     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							)		->			np.ndarray:
             a_					:		List[Any]							=  get_size_dict(SCREAMING_SNAKE_CASE__     ,							default_to_square=SCREAMING_SNAKE_CASE__     )
             if "shortest_edge" not in size:
                    raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""     )
             a_					:		Tuple							=  get_resize_output_image_size(SCREAMING_SNAKE_CASE__     ,							size=size['shortest_edge']     ,							default_to_square=SCREAMING_SNAKE_CASE__     )
             return resize(SCREAMING_SNAKE_CASE__     ,							size=SCREAMING_SNAKE_CASE__     ,							resample=SCREAMING_SNAKE_CASE__     ,							data_format=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       np.ndarray     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, ChannelDimension]] = None     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							)		->			np.ndarray:
             a_					:		str							=  get_size_dict(SCREAMING_SNAKE_CASE__     )
             return center_crop(SCREAMING_SNAKE_CASE__     ,							size=(size['height'], size['width'])     ,							data_format=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       np.ndarray     ,							SCREAMING_SNAKE_CASE__				:       float     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, ChannelDimension]] = None     ,							**SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			np.ndarray:
             return rescale(SCREAMING_SNAKE_CASE__     ,							scale=SCREAMING_SNAKE_CASE__     ,							data_format=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       np.ndarray     ,							SCREAMING_SNAKE_CASE__				:       Union[float, List[float]]     ,							SCREAMING_SNAKE_CASE__				:       Union[float, List[float]]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, ChannelDimension]] = None     ,							**SCREAMING_SNAKE_CASE__				:       List[str]     ,							)		->			np.ndarray:
             return normalize(SCREAMING_SNAKE_CASE__     ,							mean=SCREAMING_SNAKE_CASE__     ,							std=SCREAMING_SNAKE_CASE__     ,							data_format=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       ImageInput     ,							SCREAMING_SNAKE_CASE__				:       Optional[bool] = None     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int] = None     ,							SCREAMING_SNAKE_CASE__				:       PILImageResampling = None     ,							SCREAMING_SNAKE_CASE__				:       bool = None     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[bool] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[float] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[bool] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[float, List[float]]] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[float, List[float]]] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, TensorType]] = None     ,							SCREAMING_SNAKE_CASE__				:       Union[str, ChannelDimension] = ChannelDimension.FIRST     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							)		->			Union[str, Any]:
             a_					:		List[str]							=  do_resize if do_resize is not None else self.do_resize
             a_					:		Dict							=  size if size is not None else self.size
             a_					:		Dict							=  get_size_dict(SCREAMING_SNAKE_CASE__     ,							default_to_square=SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  resample if resample is not None else self.resample
             a_					:		List[Any]							=  do_center_crop if do_center_crop is not None else self.do_center_crop
             a_					:		int							=  crop_size if crop_size is not None else self.crop_size
             a_					:		Optional[int]							=  get_size_dict(SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  do_rescale if do_rescale is not None else self.do_rescale
             a_					:		Dict							=  rescale_factor if rescale_factor is not None else self.rescale_factor
             a_					:		Any							=  do_normalize if do_normalize is not None else self.do_normalize
             a_					:		str							=  image_mean if image_mean is not None else self.image_mean
             a_					:		Dict							=  image_std if image_std is not None else self.image_std

             a_					:		Optional[int]							=  make_list_of_images(SCREAMING_SNAKE_CASE__     )

             if not valid_images(SCREAMING_SNAKE_CASE__     ):
                    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_					:		Any							=  [to_numpy_array(SCREAMING_SNAKE_CASE__     ) for image in images]

             if do_resize:
                    a_					:		str							=  [self.resize(image=SCREAMING_SNAKE_CASE__     ,							size=SCREAMING_SNAKE_CASE__     ,							resample=SCREAMING_SNAKE_CASE__     ) for image in images]

             if do_center_crop:
                    a_					:		int							=  [self.center_crop(image=SCREAMING_SNAKE_CASE__     ,							size=SCREAMING_SNAKE_CASE__     ) for image in images]

             if do_rescale:
                    a_					:		Optional[Any]							=  [self.rescale(image=SCREAMING_SNAKE_CASE__     ,							scale=SCREAMING_SNAKE_CASE__     ) for image in images]

             if do_normalize:
                    a_					:		List[Any]							=  [self.normalize(image=SCREAMING_SNAKE_CASE__     ,							mean=SCREAMING_SNAKE_CASE__     ,							std=SCREAMING_SNAKE_CASE__     ) for image in images]

             a_					:		Dict							=  [to_channel_dimension_format(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ) for image in images]

             a_					:		Tuple							=  {'pixel_values': images}
             return BatchFeature(data=SCREAMING_SNAKE_CASE__     ,							tensor_type=SCREAMING_SNAKE_CASE__     )


