




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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__UpperCAmelCase     							=						logging.get_logger(__name__)



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Optional[int]							=  ['''pixel_values''']



		def __init__(							self    ,		_UpperCamelCase = True    ,		_UpperCamelCase = None    ,		_UpperCamelCase = PILImageResampling.BICUBIC    ,		_UpperCamelCase = True    ,		_UpperCamelCase = True    ,		_UpperCamelCase = 1 / 2_5_5    ,		_UpperCamelCase = None    ,		_UpperCamelCase = True    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		**_UpperCamelCase    ,		)     ->							None:
							super().__init__(**_UpperCamelCase       )
							UpperCAmelCase_   :				str						=						size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
							UpperCAmelCase_   :				List[Any]						=						get_size_dict(_UpperCamelCase       )
							UpperCAmelCase_   :				str						=						crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
							UpperCAmelCase_   :				Optional[int]						=						get_size_dict(_UpperCamelCase    ,		default_to_square=_UpperCamelCase    ,		param_name='crop_size'       )

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



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = PILImageResampling.BILINEAR    ,		_UpperCamelCase = None    ,		**_UpperCamelCase    ,		)     ->							np.ndarray:
							UpperCAmelCase_   :				int						=						get_size_dict(_UpperCamelCase       )

							if "shortest_edge" in size:
												UpperCAmelCase_   :				int						=						get_resize_output_image_size(_UpperCamelCase    ,		size=size['shortest_edge']    ,		default_to_square=_UpperCamelCase       )
												# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
							elif "height" in size and "width" in size:
												UpperCAmelCase_   :				Tuple						=						(size['height'], size['width'])
							else:
												raise ValueError(f"Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"       )
							return resize(_UpperCamelCase    ,		size=_UpperCamelCase    ,		resample=_UpperCamelCase    ,		data_format=_UpperCamelCase    ,		**_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		**_UpperCamelCase    ,		)     ->							np.ndarray:
							UpperCAmelCase_   :				Any						=						get_size_dict(_UpperCamelCase       )
							if "height" not in size or "width" not in size:
												raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}"       )
							return center_crop(_UpperCamelCase    ,		size=(size['height'], size['width'])    ,		data_format=_UpperCamelCase    ,		**_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		**_UpperCamelCase       )     ->							np.ndarray:
							return rescale(_UpperCamelCase    ,		scale=_UpperCamelCase    ,		data_format=_UpperCamelCase    ,		**_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		**_UpperCamelCase    ,		)     ->							np.ndarray:
							return normalize(_UpperCamelCase    ,		mean=_UpperCamelCase    ,		std=_UpperCamelCase    ,		data_format=_UpperCamelCase    ,		**_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = ChannelDimension.FIRST    ,		**_UpperCamelCase    ,		)     ->							BatchFeature:
							UpperCAmelCase_   :				Tuple						=						do_resize if do_resize is not None else self.do_resize
							UpperCAmelCase_   :				Any						=						do_rescale if do_rescale is not None else self.do_rescale
							UpperCAmelCase_   :				int						=						do_normalize if do_normalize is not None else self.do_normalize
							UpperCAmelCase_   :				Union[str, Any]						=						do_center_crop if do_center_crop is not None else self.do_center_crop
							UpperCAmelCase_   :				Tuple						=						crop_size if crop_size is not None else self.crop_size
							UpperCAmelCase_   :				Optional[int]						=						get_size_dict(_UpperCamelCase    ,		param_name='crop_size'    ,		default_to_square=_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						resample if resample is not None else self.resample
							UpperCAmelCase_   :				List[str]						=						rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCAmelCase_   :				List[Any]						=						image_mean if image_mean is not None else self.image_mean
							UpperCAmelCase_   :				Any						=						image_std if image_std is not None else self.image_std

							UpperCAmelCase_   :				List[str]						=						size if size is not None else self.size
							UpperCAmelCase_   :				Union[str, Any]						=						get_size_dict(_UpperCamelCase       )

							if not is_batched(_UpperCamelCase       ):
												UpperCAmelCase_   :				Dict						=						[images]

							if not valid_images(_UpperCamelCase       ):
												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.'       )

							# All transformations expect numpy arrays.
							UpperCAmelCase_   :				Any						=						[to_numpy_array(_UpperCamelCase       ) for image in images]

							if do_resize:
												UpperCAmelCase_   :				List[Any]						=						[self.resize(image=_UpperCamelCase    ,		size=_UpperCamelCase    ,		resample=_UpperCamelCase       ) for image in images]

							if do_center_crop:
												UpperCAmelCase_   :				Tuple						=						[self.center_crop(image=_UpperCamelCase    ,		size=_UpperCamelCase       ) for image in images]

							if do_rescale:
												UpperCAmelCase_   :				Union[str, Any]						=						[self.rescale(image=_UpperCamelCase    ,		scale=_UpperCamelCase       ) for image in images]

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

							UpperCAmelCase_   :				Tuple						=						[to_channel_dimension_format(_UpperCamelCase    ,		_UpperCamelCase       ) for image in images]

							UpperCAmelCase_   :				Tuple						=						{'pixel_values': images}
							return BatchFeature(data=_UpperCamelCase    ,		tensor_type=_UpperCamelCase       )














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

__UpperCAmelCase     							=						logging.get_logger(__name__)


__UpperCAmelCase     							=						Dict[str, Any]
__UpperCAmelCase     							=						List[Prediction]



@add_end_docstrings(_snake_case      )
class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		def __init__(							self    ,		*_UpperCamelCase    ,		**_UpperCamelCase       )     ->							Optional[int]:
							super().__init__(*_UpperCamelCase    ,		**_UpperCamelCase       )

							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  __UpperCAmelCase      (							self    ,		**_UpperCamelCase       )     ->							Tuple:
							UpperCAmelCase_   :				str						=						{}
							if "threshold" in kwargs:
												UpperCAmelCase_   :				Tuple						=						kwargs['threshold']
							return {}, {}, postprocess_kwargs



		def __call__(							self    ,		*_UpperCamelCase    ,		**_UpperCamelCase       )     ->							Union[Predictions, List[Prediction]]:
							return super().__call__(*_UpperCamelCase    ,		**_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Dict:
							UpperCAmelCase_   :				Any						=						load_image(_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						torch.IntTensor([[image.height, image.width]]       )
							UpperCAmelCase_   :				str						=						self.image_processor(images=[image]    ,		return_tensors='pt'       )
							if self.tokenizer is not None:
												UpperCAmelCase_   :				List[str]						=						self.tokenizer(text=inputs['words']    ,		boxes=inputs['boxes']    ,		return_tensors='pt'       )
							UpperCAmelCase_   :				Any						=						target_size
							return inputs



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							List[Any]:
							UpperCAmelCase_   :				Any						=						model_inputs.pop('target_size'       )
							UpperCAmelCase_   :				Optional[Any]						=						self.model(**_UpperCamelCase       )
							UpperCAmelCase_   :				Dict						=						outputs.__class__({'target_size': target_size, **outputs}       )
							if self.tokenizer is not None:
												UpperCAmelCase_   :				List[str]						=						model_inputs['bbox']
							return model_outputs



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase=0.9       )     ->							List[str]:
							UpperCAmelCase_   :				List[Any]						=						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.
												UpperCAmelCase_	,  UpperCAmelCase_   :				str						=						target_size[0].tolist()

												def unnormalize(_UpperCamelCase       ):
																	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),
																	        ]       )       )

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

												UpperCAmelCase_   :				Union[str, Any]						=						scores.tolist()
												UpperCAmelCase_   :				int						=						[self.model.config.idalabel[label.item()] for label in labels]
												UpperCAmelCase_   :				Any						=						[self._get_bounding_box(_UpperCamelCase       ) for box in boxes]

												# {"scores": [...], ...} --> [{"score":x, ...}, ...]
												UpperCAmelCase_   :				int						=						['score', 'label', 'box']
												UpperCAmelCase_   :				Dict						=						[
												    dict(zip(_UpperCamelCase    ,		_UpperCamelCase       )       )
												    for vals in zip(raw_annotation['scores']    ,		raw_annotation['labels']    ,		raw_annotation['boxes']       )
												]

							return annotation



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Dict[str, int]:
							if self.framework != "pt":
												raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.'       )
							UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Dict						=						box.int().tolist()
							UpperCAmelCase_   :				str						=						{
							    'xmin': xmin,
							    'ymin': ymin,
							    'xmax': xmax,
							    'ymax': ymax,
							}
							return bbox






