



'''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

lowerCAmelCase_     :       Dict								=						logging.get_logger(__name__)


lowerCAmelCase_     :       int								=						Dict[str, Any]
lowerCAmelCase_     :       Union[str, Any]								=						List[Prediction]
@add_end_docstrings(snake_case_			)
class 	lowerCamelCase_							(					snake_case_			):







						def __init__(  self   :      Optional[int]				,    *lowerCAmelCase__   :      Tuple				,    **lowerCAmelCase__   :      List[str]				):

													"""simple docstring"""
													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 					__lowercase    (  self   :      Dict				,    **lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			{}
													if "threshold" in kwargs:
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			kwargs['''threshold''']
													return {}, {}, postprocess_kwargs







						def __call__(  self   :      Optional[int]				,    *lowerCAmelCase__   :      Any				,    **lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													return super().__call__(*lowerCAmelCase__				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[str]				,    lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			load_image(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			torch.IntTensor([[image.height, image.width]]				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.image_processor(images=[image]				,    return_tensors='''pt'''				)
													if self.tokenizer is not None:
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			self.tokenizer(text=inputs['''words''']				,    boxes=inputs['''boxes''']				,    return_tensors='''pt'''				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			target_size
													return inputs







						def 					__lowercase    (  self   :      Dict				,    lowerCAmelCase__   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			model_inputs.pop('''target_size'''				)
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.model(**lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			outputs.__class__({'''target_size''': target_size, **outputs}				)
													if self.tokenizer is not None:
																				SCREAMING_SNAKE_CASE					:      str     					=			model_inputs['''bbox''']
													return model_outputs







						def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      List[str]=0.9				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			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					:      List[Any]     					=			target_size[0].tolist()

																				def unnormalize(lowerCAmelCase__   :      Optional[int]				):
																											return self._get_bounding_box(
																											    torch.Tensor(
																											        [
																											            (width * bbox[0] / 10_00),
																											            (height * bbox[1] / 10_00),
																											            (width * bbox[2] / 10_00),
																											            (height * bbox[3] / 10_00),
																											        ]				)				)

																				SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model_outputs['''logits'''].squeeze(0				).softmax(dim=-1				).max(dim=-1				)
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[unnormalize(lowerCAmelCase__				) for bbox in model_outputs['''bbox'''].squeeze(0				)]
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			['''score''', '''label''', '''box''']
																				SCREAMING_SNAKE_CASE					:      Any     					=			[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					:      List[str]     					=			self.image_processor.post_process_object_detection(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			raw_annotations[0]
																				SCREAMING_SNAKE_CASE					:      Any     					=			raw_annotation['''scores''']
																				SCREAMING_SNAKE_CASE					:      Dict     					=			raw_annotation['''labels''']
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			raw_annotation['''boxes''']

																				SCREAMING_SNAKE_CASE					:      Dict     					=			scores.tolist()
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			[self.model.config.idalabel[label.item()] for label in labels]
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			[self._get_bounding_box(lowerCAmelCase__				) for box in boxes]

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

													return annotation







						def 					__lowercase    (  self   :      Dict				,    lowerCAmelCase__   :      "torch.Tensor"				):

													"""simple docstring"""
													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					:      Optional[Any]     					=			box.int().tolist()
													SCREAMING_SNAKE_CASE					:      int     					=			{
													    '''xmin''': xmin,
													    '''ymin''': ymin,
													    '''xmax''': xmax,
													    '''ymax''': ymax,
													}
													return bbox









'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_     :       Optional[int]								=						logging.get_logger(__name__)

lowerCAmelCase_     :       Optional[Any]								=						{
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}
class 	lowerCamelCase_							(					snake_case_			):
						_lowerCAmelCase     :   str	     =				'swinv2'

						_lowerCAmelCase     :   Optional[int]	     =				{
						    'num_attention_heads': 'num_heads',
						    'num_hidden_layers': 'num_layers',
						}







						def __init__(  self   :      Optional[int]				,    lowerCAmelCase__   :      Any=2_24				,    lowerCAmelCase__   :      Optional[Any]=4				,    lowerCAmelCase__   :      Tuple=3				,    lowerCAmelCase__   :      Tuple=96				,    lowerCAmelCase__   :      Optional[int]=[2, 2, 6, 2]				,    lowerCAmelCase__   :      Tuple=[3, 6, 12, 24]				,    lowerCAmelCase__   :      List[str]=7				,    lowerCAmelCase__   :      Tuple=4.0				,    lowerCAmelCase__   :      Dict=True				,    lowerCAmelCase__   :      Any=0.0				,    lowerCAmelCase__   :      Optional[int]=0.0				,    lowerCAmelCase__   :      Optional[Any]=0.1				,    lowerCAmelCase__   :      int="gelu"				,    lowerCAmelCase__   :      Tuple=False				,    lowerCAmelCase__   :      Tuple=0.02				,    lowerCAmelCase__   :      Dict=1e-5				,    lowerCAmelCase__   :      List[str]=32				,    **lowerCAmelCase__   :      Union[str, Any]				,    ):

													"""simple docstring"""
													super().__init__(**lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      int     					=			image_size
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			patch_size
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			num_channels
													SCREAMING_SNAKE_CASE					:      int     					=			embed_dim
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			depths
													SCREAMING_SNAKE_CASE					:      str     					=			len(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			num_heads
													SCREAMING_SNAKE_CASE					:      Tuple     					=			window_size
													SCREAMING_SNAKE_CASE					:      Any     					=			mlp_ratio
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			qkv_bias
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			hidden_dropout_prob
													SCREAMING_SNAKE_CASE					:      int     					=			attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE					:      List[str]     					=			drop_path_rate
													SCREAMING_SNAKE_CASE					:      Any     					=			hidden_act
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			use_absolute_embeddings
													SCREAMING_SNAKE_CASE					:      List[str]     					=			layer_norm_eps
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			initializer_range
													SCREAMING_SNAKE_CASE					:      Dict     					=			encoder_stride
													# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
													# this indicates the channel dimension after the last stage of the model
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			int(embed_dim * 2 ** (len(lowerCAmelCase__				) - 1)				)
													SCREAMING_SNAKE_CASE					:      int     					=			(0, 0, 0, 0)




