

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_       	=	logging.get_logger(__name__)


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





@add_end_docstrings(__A					)
class 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				str							,   *_UpperCamelCase :				Tuple							,   **_UpperCamelCase :				Optional[Any]							)      ->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 				snake_case__(     self :				Optional[Any]							,   **_UpperCamelCase :				Any							)      ->Optional[int]:
									snake_case_   =	{}
									if "threshold" in kwargs:
															snake_case_   =	kwargs['''threshold''']
									return {}, {}, postprocess_kwargs







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







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







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







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

															def unnormalize(_UpperCamelCase :				int							):
																					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),
																					        ]							)							)

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

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

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

									return annotation







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


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


lowerCAmelCase_       	=	logging.get_logger(__name__)

lowerCAmelCase_       	=	{
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   List[Any]									=				"vit_msn"







			def __init__(     self :				Dict							,   _UpperCamelCase :				Optional[int]=7_6_8							,   _UpperCamelCase :				Optional[Any]=1_2							,   _UpperCamelCase :				Union[str, Any]=1_2							,   _UpperCamelCase :				str=3_0_7_2							,   _UpperCamelCase :				Tuple="gelu"							,   _UpperCamelCase :				Dict=0.0							,   _UpperCamelCase :				Dict=0.0							,   _UpperCamelCase :				List[str]=0.02							,   _UpperCamelCase :				List[Any]=1e-06							,   _UpperCamelCase :				Any=2_2_4							,   _UpperCamelCase :				Optional[Any]=1_6							,   _UpperCamelCase :				Any=3							,   _UpperCamelCase :				str=True							,   **_UpperCamelCase :				Any							,   )      ->int:
									super().__init__(**_UpperCamelCase							)

									snake_case_   =	hidden_size
									snake_case_   =	num_hidden_layers
									snake_case_   =	num_attention_heads
									snake_case_   =	intermediate_size
									snake_case_   =	hidden_act
									snake_case_   =	hidden_dropout_prob
									snake_case_   =	attention_probs_dropout_prob
									snake_case_   =	initializer_range
									snake_case_   =	layer_norm_eps
									snake_case_   =	image_size
									snake_case_   =	patch_size
									snake_case_   =	num_channels
									snake_case_   =	qkv_bias