




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       :	Optional[Any]									=     logging.get_logger(__name__)


lowerCamelCase       :	List[Any]									=     Dict[str, Any]
lowerCamelCase       :	int									=     List[Prediction]







@add_end_docstrings(UpperCamelCase     )
class 						A(		UpperCamelCase     ):



	'''simple docstring'''


	def __init__(       self      :						Optional[Any]		,							*A_      :						Optional[Any]		,							**A_      :						List[str]						)     ->			Union[str, Any]:

							"""simple docstring"""






							super().__init__(*__a		,							**__a						)

							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        a__				(       self      :						str		,							**A_      :						Dict						)     ->			List[Any]:

							"""simple docstring"""






							lowerCamelCase_	  =					{}
							if "threshold" in kwargs:
													lowerCamelCase_	  =					kwargs['threshold']
							return {}, {}, postprocess_kwargs

	def __call__(       self      :						Dict		,							*A_      :						Dict		,							**A_      :						List[Any]						)     ->			Union[Predictions, List[Prediction]]:

							"""simple docstring"""






							return super().__call__(*__a		,							**__a						)

	def        a__				(       self      :						Dict		,							A_      :						Any						)     ->			Any:

							"""simple docstring"""






							lowerCamelCase_	  =					load_image(__a						)
							lowerCamelCase_	  =					torch.IntTensor([[image.height, image.width]]						)
							lowerCamelCase_	  =					self.image_processor(images=[image]		,							return_tensors='pt'						)
							if self.tokenizer is not None:
													lowerCamelCase_	  =					self.tokenizer(text=inputs['words']		,							boxes=inputs['boxes']		,							return_tensors='pt'						)
							lowerCamelCase_	  =					target_size
							return inputs

	def        a__				(       self      :						List[Any]		,							A_      :						Optional[Any]						)     ->			int:

							"""simple docstring"""






							lowerCamelCase_	  =					model_inputs.pop('target_size'						)
							lowerCamelCase_	  =					self.model(**__a						)
							lowerCamelCase_	  =					outputs.__class__({'target_size': target_size, **outputs}						)
							if self.tokenizer is not None:
													lowerCamelCase_	  =					model_inputs['bbox']
							return model_outputs

	def        a__				(       self      :						Dict		,							A_      :						Dict		,							A_      :						Union[str, Any]=0.9						)     ->			List[Any]:

							"""simple docstring"""






							lowerCamelCase_	  =					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.
													lowerCamelCase_  ,       lowerCamelCase_	  =					target_size[0].tolist()

													def unnormalize(A_      :						List[Any]						):
																			return self._get_bounding_box(
																			    torch.Tensor(
																			        [
																			            (width * bbox[0] / 1000),
																			            (height * bbox[1] / 1000),
																			            (width * bbox[2] / 1000),
																			            (height * bbox[3] / 1000),
																			        ]						)						)

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

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

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

							return annotation





	def        a__				(       self      :						int		,							A_      :						Any						)     ->			Dict[str, int]:

							"""simple docstring"""






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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase       :	Dict									=     {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	Optional[int]									=     ["ViTFeatureExtractor"]
 lowerCamelCase       :	Dict									=     ["ViTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	int									=     [
     "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTForImageClassification",
     "ViTForMaskedImageModeling",
     "ViTModel",
     "ViTPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	Tuple									=     [
     "TFViTForImageClassification",
     "TFViTModel",
     "TFViTPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	Dict									=     [
     "FlaxViTForImageClassification",
     "FlaxViTModel",
     "FlaxViTPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_vit import ViTFeatureExtractor
  from .image_processing_vit import ViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit import (
      VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTForImageClassification,
      ViTForMaskedImageModeling,
      ViTModel,
      ViTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
 import sys

 lowerCamelCase       :	Any									=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



