





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

SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)


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

@add_end_docstrings(lowerCAmelCase_   )
class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     def __init__(							self						:    Optional[Any]						,*lowerCamelCase__						:    str						,**lowerCamelCase__						:    str       )     ->		Tuple:




           '''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 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]						,**lowerCamelCase__						:    int       )     ->		List[Any]:




           '''simple docstring'''


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






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




           '''simple docstring'''


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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]						,lowerCamelCase__						:    List[Any]       )     ->		List[Any]:




           '''simple docstring'''


           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 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple						,lowerCamelCase__						:    str       )     ->		Dict:




           '''simple docstring'''


           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 				SCREAMING_SNAKE_CASE__  (							self						:    List[Any]						,lowerCamelCase__						:    Tuple						,lowerCamelCase__						:    int=0.9       )     ->		int:




           '''simple docstring'''


           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__						:    int       ):
                       return self._get_bounding_box(
                           torch.Tensor(
                               [
                                   (width * bbox[0] / 1000),
                                   (height * bbox[1] / 1000),
                                   (width * bbox[2] / 1000),
                                   (height * bbox[3] / 1000),
                               ]       )       )

                 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 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]						,lowerCamelCase__						:    "torch.Tensor"       )     ->		Dict[str, int]:




           '''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		=						box.int().tolist()
           SCREAMING_SNAKE_CASE		=						{
               """xmin""": xmin,
               """ymin""": ymin,
               """xmax""": xmax,
               """ymax""": ymax,
           }
           return bbox









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						   =       {
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Union[str, Any]			=			"table-transformer"
     __snake_case    :	Union[str, Any]			=			["past_key_values"]
     __snake_case    :	List[Any]			=			{
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }






     def __init__(							self						:    Optional[int]						,lowerCamelCase__						:    Optional[Any]=True						,lowerCamelCase__						:    Optional[Any]=None						,lowerCamelCase__						:    List[Any]=3						,lowerCamelCase__						:    Optional[int]=100						,lowerCamelCase__						:    List[Any]=6						,lowerCamelCase__						:    Dict=2048						,lowerCamelCase__						:    List[Any]=8						,lowerCamelCase__						:    Dict=6						,lowerCamelCase__						:    Dict=2048						,lowerCamelCase__						:    Any=8						,lowerCamelCase__						:    Optional[int]=0.0						,lowerCamelCase__						:    int=0.0						,lowerCamelCase__						:    List[Any]=True						,lowerCamelCase__						:    Optional[int]="relu"						,lowerCamelCase__						:    Tuple=256						,lowerCamelCase__						:    Any=0.1						,lowerCamelCase__						:    Optional[Any]=0.0						,lowerCamelCase__						:    Tuple=0.0						,lowerCamelCase__						:    List[Any]=0.02						,lowerCamelCase__						:    int=1.0						,lowerCamelCase__						:    List[str]=False						,lowerCamelCase__						:    Optional[Any]="sine"						,lowerCamelCase__						:    List[str]="resnet50"						,lowerCamelCase__						:    Optional[Any]=True						,lowerCamelCase__						:    List[str]=False						,lowerCamelCase__						:    int=1						,lowerCamelCase__						:    Dict=5						,lowerCamelCase__						:    Tuple=2						,lowerCamelCase__						:    Union[str, Any]=1						,lowerCamelCase__						:    str=1						,lowerCamelCase__						:    Any=5						,lowerCamelCase__						:    Tuple=2						,lowerCamelCase__						:    str=0.1						,**lowerCamelCase__						:    List[str]						,)     ->		Optional[int]:




           '''simple docstring'''


           if backbone_config is not None and use_timm_backbone:
                 raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""       )

           if not use_timm_backbone:
                 if backbone_config is None:
                       logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""       )
                       SCREAMING_SNAKE_CASE		=						CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]       )
                 elif isinstance(lowerCamelCase__						,lowerCamelCase__       ):
                       SCREAMING_SNAKE_CASE		=						backbone_config.get("""model_type"""       )
                       SCREAMING_SNAKE_CASE		=						CONFIG_MAPPING[backbone_model_type]
                       SCREAMING_SNAKE_CASE		=						config_class.from_dict(lowerCamelCase__       )
                 # set timm attributes to None
                 SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						None, None, None

           SCREAMING_SNAKE_CASE		=						use_timm_backbone
           SCREAMING_SNAKE_CASE		=						backbone_config
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						num_queries
           SCREAMING_SNAKE_CASE		=						d_model
           SCREAMING_SNAKE_CASE		=						encoder_ffn_dim
           SCREAMING_SNAKE_CASE		=						encoder_layers
           SCREAMING_SNAKE_CASE		=						encoder_attention_heads
           SCREAMING_SNAKE_CASE		=						decoder_ffn_dim
           SCREAMING_SNAKE_CASE		=						decoder_layers
           SCREAMING_SNAKE_CASE		=						decoder_attention_heads
           SCREAMING_SNAKE_CASE		=						dropout
           SCREAMING_SNAKE_CASE		=						attention_dropout
           SCREAMING_SNAKE_CASE		=						activation_dropout
           SCREAMING_SNAKE_CASE		=						activation_function
           SCREAMING_SNAKE_CASE		=						init_std
           SCREAMING_SNAKE_CASE		=						init_xavier_std
           SCREAMING_SNAKE_CASE		=						encoder_layerdrop
           SCREAMING_SNAKE_CASE		=						decoder_layerdrop
           SCREAMING_SNAKE_CASE		=						encoder_layers
           SCREAMING_SNAKE_CASE		=						auxiliary_loss
           SCREAMING_SNAKE_CASE		=						position_embedding_type
           SCREAMING_SNAKE_CASE		=						backbone
           SCREAMING_SNAKE_CASE		=						use_pretrained_backbone
           SCREAMING_SNAKE_CASE		=						dilation
           # Hungarian matcher
           SCREAMING_SNAKE_CASE		=						class_cost
           SCREAMING_SNAKE_CASE		=						bbox_cost
           SCREAMING_SNAKE_CASE		=						giou_cost
           # Loss coefficients
           SCREAMING_SNAKE_CASE		=						mask_loss_coefficient
           SCREAMING_SNAKE_CASE		=						dice_loss_coefficient
           SCREAMING_SNAKE_CASE		=						bbox_loss_coefficient
           SCREAMING_SNAKE_CASE		=						giou_loss_coefficient
           SCREAMING_SNAKE_CASE		=						eos_coefficient
           super().__init__(is_encoder_decoder=lowerCamelCase__						,**lowerCamelCase__       )






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		int:




           '''simple docstring'''


           return self.encoder_attention_heads






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		int:




           '''simple docstring'''


           return self.d_model

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	int			=			version.parse("1.11"   )






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		Mapping[str, Mapping[int, str]]:




           '''simple docstring'''


           return OrderedDict(
               [
                   ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                   ("""pixel_mask""", {0: """batch"""}),
               ]       )






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		float:




           '''simple docstring'''


           return 1e-5






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		int:




           '''simple docstring'''


           return 12


