




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, ChunkPipeline


if is_vision_available():
			from PIL import Image

			from ..image_utils import load_image

if is_torch_available():
			import torch

			from transformers.modeling_outputs import BaseModelOutput

			from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

lowercase__				:      Any	   = logging.get_logger(__name__)




@add_end_docstrings(lowerCamelCase__				)
class       a__	( lowerCamelCase__				):







					def __init__(     self  ,		**A			)  ->   str:



										'''simple docstring'''



										super().__init__(**__lowerCamelCase			)

										if self.framework == "tf":
															raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''			)

										requires_backends(self  ,		"vision"			)
										self.check_model_type(__lowerCamelCase			)







					def __call__(     self  ,		A  ,		A = None  ,		**A  ,		)  ->   Tuple:



										'''simple docstring'''



										if "text_queries" in kwargs:
															a     =				kwargs.pop("text_queries"			)

										if isinstance(__lowerCamelCase  ,		(str, Image.Image)			):
															a     =				{'''image''': image, '''candidate_labels''': candidate_labels}
										else:
															a     =				image
										a     =				super().__call__(__lowerCamelCase  ,		**__lowerCamelCase			)
										return results







					def       lowerCAmelCase_    (     self  ,		**A			)  ->   Union[str, Any]:



										'''simple docstring'''



										a     =				{}
										if "threshold" in kwargs:
															a     =				kwargs['''threshold''']
										if "top_k" in kwargs:
															a     =				kwargs['''top_k''']
										return {}, {}, postprocess_params







					def       lowerCAmelCase_    (     self  ,		A			)  ->   Tuple:



										'''simple docstring'''



										a     =				load_image(inputs["image"]			)
										a     =				inputs['''candidate_labels''']
										if isinstance(__lowerCamelCase  ,		__lowerCamelCase			):
															a     =				candidate_labels.split(","			)

										a     =				torch.tensor([[image.height, image.width]]  ,		dtype=torch.intaa			)
										for i, candidate_label in enumerate(__lowerCamelCase			):
															a     =				self.tokenizer(__lowerCamelCase  ,		return_tensors=self.framework			)
															a     =				self.image_processor(__lowerCamelCase  ,		return_tensors=self.framework			)
															yield {
															    "is_last": i == len(__lowerCamelCase			) - 1,
															    "target_size": target_size,
															    "candidate_label": candidate_label,
															    **text_inputs,
															    **image_features,
															}







					def       lowerCAmelCase_    (     self  ,		A			)  ->   Tuple:



										'''simple docstring'''



										a     =				model_inputs.pop("target_size"			)
										a     =				model_inputs.pop("candidate_label"			)
										a     =				model_inputs.pop("is_last"			)

										a     =				self.model(**__lowerCamelCase			)

										a     =				{'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
										return model_outputs







					def       lowerCAmelCase_    (     self  ,		A  ,		A=0.1  ,		A=None			)  ->   List[Any]:



										'''simple docstring'''



										a     =				[]
										for model_output in model_outputs:
															a     =				model_output['''candidate_label''']
															a     =				BaseModelOutput(__lowerCamelCase			)
															a     =				self.image_processor.post_process_object_detection(
															    outputs=__lowerCamelCase  ,		threshold=__lowerCamelCase  ,		target_sizes=model_output["target_size"]			)[0]

															for index in outputs["scores"].nonzero():
																				a     =				outputs['''scores'''][index].item()
																				a     =				self._get_bounding_box(outputs["boxes"][index][0]			)

																				a     =				{'''score''': score, '''label''': label, '''box''': box}
																				results.append(__lowerCamelCase			)

										a     =				sorted(__lowerCamelCase  ,		key=lambda A			: x["score"]  ,		reverse=__lowerCamelCase			)
										if top_k:
															a     =				results[:top_k]

										return results







					def       lowerCAmelCase_    (     self  ,		A			)  ->   Dict[str, int]:



										'''simple docstring'''



										if self.framework != "pt":
															raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch."			)
										a     =				box.int().tolist()
										a     =				{
										    '''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


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

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




class       a__	( UpperCamelCase__				):
     a		:      Optional[int]		 =     """table-transformer"""
     a		:      Tuple		 =     ["""past_key_values"""]
     a		:      int		 =     {
         """hidden_size""": """d_model""",
         """num_attention_heads""": """encoder_attention_heads""",
     }







     def __init__(     self  ,		A=True  ,		A=None  ,		A=3  ,		A=100  ,		A=6  ,		A=2048  ,		A=8  ,		A=6  ,		A=2048  ,		A=8  ,		A=0.0  ,		A=0.0  ,		A=True  ,		A="relu"  ,		A=256  ,		A=0.1  ,		A=0.0  ,		A=0.0  ,		A=0.0_2  ,		A=1.0  ,		A=False  ,		A="sine"  ,		A="resnet50"  ,		A=True  ,		A=False  ,		A=1  ,		A=5  ,		A=2  ,		A=1  ,		A=1  ,		A=5  ,		A=2  ,		A=0.1  ,		**A  ,		)  ->   List[str]:



          '''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."			)
                    a     =				CONFIG_MAPPING["resnet"](out_features=["stage4"]			)
               elif isinstance(A  ,		A			):
                    a     =				backbone_config.get("model_type"			)
                    a     =				CONFIG_MAPPING[backbone_model_type]
                    a     =				config_class.from_dict(A			)
               # set timm attributes to None
               a  ,				a  ,				a     =				None, None, None

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







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return self.encoder_attention_heads







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return self.d_model







class       a__	( UpperCamelCase__				):
     a		:      Any		 =     version.parse("""1.11"""				)







     @property
     def       lowerCAmelCase_    (     self			)  ->   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       lowerCAmelCase_    (     self			)  ->   float:



          '''simple docstring'''



          return 1e-5







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return 12

