





'''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, 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

lowerCAmelCase :					Union[str, Any] 							=logging.get_logger(__name__)
@add_end_docstrings(_lowerCAmelCase							)
class 				a_       (    _lowerCAmelCase							):





     def __init__(			self		: List[Any]     ,				**lowercase		: Optional[int]						):





        """simple docstring"""




        super().__init__(**lowercase						)

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

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





     def __call__(			self		: Tuple     ,				lowercase		: Union[str, "Image.Image", List[Dict[str, Any]]]     ,				lowercase		: Union[str, List[str]] = None     ,				**lowercase		: str     ,				):





        """simple docstring"""




        if "text_queries" in kwargs:
           lowercase_      :List[Any]         =			kwargs.pop("text_queries"						)

        if isinstance(lowercase     ,				(str, Image.Image)						):
           lowercase_      :List[str]         =			{"image": image, "candidate_labels": candidate_labels}
        else:
           lowercase_      :Optional[Any]         =			image
        lowercase_      :str         =			super().__call__(lowercase     ,				**lowercase						)
        return results





     def 							lowercase__				(			self		: Optional[int]     ,				**lowercase		: List[str]						):





        """simple docstring"""




        lowercase_      :Tuple         =			{}
        if "threshold" in kwargs:
           lowercase_      :Dict         =			kwargs["threshold"]
        if "top_k" in kwargs:
           lowercase_      :Optional[Any]         =			kwargs["top_k"]
        return {}, {}, postprocess_params





     def 							lowercase__				(			self		: List[Any]     ,				lowercase		: Dict						):





        """simple docstring"""




        lowercase_      :Any         =			load_image(inputs["image"]						)
        lowercase_      :List[str]         =			inputs["candidate_labels"]
        if isinstance(lowercase     ,				lowercase						):
           lowercase_      :Union[str, Any]         =			candidate_labels.split(","						)

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





     def 							lowercase__				(			self		: List[str]     ,				lowercase		: List[str]						):





        """simple docstring"""




        lowercase_      :Dict         =			model_inputs.pop("target_size"						)
        lowercase_      :str         =			model_inputs.pop("candidate_label"						)
        lowercase_      :List[Any]         =			model_inputs.pop("is_last"						)

        lowercase_      :Optional[Any]         =			self.model(**lowercase						)

        lowercase_      :str         =			{"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
        return model_outputs





     def 							lowercase__				(			self		: Optional[int]     ,				lowercase		: List[str]     ,				lowercase		: List[str]=0.1     ,				lowercase		: Optional[int]=None						):





        """simple docstring"""




        lowercase_      :Dict         =			[]
        for model_output in model_outputs:
           lowercase_      :int         =			model_output["candidate_label"]
           lowercase_      :str         =			BaseModelOutput(lowercase						)
           lowercase_      :List[Any]         =			self.image_processor.post_process_object_detection(
               outputs=lowercase     ,				threshold=lowercase     ,				target_sizes=model_output["target_size"]						)[0]

           for index in outputs["scores"].nonzero():
              lowercase_      :Optional[int]         =			outputs["scores"][index].item()
              lowercase_      :int         =			self._get_bounding_box(outputs["boxes"][index][0]						)

              lowercase_      :int         =			{"score": score, "label": label, "box": box}
              results.append(lowercase						)

        lowercase_      :Dict         =			sorted(lowercase     ,				key=lambda lowercase						: x["score"]     ,				reverse=lowercase						)
        if top_k:
           lowercase_      :List[str]         =			results[:top_k]

        return results







     def 							lowercase__				(			self		: Union[str, Any]     ,				lowercase		: "torch.Tensor"						):





        """simple docstring"""




        if self.framework != "pt":
           raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch."						)
        lowercase_      ,							lowercase_      ,							lowercase_      ,							lowercase_      :List[str]         =			box.int().tolist()
        lowercase_      :List[Any]         =			{
            "xmin": xmin,
            "ymin": ymin,
            "xmax": xmax,
            "ymax": ymax,
        }
        return bbox









'''simple docstring'''


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowerCAmelCase :					Any 							={
    '''facebook/maskformer-swin-base-ade''': (
        '''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowerCAmelCase :					int 							=logging.get_logger(__name__)
class 				a_       (    _lowerCAmelCase							):
     __A								=      "maskformer"
     __A								=      {"hidden_size": "mask_feature_size"}
     __A								=      ["resnet", "swin"]
     __A								=      ["detr"]





     def __init__(			self		: List[Any]     ,				lowercase		: int = 256     ,				lowercase		: int = 256     ,				lowercase		: float = 0.1     ,				lowercase		: bool = False     ,				lowercase		: Optional[Dict] = None     ,				lowercase		: Optional[Dict] = None     ,				lowercase		: float = 0.02     ,				lowercase		: float = 1.0     ,				lowercase		: float = 1.0     ,				lowercase		: float = 1.0     ,				lowercase		: float = 20.0     ,				lowercase		: Optional[bool] = None     ,				**lowercase		: Any     ,				):





        """simple docstring"""




        if backbone_config is None:
           # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
           lowercase_      :Any         =			SwinConfig(
               image_size=384     ,				in_channels=3     ,				patch_size=4     ,				embed_dim=128     ,				depths=[2, 2, 18, 2]     ,				num_heads=[4, 8, 16, 32]     ,				window_size=12     ,				drop_path_rate=0.3     ,				out_features=["stage1", "stage2", "stage3", "stage4"]     ,				)

        if isinstance(lowercase     ,				lowercase						):
           lowercase_      :Optional[int]         =			backbone_config.pop("model_type"						)
           lowercase_      :Optional[int]         =			CONFIG_MAPPING[backbone_model_type]
           lowercase_      :int         =			config_class.from_dict(lowercase						)

        # verify that the backbone is supported
        if backbone_config.model_type not in self.backbones_supported:
           logger.warning_once(
               F'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '
               F'Supported model types: {",".join(self.backbones_supported						)}'						)

        if decoder_config is None:
           # fall back to https://huggingface.co/facebook/detr-resnet-50
           lowercase_      :Optional[Any]         =			DetrConfig()
        else:
           # verify that the decoder is supported
           lowercase_      :Tuple         =			(
               decoder_config.pop("model_type"						) if isinstance(lowercase     ,				lowercase						) else decoder_config.model_type
           )
           if decoder_type not in self.decoders_supported:
              raise ValueError(
                  F'Transformer Decoder {decoder_type} not supported, please use one of'
                  F' {",".join(self.decoders_supported						)}'						)
           if isinstance(lowercase     ,				lowercase						):
              lowercase_      :str         =			CONFIG_MAPPING[decoder_type]
              lowercase_      :List[str]         =			config_class.from_dict(lowercase						)

        lowercase_      :str         =			backbone_config
        lowercase_      :Union[str, Any]         =			decoder_config
        # main feature dimension for the model
        lowercase_      :Any         =			fpn_feature_size
        lowercase_      :Optional[int]         =			mask_feature_size
        # initializer
        lowercase_      :List[Any]         =			init_std
        lowercase_      :Union[str, Any]         =			init_xavier_std
        # Hungarian matcher && loss
        lowercase_      :List[str]         =			cross_entropy_weight
        lowercase_      :int         =			dice_weight
        lowercase_      :List[str]         =			mask_weight
        lowercase_      :Optional[Any]         =			use_auxiliary_loss
        lowercase_      :str         =			no_object_weight
        lowercase_      :int         =			output_auxiliary_logits

        lowercase_      :Optional[Any]         =			self.decoder_config.encoder_attention_heads
        lowercase_      :int         =			self.decoder_config.num_hidden_layers
        super().__init__(**lowercase						)





     @classmethod
     def 							lowercase__				(			cls		: Tuple     ,				lowercase		: PretrainedConfig     ,				lowercase		: PretrainedConfig     ,				**lowercase		: Union[str, Any]						):





        """simple docstring"""




        return cls(
            backbone_config=lowercase     ,				decoder_config=lowercase     ,				**lowercase     ,				)







     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :str         =			copy.deepcopy(self.__dict__						)
        lowercase_      :int         =			self.backbone_config.to_dict()
        lowercase_      :List[Any]         =			self.decoder_config.to_dict()
        lowercase_      :Optional[Any]         =			self.__class__.model_type
        return output


