

"""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, 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_					:	Optional[int]							  =						logging.get_logger(__name__)


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



@add_end_docstrings(_lowerCamelCase				)
class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    def __init__(     self: Tuple    ,						*UpperCamelCase: str    ,						**UpperCamelCase: List[str]       ):






         """simple docstring"""


         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 				UpperCamelCase	(     self: Optional[int]    ,						**UpperCamelCase: List[Any]       ):






         """simple docstring"""


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

    def __call__(     self: Union[str, Any]    ,						*UpperCamelCase: Any    ,						**UpperCamelCase: Union[str, Any]       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: Any    ,						UpperCamelCase: Any       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: Optional[Any]    ,						UpperCamelCase: int       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: List[Any]    ,						UpperCamelCase: Tuple    ,						UpperCamelCase: Tuple=0.9       ):






         """simple docstring"""


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

              def unnormalize(UpperCamelCase: int       ):
                   return self._get_bounding_box(
                       torch.Tensor(
                           [
                               (width * bbox[0] / 10_00),
                               (height * bbox[1] / 10_00),
                               (width * bbox[2] / 10_00),
                               (height * bbox[3] / 10_00),
                           ]       )       )

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

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

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

         return annotation





    def 				UpperCamelCase	(     self: Optional[int]    ,						UpperCamelCase: "torch.Tensor"       ):






         """simple docstring"""


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






"""simple docstring"""



import warnings
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


SCREAMING_SNAKE_CASE_					:	Any							  =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						{
    'nvidia/segformer-b0-finetuned-ade-512-512': (
        'https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					"segformer"

    def __init__(     self: Optional[Any]    ,						UpperCamelCase: Union[str, Any]=3    ,						UpperCamelCase: int=4    ,						UpperCamelCase: int=[2, 2, 2, 2]    ,						UpperCamelCase: List[str]=[8, 4, 2, 1]    ,						UpperCamelCase: List[Any]=[32, 64, 1_60, 2_56]    ,						UpperCamelCase: Any=[7, 3, 3, 3]    ,						UpperCamelCase: Union[str, Any]=[4, 2, 2, 2]    ,						UpperCamelCase: Tuple=[1, 2, 5, 8]    ,						UpperCamelCase: Optional[int]=[4, 4, 4, 4]    ,						UpperCamelCase: Dict="gelu"    ,						UpperCamelCase: Optional[Any]=0.0    ,						UpperCamelCase: List[str]=0.0    ,						UpperCamelCase: Union[str, Any]=0.1    ,						UpperCamelCase: List[Any]=0.02    ,						UpperCamelCase: int=0.1    ,						UpperCamelCase: Optional[int]=1e-6    ,						UpperCamelCase: int=2_56    ,						UpperCamelCase: Union[str, Any]=2_55    ,						**UpperCamelCase: Optional[Any]    ,						):






         """simple docstring"""


         super().__init__(**UpperCamelCase       )

         if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
              warnings.warn(
                  """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
                  """ removed, as the behaviour will default to that of reshape_last_stage = True."""    ,						UpperCamelCase    ,						)

         A__										=  num_channels
         A__										=  num_encoder_blocks
         A__										=  depths
         A__										=  sr_ratios
         A__										=  hidden_sizes
         A__										=  patch_sizes
         A__										=  strides
         A__										=  mlp_ratios
         A__										=  num_attention_heads
         A__										=  hidden_act
         A__										=  hidden_dropout_prob
         A__										=  attention_probs_dropout_prob
         A__										=  classifier_dropout_prob
         A__										=  initializer_range
         A__										=  drop_path_rate
         A__										=  layer_norm_eps
         A__										=  decoder_hidden_size
         A__										=  kwargs.get("""reshape_last_stage"""    ,						UpperCamelCase       )
         A__										=  semantic_loss_ignore_index



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					version.parse("1.11"				)

    @property
    def 				UpperCamelCase	(     self: List[str]       ):






         """simple docstring"""


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

    @property
    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         return 1e-4





    @property
    def 				UpperCamelCase	(     self: Optional[int]       ):






         """simple docstring"""


         return 12






