


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(__A							)
class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def __init__(	self  ,       *__A  ,       **__A    ):
        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 	snake_case_  (	self  ,       **__A    ):
        __a					=  {}
        if "threshold" in kwargs:
           __a					=  kwargs['threshold']
        return {}, {}, postprocess_kwargs


     def __call__(	self  ,       *__A  ,       **__A    ):
        return super().__call__(*__UpperCAmelCase  ,       **__UpperCAmelCase    )


     def 	snake_case_  (	self  ,       __A    ):
        __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 	snake_case_  (	self  ,       __A    ):
        __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 	snake_case_  (	self  ,       __A  ,       __A=0.9    ):
        __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					=  target_size[0].tolist()

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

           __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 	snake_case_  (	self  ,       __A    ):

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










"""simple docstring"""





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
  from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__A											=						logging.get_logger(__name__)

__A											=						{
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:Union[str, Any]						    =   """deberta-v2"""






       def __init__(       self       ,					__UpperCAmelCase=1_2_8_1_0_0       ,					__UpperCAmelCase=1_5_3_6       ,					__UpperCAmelCase=2_4       ,					__UpperCAmelCase=2_4       ,					__UpperCAmelCase=6_1_4_4       ,					__UpperCAmelCase="gelu"       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=5_1_2       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase=0.02       ,					__UpperCAmelCase=1E-7       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=-1       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase="gelu"       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         super().__init__(**__UpperCAmelCase )

         lowerCAmelCase__     :Optional[int]											=				hidden_size
         lowerCAmelCase__     :Optional[int]											=				num_hidden_layers
         lowerCAmelCase__     :Optional[int]											=				num_attention_heads
         lowerCAmelCase__     :int											=				intermediate_size
         lowerCAmelCase__     :Dict											=				hidden_act
         lowerCAmelCase__     :Tuple											=				hidden_dropout_prob
         lowerCAmelCase__     :Optional[int]											=				attention_probs_dropout_prob
         lowerCAmelCase__     :Optional[Any]											=				max_position_embeddings
         lowerCAmelCase__     :Optional[Any]											=				type_vocab_size
         lowerCAmelCase__     :List[Any]											=				initializer_range
         lowerCAmelCase__     :Dict											=				relative_attention
         lowerCAmelCase__     :Union[str, Any]											=				max_relative_positions
         lowerCAmelCase__     :List[str]											=				pad_token_id
         lowerCAmelCase__     :Optional[int]											=				position_biased_input

         # Backwards compatibility
         if type(__UpperCAmelCase ) == str:
           lowerCAmelCase__     :Dict											=				[x.strip() for x in pos_att_type.lower().split('|' )]

         lowerCAmelCase__     :str											=				pos_att_type
         lowerCAmelCase__     :Dict											=				vocab_size
         lowerCAmelCase__     :Tuple											=				layer_norm_eps

         lowerCAmelCase__     :Optional[Any]											=				kwargs.get('pooler_hidden_size'       ,					__UpperCAmelCase )
         lowerCAmelCase__     :Union[str, Any]											=				pooler_dropout
         lowerCAmelCase__     :int											=				pooler_hidden_act






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         if self.task == "multiple-choice":
           lowerCAmelCase__     :str											=				{0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
           lowerCAmelCase__     :Tuple											=				{0: 'batch', 1: 'sequence'}
         if self._config.type_vocab_size > 0:
           return OrderedDict(
               [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)] )
         else:
           return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)] )






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return 1_2






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = -1       ,					__UpperCAmelCase = -1       ,					__UpperCAmelCase = -1       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = 3       ,					__UpperCAmelCase = 4_0       ,					__UpperCAmelCase = 4_0       ,					__UpperCAmelCase = None       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				super().generate_dummy_inputs(preprocessor=__UpperCAmelCase       ,					framework=__UpperCAmelCase )
         if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
           del dummy_inputs["token_type_ids"]
         return dummy_inputs
