
"""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

UpperCamelCase			:       Any									=				logging.get_logger(__name__)


UpperCamelCase			:       str									=				Dict[str, Any]
UpperCamelCase			:       Any									=				List[Prediction]

@add_end_docstrings(__SCREAMING_SNAKE_CASE    )
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):





     def __init__(			self			,  *__UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''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			,  **__UpperCAmelCase   ):

      '''simple docstring'''




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





     def __call__(			self			,  *__UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase=0.9   ):

      '''simple docstring'''




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

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

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

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

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

      return annotation







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




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





"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCamelCase			:       int									=				logging.get_logger(__name__)

UpperCamelCase			:       int									=				{
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"bit"
     lowercase		     =		["preactivation", "bottleneck"]
     lowercase		     =		["SAME", "VALID"]





     def __init__(			self			,  __UpperCAmelCase=3			,  __UpperCAmelCase=64			,  __UpperCAmelCase=[256, 512, 1024, 2048]			,  __UpperCAmelCase=[3, 4, 6, 3]			,  __UpperCAmelCase="preactivation"			,  __UpperCAmelCase="relu"			,  __UpperCAmelCase=None			,  __UpperCAmelCase=32			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=False			,  __UpperCAmelCase=32			,  __UpperCAmelCase=1			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      super().__init__(**__UpperCAmelCase   )
      if layer_type not in self.layer_types:
       raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types   )}'   )
      if global_padding is not None:
       if global_padding.upper() in self.supported_padding:
        __UpperCamelCase       						= global_padding.upper()
       else:
        raise ValueError(F'Padding strategy {global_padding} not supported'   )
      __UpperCamelCase       						= num_channels
      __UpperCamelCase       						= embedding_size
      __UpperCamelCase       						= hidden_sizes
      __UpperCamelCase       						= depths
      __UpperCamelCase       						= layer_type
      __UpperCamelCase       						= hidden_act
      __UpperCamelCase       						= global_padding
      __UpperCamelCase       						= num_groups
      __UpperCamelCase       						= drop_path_rate
      __UpperCamelCase       						= embedding_dynamic_padding
      __UpperCamelCase       						= output_stride
      __UpperCamelCase       						= width_factor

      __UpperCamelCase       						= ['stem'] + [F'stage{idx}' for idx in range(1			,  len(__UpperCAmelCase   ) + 1   )]
      __UpperCamelCase						,			__UpperCamelCase       						= get_aligned_output_features_output_indices(
          out_features=__UpperCAmelCase			,  out_indices=__UpperCAmelCase			,  stage_names=self.stage_names   )



