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_			    =							logging.get_logger(__name__)


UpperCAmelCase_			    =							Dict[str, Any]
UpperCAmelCase_			    =							List[Prediction]




@add_end_docstrings(__a    )
class  __magic_name__  (		__a    ):





   """simple docstring"""




   def __init__(	self					:  Dict		,  *_lowercase					:  int		,  **_lowercase					:  str							):



    """simple docstring"""



    super().__init__(*_lowercase		,  **_lowercase							)

    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   lowerCAmelCase						(	self					:  int		,  **_lowercase					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= {}
    if "threshold" in kwargs:
     _UpperCamelCase:     Dict							= kwargs['''threshold''']
    return {}, {}, postprocess_kwargs



   def __call__(	self					:  Tuple		,  *_lowercase					:  str		,  **_lowercase					:  int							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= load_image(_lowercase							)
    _UpperCamelCase:     Optional[Any]							= torch.IntTensor([[image.height, image.width]]							)
    _UpperCamelCase:     Dict							= self.image_processor(images=[image]		,  return_tensors='''pt'''							)
    if self.tokenizer is not None:
     _UpperCamelCase:     List[str]							= self.tokenizer(text=inputs['''words''']		,  boxes=inputs['''boxes''']		,  return_tensors='''pt'''							)
    _UpperCamelCase:     int							= target_size
    return inputs



   def   lowerCAmelCase						(	self					:  Union[str, Any]		,  _lowercase					:  Optional[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= model_inputs.pop('''target_size'''							)
    _UpperCamelCase:     Tuple							= self.model(**_lowercase							)
    _UpperCamelCase:     List[Any]							= outputs.__class__({'''target_size''': target_size, **outputs}							)
    if self.tokenizer is not None:
     _UpperCamelCase:     Union[str, Any]							= model_inputs['''bbox''']
    return model_outputs



   def   lowerCAmelCase						(	self					:  Tuple		,  _lowercase					:  int		,  _lowercase					:  Dict=0.9							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= 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:     Union[str, Any]							= target_size[0].tolist()

     def unnormalize(_lowercase					:  Any							):
      return self._get_bounding_box(
          torch.Tensor(
              [
                  (width * bbox[0] / 1_000),
                  (height * bbox[1] / 1_000),
                  (width * bbox[2] / 1_000),
                  (height * bbox[3] / 1_000),
              ]							)							)

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

     _UpperCamelCase:     Any							= scores.tolist()
     _UpperCamelCase:     int							= [self.model.config.idalabel[label.item()] for label in labels]
     _UpperCamelCase:     str							= [self._get_bounding_box(_lowercase							) for box in boxes]

     # {"scores": [...], ...} --> [{"score":x, ...}, ...]
     _UpperCamelCase:     Union[str, Any]							= ['''score''', '''label''', '''box''']
     _UpperCamelCase:     List[Any]							= [
         dict(zip(_lowercase		,  _lowercase							)							)
         for vals in zip(raw_annotation['''scores''']		,  raw_annotation['''labels''']		,  raw_annotation['''boxes''']							)
     ]

    return annotation



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



    """simple docstring"""



    if self.framework != "pt":
     raise ValueError('''The ObjectDetectionPipeline is only available in PyTorch.'''							)
    _UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase:     Optional[Any]							= box.int().tolist()
    _UpperCamelCase:     str							= {
        '''xmin''': xmin,
        '''ymin''': ymin,
        '''xmax''': xmax,
        '''ymax''': ymax,
    }
    return bbox
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_			    =							{'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							['''XLNetTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							['''XLNetTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''XLNetForMultipleChoice''',
      '''XLNetForQuestionAnswering''',
      '''XLNetForQuestionAnsweringSimple''',
      '''XLNetForSequenceClassification''',
      '''XLNetForTokenClassification''',
      '''XLNetLMHeadModel''',
      '''XLNetModel''',
      '''XLNetPreTrainedModel''',
      '''load_tf_weights_in_xlnet''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFXLNetForMultipleChoice''',
      '''TFXLNetForQuestionAnsweringSimple''',
      '''TFXLNetForSequenceClassification''',
      '''TFXLNetForTokenClassification''',
      '''TFXLNetLMHeadModel''',
      '''TFXLNetMainLayer''',
      '''TFXLNetModel''',
      '''TFXLNetPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_			    =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)