



'''simple docstring'''


from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


lowerCAmelCase :					Optional[Any] 							=logging.get_logger(__name__)

lowerCAmelCase :					Dict[Optional[str], Type[Formatter]] 							={}
lowerCAmelCase :					Dict[Optional[str], str] 							={}
lowerCAmelCase :					Dict[Optional[str], Exception] 							={}
def 							UpperCAmelCase_  (						__lowerCamelCase : type     ,__lowerCamelCase : Optional[str]     ,__lowerCamelCase : Optional[List[str]] = None     ,):
   lowercase_      :Optional[int]         =			aliases if aliases is not None else []
   if format_type in _FORMAT_TYPES:
      logger.warning(
          F'Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'			)
   lowercase_      :int         =			formatter_cls
   for alias in set(aliases + [format_type]			):
      if alias in _FORMAT_TYPES_ALIASES:
         logger.warning(
             F'Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'			)
      lowercase_      :str         =			format_type
def 							UpperCAmelCase_  (						__lowerCamelCase : Exception     ,__lowerCamelCase : Optional[str]     ,__lowerCamelCase : Optional[List[str]] = None			):
   lowercase_      :List[Any]         =			aliases if aliases is not None else []
   for alias in set(aliases + [format_type]			):
      lowercase_      :List[Any]         =			unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')

if config.TORCH_AVAILABLE:
 from .torch_formatter import TorchFormatter

 _register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
 lowerCAmelCase :					Optional[int] 							=ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
 _register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])

if config.TF_AVAILABLE:
 from .tf_formatter import TFFormatter

 _register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
 lowerCAmelCase :					Tuple 							=ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
 _register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])

if config.JAX_AVAILABLE:
 from .jax_formatter import JaxFormatter

 _register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
 lowerCAmelCase :					str 							=ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
 _register_unavailable_formatter(_jax_error, '''jax''', aliases=[])
def 							UpperCAmelCase_  (						__lowerCamelCase : Optional[str]			):
   if format_type in _FORMAT_TYPES_ALIASES:
      return _FORMAT_TYPES_ALIASES[format_type]
   else:
      return format_type


def 							UpperCAmelCase_  (						__lowerCamelCase : Optional[str]     ,**__lowerCamelCase : str			):
   lowercase_      :Union[str, Any]         =			get_format_type_from_alias(__lowerCamelCase			)
   if format_type in _FORMAT_TYPES:
      return _FORMAT_TYPES[format_type](**__lowerCamelCase			)
   if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
      raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
   else:
      raise ValueError(
          F'Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None			)}, but got \'{format_type}\''			)









'''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


