



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

a_   :Union[str, Any]        =      logging.get_logger(__name__)




@add_end_docstrings(lowerCAmelCase_     )
class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     def __init__(       self  :  Optional[Any], **_snake_case  :  List[Any]					)   ->Any:
            super().__init__(**_snake_case					)

            if self.framework == "tf":
                   raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''					)

            requires_backends(self, 'vision'					)
            self.check_model_type(_snake_case					)






     def __call__(       self  :  Optional[int], _snake_case  :  Union[str, "Image.Image", List[Dict[str, Any]]], _snake_case  :  Union[str, List[str]] = None, **_snake_case  :  Any, )   ->int:
            if "text_queries" in kwargs:
                   snake_case__							:	Optional[int]   	=	kwargs.pop('text_queries'					)

            if isinstance(_snake_case, (str, Image.Image)					):
                   snake_case__							:	Any   	=	{'image': image, 'candidate_labels': candidate_labels}
            else:
                   snake_case__							:	Optional[int]   	=	image
            snake_case__							:	Tuple   	=	super().__call__(_snake_case, **_snake_case					)
            return results






     def    lowercase_   (       self  :  str, **_snake_case  :  Union[str, Any]					)   ->int:
            snake_case__							:	Dict   	=	{}
            if "threshold" in kwargs:
                   snake_case__							:	int   	=	kwargs['threshold']
            if "top_k" in kwargs:
                   snake_case__							:	Any   	=	kwargs['top_k']
            return {}, {}, postprocess_params






     def    lowercase_   (       self  :  List[str], _snake_case  :  int					)   ->List[str]:
            snake_case__							:	Dict   	=	load_image(inputs['image']					)
            snake_case__							:	Optional[int]   	=	inputs['candidate_labels']
            if isinstance(_snake_case, _snake_case					):
                   snake_case__							:	Any   	=	candidate_labels.split(','					)

            snake_case__							:	List[str]   	=	torch.tensor([[image.height, image.width]], dtype=torch.intaa					)
            for i, candidate_label in enumerate(_snake_case					):
                   snake_case__							:	List[Any]   	=	self.tokenizer(_snake_case, return_tensors=self.framework					)
                   snake_case__							:	List[str]   	=	self.image_processor(_snake_case, return_tensors=self.framework					)
                   yield {
                       "is_last": i == len(_snake_case					) - 1,
                       "target_size": target_size,
                       "candidate_label": candidate_label,
                       **text_inputs,
                       **image_features,
                   }






     def    lowercase_   (       self  :  Optional[Any], _snake_case  :  Optional[Any]					)   ->Optional[int]:
            snake_case__							:	str   	=	model_inputs.pop('target_size'					)
            snake_case__							:	Dict   	=	model_inputs.pop('candidate_label'					)
            snake_case__							:	Dict   	=	model_inputs.pop('is_last'					)

            snake_case__							:	Optional[int]   	=	self.model(**_snake_case					)

            snake_case__							:	List[Any]   	=	{'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
            return model_outputs






     def    lowercase_   (       self  :  Any, _snake_case  :  Any, _snake_case  :  Dict=0.1, _snake_case  :  List[str]=None					)   ->Dict:
            snake_case__							:	Dict   	=	[]
            for model_output in model_outputs:
                   snake_case__							:	Any   	=	model_output['candidate_label']
                   snake_case__							:	str   	=	BaseModelOutput(_snake_case					)
                   snake_case__							:	List[str]   	=	self.image_processor.post_process_object_detection(
                       outputs=_snake_case, threshold=_snake_case, target_sizes=model_output['target_size']					)[0]

                   for index in outputs["scores"].nonzero():
                          snake_case__							:	List[str]   	=	outputs['scores'][index].item()
                          snake_case__							:	Union[str, Any]   	=	self._get_bounding_box(outputs['boxes'][index][0]					)

                          snake_case__							:	int   	=	{'score': score, 'label': label, 'box': box}
                          results.append(_snake_case					)

            snake_case__							:	Tuple   	=	sorted(_snake_case, key=lambda _snake_case					: x["score"], reverse=_snake_case					)
            if top_k:
                   snake_case__							:	List[str]   	=	results[:top_k]

            return results






     def    lowercase_   (       self  :  List[str], _snake_case  :  "torch.Tensor"					)   ->Dict[str, int]:
            if self.framework != "pt":
                   raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.'					)
            snake_case__	,				snake_case__	,				snake_case__	,				snake_case__							:	Union[str, Any]   	=	box.int().tolist()
            snake_case__							:	str   	=	{
                'xmin': xmin,
                'ymin': ymin,
                'xmax': xmax,
                'ymax': ymax,
            }
            return bbox











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


a_   :int        =      {
    "configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :List[str]        =      [
     "LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LongT5EncoderModel",
     "LongT5ForConditionalGeneration",
     "LongT5Model",
     "LongT5PreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :int        =      [
     "FlaxLongT5ForConditionalGeneration",
     "FlaxLongT5Model",
     "FlaxLongT5PreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_longta import (
      LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
      LongTaEncoderModel,
      LongTaForConditionalGeneration,
      LongTaModel,
      LongTaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_longta import (
      FlaxLongTaForConditionalGeneration,
      FlaxLongTaModel,
      FlaxLongTaPreTrainedModel,
  )


else:
 import sys

 a_   :Optional[int]        =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






