

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

lowercase_   							=  logging.get_logger(__name__)





@add_end_docstrings(SCREAMING_SNAKE_CASE				)
class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):




  def __init__(							self				,  **lowerCAmelCase   )    ->					Optional[int]:
        '''simple docstring'''






        super().__init__(**lowerCAmelCase   )

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

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




  def __call__(							self				,  lowerCAmelCase				,  lowerCAmelCase = None				,  **lowerCAmelCase				,  )    ->					List[str]:
        '''simple docstring'''






        if "text_queries" in kwargs:
              _lowercase      			=kwargs.pop('text_queries'   )

        if isinstance(lowerCAmelCase				,  (str, Image.Image)   ):
              _lowercase      			={'image': image, 'candidate_labels': candidate_labels}
        else:
              _lowercase      			=image
        _lowercase      			=super().__call__(lowerCAmelCase				,  **lowerCAmelCase   )
        return results




  def     A__	(							self				,  **lowerCAmelCase   )    ->					int:
        '''simple docstring'''






        _lowercase      			={}
        if "threshold" in kwargs:
              _lowercase      			=kwargs['threshold']
        if "top_k" in kwargs:
              _lowercase      			=kwargs['top_k']
        return {}, {}, postprocess_params




  def     A__	(							self				,  lowerCAmelCase   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=load_image(inputs['image']   )
        _lowercase      			=inputs['candidate_labels']
        if isinstance(lowerCAmelCase				,  lowerCAmelCase   ):
              _lowercase      			=candidate_labels.split(','   )

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




  def     A__	(							self				,  lowerCAmelCase   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=model_inputs.pop('target_size'   )
        _lowercase      			=model_inputs.pop('candidate_label'   )
        _lowercase      			=model_inputs.pop('is_last'   )

        _lowercase      			=self.model(**lowerCAmelCase   )

        _lowercase      			={'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
        return model_outputs




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase=0.1				,  lowerCAmelCase=None   )    ->					List[Any]:
        '''simple docstring'''






        _lowercase      			=[]
        for model_output in model_outputs:
              _lowercase      			=model_output['candidate_label']
              _lowercase      			=BaseModelOutput(lowerCAmelCase   )
              _lowercase      			=self.image_processor.post_process_object_detection(
                  outputs=lowerCAmelCase				,  threshold=lowerCAmelCase				,  target_sizes=model_output['target_size']   )[0]

              for index in outputs["scores"].nonzero():
                    _lowercase      			=outputs['scores'][index].item()
                    _lowercase      			=self._get_bounding_box(outputs['boxes'][index][0]   )

                    _lowercase      			={'score': score, 'label': label, 'box': box}
                    results.append(lowerCAmelCase   )

        _lowercase      			=sorted(lowerCAmelCase				,  key=lambda lowerCAmelCase   : x["score"]				,  reverse=lowerCAmelCase   )
        if top_k:
              _lowercase      			=results[:top_k]

        return results




  def     A__	(							self				,  lowerCAmelCase   )    ->					Dict[str, int]:
        '''simple docstring'''






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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_   							=  {
    'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_   							=  [
      'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TimesformerModel',
      'TimesformerForVideoClassification',
      'TimesformerPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_timesformer import (
        TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimesformerForVideoClassification,
        TimesformerModel,
        TimesformerPreTrainedModel,
    )

else:
  import sys

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


