


"""simple docstring"""



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
  import PIL

__snake_case             =   logging.get_logger(__name__)







def   __lowerCAmelCase						(						lowercase  :  Any			)     ->						List[List[ImageInput]]:


    """simple docstring"""





    if isinstance(lowercase						,							(list, tuple)			) and isinstance(videos[0]						,							(list, tuple)			) and is_valid_image(videos[0][0]			):
        return videos

    elif isinstance(lowercase						,							(list, tuple)			) and is_valid_image(videos[0]			):
        return [videos]

    elif is_valid_image(lowercase			):
        return [[videos]]

    raise ValueError(F'Could not make batched video from {videos}'			)







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	Dict							  =  ['''pixel_values''']







   def __init__(     self						,     UpperCamelCase__ = True						,     UpperCamelCase__ = None						,     UpperCamelCase__ = PILImageResampling.BILINEAR						,     UpperCamelCase__ = True						,     UpperCamelCase__ = None						,     UpperCamelCase__ = True						,     UpperCamelCase__ = 1 / 255						,     UpperCamelCase__ = True						,     UpperCamelCase__ = True						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     **UpperCamelCase__						,     )     ->		None:

       '''simple docstring'''




       super().__init__(**UpperCamelCase__						)
       snake_case   :				int				 =   size if size is not None else {"shortest_edge": 256}
       snake_case   :				List[Any]				 =   get_size_dict(UpperCamelCase__						,     default_to_square=UpperCamelCase__						)
       snake_case   :				Dict				 =   crop_size if crop_size is not None else {"height": 224, "width": 224}
       snake_case   :				Optional[Any]				 =   get_size_dict(UpperCamelCase__						,     param_name="crop_size"						)

       snake_case   :				str				 =   do_resize
       snake_case   :				Union[str, Any]				 =   size
       snake_case   :				int				 =   do_center_crop
       snake_case   :				Optional[int]				 =   crop_size
       snake_case   :				Union[str, Any]				 =   resample
       snake_case   :				Tuple				 =   do_rescale
       snake_case   :				str				 =   rescale_factor
       snake_case   :				Tuple				 =   offset
       snake_case   :				int				 =   do_normalize
       snake_case   :				List[Any]				 =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       snake_case   :				List[str]				 =   image_std if image_std is not None else IMAGENET_STANDARD_STD







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__ = PILImageResampling.BILINEAR						,     UpperCamelCase__ = None						,     **UpperCamelCase__						,     )     ->		np.ndarray:

       '''simple docstring'''




       snake_case   :				Dict				 =   get_size_dict(UpperCamelCase__						,     default_to_square=UpperCamelCase__						)
       if "shortest_edge" in size:
           snake_case   :				Optional[Any]				 =   get_resize_output_image_size(UpperCamelCase__						,     size["shortest_edge"]						,     default_to_square=UpperCamelCase__						)
       elif "height" in size and "width" in size:
           snake_case   :				Optional[Any]				 =   (size["height"], size["width"])
       else:
           raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'						)
       return resize(UpperCamelCase__						,     size=UpperCamelCase__						,     resample=UpperCamelCase__						,     data_format=UpperCamelCase__						,     **UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__ = None						,     **UpperCamelCase__						,     )     ->		np.ndarray:

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   get_size_dict(UpperCamelCase__						)
       if "height" not in size or "width" not in size:
           raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'						)
       return center_crop(UpperCamelCase__						,     size=(size["height"], size["width"])						,     data_format=UpperCamelCase__						,     **UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__ = True						,     UpperCamelCase__ = None						,     **UpperCamelCase__						,     )     ->		Any:

       '''simple docstring'''




       snake_case   :				List[str]				 =   image.astype(np.floataa						)
       if offset:
           snake_case   :				Optional[int]				 =   image - (scale / 2)
       return rescale(UpperCamelCase__						,     scale=UpperCamelCase__						,     data_format=UpperCamelCase__						,     **UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__ = None						,     **UpperCamelCase__						,     )     ->		np.ndarray:

       '''simple docstring'''




       return normalize(UpperCamelCase__						,     mean=UpperCamelCase__						,     std=UpperCamelCase__						,     data_format=UpperCamelCase__						,     **UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = ChannelDimension.FIRST						,     )     ->		np.ndarray:

       '''simple docstring'''




       if do_resize and size is None or resample is None:
           raise ValueError("Size and resample must be specified if do_resize is True."						)

       if do_center_crop and crop_size is None:
           raise ValueError("Crop size must be specified if do_center_crop is True."						)

       if do_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."						)

       if do_normalize and (image_mean is None or image_std is None):
           raise ValueError("Image mean and std must be specified if do_normalize is True."						)

       if offset and not do_rescale:
           raise ValueError("For offset, do_rescale must also be set to True."						)

       # All transformations expect numpy arrays.
       snake_case   :				Union[str, Any]				 =   to_numpy_array(UpperCamelCase__						)

       if do_resize:
           snake_case   :				Union[str, Any]				 =   self.resize(image=UpperCamelCase__						,     size=UpperCamelCase__						,     resample=UpperCamelCase__						)

       if do_center_crop:
           snake_case   :				List[str]				 =   self.center_crop(UpperCamelCase__						,     size=UpperCamelCase__						)

       if do_rescale:
           snake_case   :				int				 =   self.rescale(image=UpperCamelCase__						,     scale=UpperCamelCase__						,     offset=UpperCamelCase__						)

       if do_normalize:
           snake_case   :				List[Any]				 =   self.normalize(image=UpperCamelCase__						,     mean=UpperCamelCase__						,     std=UpperCamelCase__						)

       snake_case   :				str				 =   to_channel_dimension_format(UpperCamelCase__						,     UpperCamelCase__						)
       return image







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = ChannelDimension.FIRST						,     **UpperCamelCase__						,     )     ->		PIL.Image.Image:

       '''simple docstring'''




       snake_case   :				str				 =   do_resize if do_resize is not None else self.do_resize
       snake_case   :				Any				 =   resample if resample is not None else self.resample
       snake_case   :				Dict				 =   do_center_crop if do_center_crop is not None else self.do_center_crop
       snake_case   :				List[Any]				 =   do_rescale if do_rescale is not None else self.do_rescale
       snake_case   :				Optional[Any]				 =   rescale_factor if rescale_factor is not None else self.rescale_factor
       snake_case   :				Union[str, Any]				 =   offset if offset is not None else self.offset
       snake_case   :				Optional[Any]				 =   do_normalize if do_normalize is not None else self.do_normalize
       snake_case   :				Optional[Any]				 =   image_mean if image_mean is not None else self.image_mean
       snake_case   :				Any				 =   image_std if image_std is not None else self.image_std

       snake_case   :				Any				 =   size if size is not None else self.size
       snake_case   :				int				 =   get_size_dict(UpperCamelCase__						,     default_to_square=UpperCamelCase__						)
       snake_case   :				int				 =   crop_size if crop_size is not None else self.crop_size
       snake_case   :				Optional[int]				 =   get_size_dict(UpperCamelCase__						,     param_name="crop_size"						)

       if not valid_images(UpperCamelCase__						):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray."						)

       snake_case   :				List[str]				 =   make_batched(UpperCamelCase__						)

       snake_case   :				Dict				 =   [
           [
               self._preprocess_image(
                   image=UpperCamelCase__						,     do_resize=UpperCamelCase__						,     size=UpperCamelCase__						,     resample=UpperCamelCase__						,     do_center_crop=UpperCamelCase__						,     crop_size=UpperCamelCase__						,     do_rescale=UpperCamelCase__						,     rescale_factor=UpperCamelCase__						,     offset=UpperCamelCase__						,     do_normalize=UpperCamelCase__						,     image_mean=UpperCamelCase__						,     image_std=UpperCamelCase__						,     data_format=UpperCamelCase__						,     )
               for img in video
           ]
           for video in videos
       ]

       snake_case   :				List[str]				 =   {"pixel_values": videos}
       return BatchFeature(data=UpperCamelCase__						,     tensor_type=UpperCamelCase__						)









"""simple docstring"""



import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
  from PIL import Image
else:







  class _lowerCAmelCase    :







     @staticmethod
     def 			lowerCamelCase		(     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		Union[str, Any]:

         '''simple docstring'''




         pass







@is_pipeline_test
@require_vision
@require_torch
class _lowerCAmelCase    (				unittest.TestCase     ):
   __UpperCAmelCase			:	List[Any]							  =  MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   pipeline(
           "zero-shot-object-detection"						,     model="hf-internal-testing/tiny-random-owlvit-object-detection"						)

       snake_case   :				int				 =   [
           {
               "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
               "candidate_labels": ["cat", "remote", "couch"],
           }
       ]
       return object_detector, examples







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Tuple:

       '''simple docstring'''




       snake_case   :				str				 =   object_detector(examples[0]						,     threshold=0.0						)

       snake_case   :				str				 =   len(UpperCamelCase__						)
       self.assertGreater(UpperCamelCase__						,     0						)
       self.assertEqual(
           UpperCamelCase__						,     [
               {
                   "score": ANY(UpperCamelCase__						),
                   "label": ANY(UpperCamelCase__						),
                   "box": {"xmin": ANY(UpperCamelCase__						), "ymin": ANY(UpperCamelCase__						), "xmax": ANY(UpperCamelCase__						), "ymax": ANY(UpperCamelCase__						)},
               }
               for i in range(UpperCamelCase__						)
           ]						,     )







   @require_tf
   @unittest.skip("Zero Shot Object Detection not implemented in TF"						)
   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       pass







   @require_torch
   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       snake_case   :				Dict				 =   pipeline(
           "zero-shot-object-detection"						,     model="hf-internal-testing/tiny-random-owlvit-object-detection"						)

       snake_case   :				Optional[Any]				 =   object_detector(
           "./tests/fixtures/tests_samples/COCO/000000039769.png"						,     candidate_labels=["cat", "remote", "couch"]						,     threshold=0.64						,     )

       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
               {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
               {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
               {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
               {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
               {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
               {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
               {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
               {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
           ]						,     )

       snake_case   :				Dict				 =   object_detector(
           [
               {
                   "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
                   "candidate_labels": ["cat", "remote", "couch"],
               }
           ]						,     threshold=0.64						,     )

       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               [
                   {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                   {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                   {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                   {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                   {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                   {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                   {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                   {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
                   {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
               ]
           ]						,     )







   @require_torch
   @slow
   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   pipeline("zero-shot-object-detection"						)

       snake_case   :				Tuple				 =   object_detector(
           "http://images.cocodataset.org/val2017/000000039769.jpg"						,     candidate_labels=["cat", "remote", "couch"]						,     )
       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
               {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
               {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
               {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
               {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
           ]						,     )

       snake_case   :				List[Any]				 =   object_detector(
           [
               {
                   "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
                   "candidate_labels": ["cat", "remote", "couch"],
               },
               {
                   "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
                   "candidate_labels": ["cat", "remote", "couch"],
               },
           ]						,     )
       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               [
                   {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                   {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                   {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                   {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                   {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
               ],
               [
                   {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                   {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                   {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                   {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                   {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
               ],
           ]						,     )







   @require_tf
   @unittest.skip("Zero Shot Object Detection not implemented in TF"						)
   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       pass







   @require_torch
   @slow
   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   0.2
       snake_case   :				List[str]				 =   pipeline("zero-shot-object-detection"						)

       snake_case   :				List[Any]				 =   object_detector(
           "http://images.cocodataset.org/val2017/000000039769.jpg"						,     candidate_labels=["cat", "remote", "couch"]						,     threshold=UpperCamelCase__						,     )
       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
               {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
               {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
           ]						,     )







   @require_torch
   @slow
   def 			lowerCamelCase		(     self						)     ->		Optional[int]:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   2
       snake_case   :				Optional[Any]				 =   pipeline("zero-shot-object-detection"						)

       snake_case   :				Optional[int]				 =   object_detector(
           "http://images.cocodataset.org/val2017/000000039769.jpg"						,     candidate_labels=["cat", "remote", "couch"]						,     top_k=UpperCamelCase__						,     )
       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
               {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
           ]						,     )





