





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

_UpperCAmelCase       : List[Any]           =       logging.get_logger(__name__)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  List[List[ImageInput]]:
    if isinstance(_UpperCAmelCase		,			(list, tuple)				) and isinstance(videos[0]		,			(list, tuple)				) and is_valid_image(videos[0][0]				):
        return videos

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

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

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


class      lowerCAmelCase							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	["""pixel_values"""]
 def __init__(   self						:   int    ,  UpperCAmelCase						:   bool = True    ,  UpperCAmelCase						:   Dict[str, int] = None    ,  UpperCAmelCase						:   PILImageResampling = PILImageResampling.BILINEAR    ,  UpperCAmelCase						:   bool = True    ,  UpperCAmelCase						:   Dict[str, int] = None    ,  UpperCAmelCase						:   bool = True    ,  UpperCAmelCase						:   Union[int, float] = 1 / 255    ,  UpperCAmelCase						:   bool = True    ,  UpperCAmelCase						:   bool = True    ,  UpperCAmelCase						:   Optional[Union[float, List[float]]] = None    ,  UpperCAmelCase						:   Optional[Union[float, List[float]]] = None    ,  **UpperCAmelCase						:   Any    ,  )    ->			None:
     super().__init__(**UpperCAmelCase			)
     lowerCamelCase__					:   Any      				=	size if size is not None else {'shortest_edge': 256}
     lowerCamelCase__					:   str      				=	get_size_dict(UpperCAmelCase    ,  default_to_square=UpperCAmelCase			)
     lowerCamelCase__					:   Tuple      				=	crop_size if crop_size is not None else {'height': 224, 'width': 224}
     lowerCamelCase__					:   List[str]      				=	get_size_dict(UpperCAmelCase    ,  param_name='crop_size'			)

     lowerCamelCase__					:   Tuple      				=	do_resize
     lowerCamelCase__					:   Union[str, Any]      				=	size
     lowerCamelCase__					:   Union[str, Any]      				=	do_center_crop
     lowerCamelCase__					:   Dict      				=	crop_size
     lowerCamelCase__					:   List[Any]      				=	resample
     lowerCamelCase__					:   Tuple      				=	do_rescale
     lowerCamelCase__					:   Optional[int]      				=	rescale_factor
     lowerCamelCase__					:   int      				=	offset
     lowerCamelCase__					:   Tuple      				=	do_normalize
     lowerCamelCase__					:   Union[str, Any]      				=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     lowerCamelCase__					:   str      				=	image_std if image_std is not None else IMAGENET_STANDARD_STD
 def   A_			(   self						:   Union[str, Any]    ,  UpperCAmelCase						:   np.ndarray    ,  UpperCAmelCase						:   Dict[str, int]    ,  UpperCAmelCase						:   PILImageResampling = PILImageResampling.BILINEAR    ,  UpperCAmelCase						:   Optional[Union[str, ChannelDimension]] = None    ,  **UpperCAmelCase						:   Tuple    ,  )    ->			np.ndarray:
     lowerCamelCase__					:   int      				=	get_size_dict(UpperCAmelCase    ,  default_to_square=UpperCAmelCase			)
     if "shortest_edge" in size:
         lowerCamelCase__					:   Optional[int]      				=	get_resize_output_image_size(UpperCAmelCase    ,  size['shortest_edge']    ,  default_to_square=UpperCAmelCase			)
     elif "height" in size and "width" in size:
         lowerCamelCase__					:   Dict      				=	(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   A_			(   self						:   str    ,  UpperCAmelCase						:   np.ndarray    ,  UpperCAmelCase						:   Dict[str, int]    ,  UpperCAmelCase						:   Optional[Union[str, ChannelDimension]] = None    ,  **UpperCAmelCase						:   int    ,  )    ->			np.ndarray:
     lowerCamelCase__					:   Optional[Any]      				=	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   A_			(   self						:   str    ,  UpperCAmelCase						:   np.ndarray    ,  UpperCAmelCase						:   Union[int, float]    ,  UpperCAmelCase						:   bool = True    ,  UpperCAmelCase						:   Optional[Union[str, ChannelDimension]] = None    ,  **UpperCAmelCase						:   str    ,  )    ->			List[str]:
     lowerCamelCase__					:   Optional[int]      				=	image.astype(np.floataa			)
     if offset:
         lowerCamelCase__					:   Optional[int]      				=	image - (scale / 2)
     return rescale(UpperCAmelCase    ,  scale=UpperCAmelCase    ,  data_format=UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Union[str, Any]    ,  UpperCAmelCase						:   np.ndarray    ,  UpperCAmelCase						:   Union[float, List[float]]    ,  UpperCAmelCase						:   Union[float, List[float]]    ,  UpperCAmelCase						:   Optional[Union[str, ChannelDimension]] = None    ,  **UpperCAmelCase						:   List[Any]    ,  )    ->			np.ndarray:
     return normalize(UpperCAmelCase    ,  mean=UpperCAmelCase    ,  std=UpperCAmelCase    ,  data_format=UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Any    ,  UpperCAmelCase						:   ImageInput    ,  UpperCAmelCase						:   bool = None    ,  UpperCAmelCase						:   Dict[str, int] = None    ,  UpperCAmelCase						:   PILImageResampling = None    ,  UpperCAmelCase						:   bool = None    ,  UpperCAmelCase						:   Dict[str, int] = None    ,  UpperCAmelCase						:   bool = None    ,  UpperCAmelCase						:   float = None    ,  UpperCAmelCase						:   bool = None    ,  UpperCAmelCase						:   bool = None    ,  UpperCAmelCase						:   Optional[Union[float, List[float]]] = None    ,  UpperCAmelCase						:   Optional[Union[float, List[float]]] = None    ,  UpperCAmelCase						:   Optional[ChannelDimension] = ChannelDimension.FIRST    ,  )    ->			np.ndarray:
     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.
     lowerCamelCase__					:   Tuple      				=	to_numpy_array(UpperCAmelCase			)

     if do_resize:
         lowerCamelCase__					:   int      				=	self.resize(image=UpperCAmelCase    ,  size=UpperCAmelCase    ,  resample=UpperCAmelCase			)

     if do_center_crop:
         lowerCamelCase__					:   str      				=	self.center_crop(UpperCAmelCase    ,  size=UpperCAmelCase			)

     if do_rescale:
         lowerCamelCase__					:   str      				=	self.rescale(image=UpperCAmelCase    ,  scale=UpperCAmelCase    ,  offset=UpperCAmelCase			)

     if do_normalize:
         lowerCamelCase__					:   Dict      				=	self.normalize(image=UpperCAmelCase    ,  mean=UpperCAmelCase    ,  std=UpperCAmelCase			)

     lowerCamelCase__					:   List[Any]      				=	to_channel_dimension_format(UpperCAmelCase    ,  UpperCAmelCase			)
     return image







 def   A_			(   self						:   int    ,  UpperCAmelCase						:   ImageInput    ,  UpperCAmelCase						:   bool = None    ,  UpperCAmelCase						:   Dict[str, int] = None    ,  UpperCAmelCase						:   PILImageResampling = None    ,  UpperCAmelCase						:   bool = None    ,  UpperCAmelCase						:   Dict[str, int] = None    ,  UpperCAmelCase						:   bool = None    ,  UpperCAmelCase						:   float = None    ,  UpperCAmelCase						:   bool = None    ,  UpperCAmelCase						:   bool = None    ,  UpperCAmelCase						:   Optional[Union[float, List[float]]] = None    ,  UpperCAmelCase						:   Optional[Union[float, List[float]]] = None    ,  UpperCAmelCase						:   Optional[Union[str, TensorType]] = None    ,  UpperCAmelCase						:   ChannelDimension = ChannelDimension.FIRST    ,  **UpperCAmelCase						:   int    ,  )    ->			PIL.Image.Image:
     lowerCamelCase__					:   List[str]      				=	do_resize if do_resize is not None else self.do_resize
     lowerCamelCase__					:   List[Any]      				=	resample if resample is not None else self.resample
     lowerCamelCase__					:   Dict      				=	do_center_crop if do_center_crop is not None else self.do_center_crop
     lowerCamelCase__					:   int      				=	do_rescale if do_rescale is not None else self.do_rescale
     lowerCamelCase__					:   str      				=	rescale_factor if rescale_factor is not None else self.rescale_factor
     lowerCamelCase__					:   Union[str, Any]      				=	offset if offset is not None else self.offset
     lowerCamelCase__					:   Optional[Any]      				=	do_normalize if do_normalize is not None else self.do_normalize
     lowerCamelCase__					:   Tuple      				=	image_mean if image_mean is not None else self.image_mean
     lowerCamelCase__					:   List[str]      				=	image_std if image_std is not None else self.image_std

     lowerCamelCase__					:   Union[str, Any]      				=	size if size is not None else self.size
     lowerCamelCase__					:   int      				=	get_size_dict(UpperCAmelCase    ,  default_to_square=UpperCAmelCase			)
     lowerCamelCase__					:   Optional[int]      				=	crop_size if crop_size is not None else self.crop_size
     lowerCamelCase__					:   List[Any]      				=	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.'			)

     lowerCamelCase__					:   Any      				=	make_batched(UpperCAmelCase			)

     lowerCamelCase__					:   List[Any]      				=	[
         [
             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
     ]

     lowerCamelCase__					:   Union[str, Any]      				=	{'pixel_values': videos}
     return BatchFeature(data=UpperCAmelCase    ,  tensor_type=UpperCAmelCase			)












import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available

class      lowerCAmelCase							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	["""image_processor""", """tokenizer"""]
 UpperCAmelCase__									=	"""OwlViTImageProcessor"""
 UpperCAmelCase__									=	("""CLIPTokenizer""", """CLIPTokenizerFast""")
 def __init__(   self						:   Optional[int]    ,  UpperCAmelCase						:   str=None    ,  UpperCAmelCase						:   Any=None    ,  **UpperCAmelCase						:   Optional[Any]			)    ->			Dict:
     lowerCamelCase__					:   Any      				=	None
     if "feature_extractor" in kwargs:
         warnings.warn(
             'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
             ' instead.'    ,  UpperCAmelCase    ,  )
         lowerCamelCase__					:   Optional[Any]      				=	kwargs.pop('feature_extractor'			)

     lowerCamelCase__					:   Dict      				=	image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
         raise ValueError('You need to specify an `image_processor`.'			)
     if tokenizer is None:
         raise ValueError('You need to specify a `tokenizer`.'			)

     super().__init__(UpperCAmelCase    ,  UpperCAmelCase			)
 def __call__(   self						:   List[Any]    ,  UpperCAmelCase						:   List[Any]=None    ,  UpperCAmelCase						:   Tuple=None    ,  UpperCAmelCase						:   int=None    ,  UpperCAmelCase						:   Optional[int]="max_length"    ,  UpperCAmelCase						:   Dict="np"    ,  **UpperCAmelCase						:   Union[str, Any]			)    ->			Union[str, Any]:

     if text is None and query_images is None and images is None:
         raise ValueError(
             'You have to specify at least one text or query image or image. All three cannot be none.'			)

     if text is not None:
         if isinstance(UpperCAmelCase    ,  UpperCAmelCase			) or (isinstance(UpperCAmelCase    ,  UpperCAmelCase			) and not isinstance(text[0]    ,  UpperCAmelCase			)):
             lowerCamelCase__					:   Any      				=	[self.tokenizer(UpperCAmelCase    ,  padding=UpperCAmelCase    ,  return_tensors=UpperCAmelCase    ,  **UpperCAmelCase			)]

         elif isinstance(UpperCAmelCase    ,  UpperCAmelCase			) and isinstance(text[0]    ,  UpperCAmelCase			):
             lowerCamelCase__					:   List[str]      				=	[]

             # Maximum number of queries across batch
             lowerCamelCase__					:   List[Any]      				=	max([len(UpperCAmelCase			) for t in text]			)

             # Pad all batch samples to max number of text queries
             for t in text:
                 if len(UpperCAmelCase			) != max_num_queries:
                     lowerCamelCase__					:   int      				=	t + [' '] * (max_num_queries - len(UpperCAmelCase			))

                 lowerCamelCase__					:   str      				=	self.tokenizer(UpperCAmelCase    ,  padding=UpperCAmelCase    ,  return_tensors=UpperCAmelCase    ,  **UpperCAmelCase			)
                 encodings.append(UpperCAmelCase			)
         else:
             raise TypeError('Input text should be a string, a list of strings or a nested list of strings'			)

         if return_tensors == "np":
             lowerCamelCase__					:   Optional[Any]      				=	np.concatenate([encoding['input_ids'] for encoding in encodings]    ,  axis=0			)
             lowerCamelCase__					:   int      				=	np.concatenate([encoding['attention_mask'] for encoding in encodings]    ,  axis=0			)

         elif return_tensors == "jax" and is_flax_available():
             import jax.numpy as jnp

             lowerCamelCase__					:   Optional[int]      				=	jnp.concatenate([encoding['input_ids'] for encoding in encodings]    ,  axis=0			)
             lowerCamelCase__					:   Any      				=	jnp.concatenate([encoding['attention_mask'] for encoding in encodings]    ,  axis=0			)

         elif return_tensors == "pt" and is_torch_available():
             import torch

             lowerCamelCase__					:   Union[str, Any]      				=	torch.cat([encoding['input_ids'] for encoding in encodings]    ,  dim=0			)
             lowerCamelCase__					:   Dict      				=	torch.cat([encoding['attention_mask'] for encoding in encodings]    ,  dim=0			)

         elif return_tensors == "tf" and is_tf_available():
             import tensorflow as tf

             lowerCamelCase__					:   Any      				=	tf.stack([encoding['input_ids'] for encoding in encodings]    ,  axis=0			)
             lowerCamelCase__					:   Optional[int]      				=	tf.stack([encoding['attention_mask'] for encoding in encodings]    ,  axis=0			)

         else:
             raise ValueError('Target return tensor type could not be returned'			)

         lowerCamelCase__					:   str      				=	BatchEncoding()
         lowerCamelCase__					:   int      				=	input_ids
         lowerCamelCase__					:   Any      				=	attention_mask

     if query_images is not None:
         lowerCamelCase__					:   Union[str, Any]      				=	BatchEncoding()
         lowerCamelCase__					:   Optional[Any]      				=	self.image_processor(
             UpperCAmelCase    ,  return_tensors=UpperCAmelCase    ,  **UpperCAmelCase			).pixel_values
         lowerCamelCase__					:   List[Any]      				=	query_pixel_values

     if images is not None:
         lowerCamelCase__					:   List[Any]      				=	self.image_processor(UpperCAmelCase    ,  return_tensors=UpperCAmelCase    ,  **UpperCAmelCase			)

     if text is not None and images is not None:
         lowerCamelCase__					:   Tuple      				=	image_features.pixel_values
         return encoding
     elif query_images is not None and images is not None:
         lowerCamelCase__					:   Dict      				=	image_features.pixel_values
         return encoding
     elif text is not None or query_images is not None:
         return encoding
     else:
         return BatchEncoding(data=dict(**UpperCAmelCase			)    ,  tensor_type=UpperCAmelCase			)
 def   A_			(   self						:   str    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   List[Any]			)    ->			Any:
     return self.image_processor.post_process(*UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   int    ,  *UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   Tuple			)    ->			str:
     return self.image_processor.post_process_object_detection(*UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Tuple    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   Optional[int]			)    ->			Optional[Any]:
     return self.image_processor.post_process_image_guided_detection(*UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Optional[Any]    ,  *UpperCAmelCase						:   Tuple    ,  **UpperCAmelCase						:   List[Any]			)    ->			Tuple:
     return self.tokenizer.batch_decode(*UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Union[str, Any]    ,  *UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   List[Any]			)    ->			List[str]:
     return self.tokenizer.decode(*UpperCAmelCase    ,  **UpperCAmelCase			)
 @property
 def   A_			(   self						:   List[str]			)    ->			Optional[Any]:
     warnings.warn(
         '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,  UpperCAmelCase    ,  )
     return self.image_processor_class







 @property
 def   A_			(   self						:   int			)    ->			int:
     warnings.warn(
         '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,  UpperCAmelCase    ,  )
     return self.image_processor





