





'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

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 TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


__lowerCAmelCase				:      List[str]						      =logging.get_logger(__name__)





def 		UpperCamelCase							(				_lowerCamelCase   :   Optional[int]			):
    if isinstance(_lowerCamelCase		,			(list, tuple)			) and isinstance(videos[0]		,			(list, tuple)			) and is_valid_image(videos[0][0]			):
        return videos

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

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

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






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							["""pixel_values"""]
 def __init__(							self :str		,			lowercase_ :bool = True		,			lowercase_ :Dict[str, int] = None		,			lowercase_ :PILImageResampling = PILImageResampling.BILINEAR		,			lowercase_ :bool = True		,			lowercase_ :Dict[str, int] = None		,			lowercase_ :bool = True		,			lowercase_ :Union[int, float] = 1 / 2_55		,			lowercase_ :bool = True		,			lowercase_ :Optional[Union[float, List[float]]] = None		,			lowercase_ :Optional[Union[float, List[float]]] = None		,			**lowercase_ :Optional[Any]		,			)->				None:
     super().__init__(**lowercase_				)
     A__							=     size if size is not None else {"shortest_edge": 2_24}
     A__							=     get_size_dict(lowercase_		,			default_to_square=lowercase_				)
     A__							=     crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
     A__							=     get_size_dict(lowercase_		,			param_name="crop_size"				)

     A__							=     do_resize
     A__							=     size
     A__							=     do_center_crop
     A__							=     crop_size
     A__							=     resample
     A__							=     do_rescale
     A__							=     rescale_factor
     A__							=     do_normalize
     A__							=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     A__							=     image_std if image_std is not None else IMAGENET_STANDARD_STD
 def 	UpperCAmelCase_				(							self :Union[str, Any]		,			lowercase_ :np.ndarray		,			lowercase_ :Dict[str, int]		,			lowercase_ :PILImageResampling = PILImageResampling.BILINEAR		,			lowercase_ :Optional[Union[str, ChannelDimension]] = None		,			**lowercase_ :Tuple		,			)->				np.ndarray:
     A__							=     get_size_dict(lowercase_		,			default_to_square=lowercase_				)
     if "shortest_edge" in size:
         A__							=     get_resize_output_image_size(lowercase_		,			size["shortest_edge"]		,			default_to_square=lowercase_				)
     elif "height" in size and "width" in size:
         A__							=     (size["height"], size["width"])
     else:
         raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"				)
     return resize(lowercase_		,			size=lowercase_		,			resample=lowercase_		,			data_format=lowercase_		,			**lowercase_				)
 def 	UpperCAmelCase_				(							self :Tuple		,			lowercase_ :np.ndarray		,			lowercase_ :Dict[str, int]		,			lowercase_ :Optional[Union[str, ChannelDimension]] = None		,			**lowercase_ :int		,			)->				np.ndarray:
     A__							=     get_size_dict(lowercase_				)
     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(lowercase_		,			size=(size["height"], size["width"])		,			data_format=lowercase_		,			**lowercase_				)
 def 	UpperCAmelCase_				(							self :List[Any]		,			lowercase_ :np.ndarray		,			lowercase_ :Union[int, float]		,			lowercase_ :Optional[Union[str, ChannelDimension]] = None		,			**lowercase_ :List[str]		,			)->				Optional[Any]:
     return rescale(lowercase_		,			scale=lowercase_		,			data_format=lowercase_		,			**lowercase_				)
 def 	UpperCAmelCase_				(							self :Union[str, Any]		,			lowercase_ :np.ndarray		,			lowercase_ :Union[float, List[float]]		,			lowercase_ :Union[float, List[float]]		,			lowercase_ :Optional[Union[str, ChannelDimension]] = None		,			**lowercase_ :Any		,			)->				np.ndarray:
     return normalize(lowercase_		,			mean=lowercase_		,			std=lowercase_		,			data_format=lowercase_		,			**lowercase_				)
 def 	UpperCAmelCase_				(							self :Dict		,			lowercase_ :ImageInput		,			lowercase_ :bool = None		,			lowercase_ :Dict[str, int] = None		,			lowercase_ :PILImageResampling = None		,			lowercase_ :bool = None		,			lowercase_ :Dict[str, int] = None		,			lowercase_ :bool = None		,			lowercase_ :float = None		,			lowercase_ :bool = None		,			lowercase_ :Optional[Union[float, List[float]]] = None		,			lowercase_ :Optional[Union[float, List[float]]] = None		,			lowercase_ :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."				)

     # All transformations expect numpy arrays.
     A__							=     to_numpy_array(lowercase_				)

     if do_resize:
         A__							=     self.resize(image=lowercase_		,			size=lowercase_		,			resample=lowercase_				)

     if do_center_crop:
         A__							=     self.center_crop(lowercase_		,			size=lowercase_				)

     if do_rescale:
         A__							=     self.rescale(image=lowercase_		,			scale=lowercase_				)

     if do_normalize:
         A__							=     self.normalize(image=lowercase_		,			mean=lowercase_		,			std=lowercase_				)

     A__							=     to_channel_dimension_format(lowercase_		,			lowercase_				)
     return image



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

     A__							=     size if size is not None else self.size
     A__							=     get_size_dict(lowercase_		,			default_to_square=lowercase_				)
     A__							=     crop_size if crop_size is not None else self.crop_size
     A__							=     get_size_dict(lowercase_		,			param_name="crop_size"				)

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

     A__							=     make_batched(lowercase_				)

     A__							=     [
         [
             self._preprocess_image(
                 image=lowercase_		,			do_resize=lowercase_		,			size=lowercase_		,			resample=lowercase_		,			do_center_crop=lowercase_		,			crop_size=lowercase_		,			do_rescale=lowercase_		,			rescale_factor=lowercase_		,			do_normalize=lowercase_		,			image_mean=lowercase_		,			image_std=lowercase_		,			data_format=lowercase_		,			)
             for img in video
         ]
         for video in videos
     ]

     A__							=     {"pixel_values": videos}
     return BatchFeature(data=lowercase_		,			tensor_type=lowercase_				)










'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							["""image_processor""", """tokenizer"""]
 __lowercase  						=							"""CLIPImageProcessor"""
 __lowercase  						=							("""CLIPTokenizer""", """CLIPTokenizerFast""")
 def __init__(							self :List[Any]		,			lowercase_ :List[str]=None		,			lowercase_ :Any=None		,			**lowercase_ :Union[str, Any]				)->				Optional[Any]:
     A__							=     None
     if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."		,			lowercase_		,			)
         A__							=     kwargs.pop("feature_extractor"				)

     A__							=     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__(lowercase_		,			lowercase_				)
 def __call__(							self :Optional[Any]		,			lowercase_ :Optional[int]=None		,			lowercase_ :Dict=None		,			lowercase_ :List[Any]=None		,			**lowercase_ :Optional[Any]				)->				Union[str, Any]:

     if text is None and images is None:
         raise ValueError("You have to specify either text or images. Both cannot be none."				)

     if text is not None:
         A__							=     self.tokenizer(lowercase_		,			return_tensors=lowercase_		,			**lowercase_				)

     if images is not None:
         A__							=     self.image_processor(lowercase_		,			return_tensors=lowercase_		,			**lowercase_				)

     if text is not None and images is not None:
         A__							=     image_features.pixel_values
         return encoding
     elif text is not None:
         return encoding
     else:
         return BatchEncoding(data=dict(**lowercase_				)		,			tensor_type=lowercase_				)
 def 	UpperCAmelCase_				(							self :int		,			*lowercase_ :Tuple		,			**lowercase_ :Any				)->				Dict:
     return self.tokenizer.batch_decode(*lowercase_		,			**lowercase_				)
 def 	UpperCAmelCase_				(							self :Any		,			*lowercase_ :Any		,			**lowercase_ :Any				)->				List[str]:
     return self.tokenizer.decode(*lowercase_		,			**lowercase_				)
 @property
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Optional[int]:
     A__							=     self.tokenizer.model_input_names
     A__							=     self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)
 @property
 def 	UpperCAmelCase_				(							self :List[Any]				)->				str:
     warnings.warn(
         "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,			lowercase_		,			)
     return self.image_processor_class



 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				List[Any]:
     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,			lowercase_		,			)
     return self.image_processor



