from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		logging.get_logger(__name__)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      ["""pixel_values"""]

  def __init__(		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   Dict[str, int] = None  ,				__SCREAMING_SNAKE_CASE			:   PILImageResampling = PILImageResampling.BICUBIC  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   Dict[str, int] = None  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   Union[int, float] = 1 / 2_55  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)
   __a							=	size if size is not None else {"shortest_edge": 2_24}
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE  ,				default_to_square=__SCREAMING_SNAKE_CASE							)
   __a							=	crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE  ,				param_name="crop_size"							)

   __a							=	do_resize
   __a							=	size
   __a							=	resample
   __a							=	do_center_crop
   __a							=	crop_size
   __a							=	do_rescale
   __a							=	rescale_factor
   __a							=	do_normalize
   __a							=	image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
   __a							=	image_std if image_std is not None else IMAGENET_DEFAULT_STD

  def 		_UpperCAmelCase (		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   np.ndarray  ,				__SCREAMING_SNAKE_CASE			:   Dict[str, int]  ,				__SCREAMING_SNAKE_CASE			:   PILImageResampling = PILImageResampling.BICUBIC  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, ChannelDimension]] = None  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]  ,				):
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE  ,				default_to_square=__SCREAMING_SNAKE_CASE							)
   # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
   if "shortest_edge" in size:
    __a							=	int((2_56 / 2_24) * size["shortest_edge"]							)
    __a							=	get_resize_output_image_size(__SCREAMING_SNAKE_CASE  ,				size=__SCREAMING_SNAKE_CASE  ,				default_to_square=__SCREAMING_SNAKE_CASE							)
    __a							=	{"height": output_size[0], "width": output_size[1]}
   if "height" not in size_dict or "width" not in size_dict:
    raise ValueError(
        f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""							)
   return resize(
       __SCREAMING_SNAKE_CASE  ,				size=(size_dict["height"], size_dict["width"])  ,				resample=__SCREAMING_SNAKE_CASE  ,				data_format=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   np.ndarray  ,				__SCREAMING_SNAKE_CASE			:   Dict[str, int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, ChannelDimension]] = None  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]  ,				):
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE							)
   if "height" not in size or "width" not in size:
    raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""							)
   return center_crop(__SCREAMING_SNAKE_CASE  ,				size=(size["height"], size["width"])  ,				data_format=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   int  ,				__SCREAMING_SNAKE_CASE			:   np.ndarray  ,				__SCREAMING_SNAKE_CASE			:   Union[int, float]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, ChannelDimension]] = None  ,				**__SCREAMING_SNAKE_CASE			:   Dict  ,				):
   return rescale(__SCREAMING_SNAKE_CASE  ,				scale=__SCREAMING_SNAKE_CASE  ,				data_format=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   np.ndarray  ,				__SCREAMING_SNAKE_CASE			:   Union[float, List[float]]  ,				__SCREAMING_SNAKE_CASE			:   Union[float, List[float]]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, ChannelDimension]] = None  ,				**__SCREAMING_SNAKE_CASE			:   int  ,				):
   return normalize(__SCREAMING_SNAKE_CASE  ,				mean=__SCREAMING_SNAKE_CASE  ,				std=__SCREAMING_SNAKE_CASE  ,				data_format=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   ImageInput  ,				__SCREAMING_SNAKE_CASE			:   Optional[bool] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Dict[str, int]] = None  ,				__SCREAMING_SNAKE_CASE			:   PILImageResampling = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[bool] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Dict[str, int]] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[bool] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[float] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[bool] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, Iterable[float]]] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, Iterable[float]]] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[TensorType] = None  ,				__SCREAMING_SNAKE_CASE			:   ChannelDimension = ChannelDimension.FIRST  ,				**__SCREAMING_SNAKE_CASE			:   str  ,				):
   __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(__SCREAMING_SNAKE_CASE  ,				default_to_square=__SCREAMING_SNAKE_CASE							)
   __a							=	crop_size if crop_size is not None else self.crop_size
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE  ,				param_name="crop_size"							)

   __a							=	make_list_of_images(__SCREAMING_SNAKE_CASE							)

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

   if do_resize and size is None:
    raise ValueError("Size 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(__SCREAMING_SNAKE_CASE							) for image in images]

   if do_resize:
    __a							=	[self.resize(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							) for image in images]

   if do_center_crop:
    __a							=	[self.center_crop(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							) for image in images]

   if do_rescale:
    __a							=	[self.rescale(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							) for image in images]

   if do_normalize:
    __a							=	[self.normalize(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							) for image in images]

   __a							=	[to_channel_dimension_format(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							) for image in images]

   __a							=	{"pixel_values": images}
   return BatchFeature(data=__SCREAMING_SNAKE_CASE  ,				tensor_type=__SCREAMING_SNAKE_CASE							)



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      ["""image_processor""", """tokenizer"""]
  _SCREAMING_SNAKE_CASE     					=      """ChineseCLIPImageProcessor"""
  _SCREAMING_SNAKE_CASE     					=      ("""BertTokenizer""", """BertTokenizerFast""")

  def __init__(		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Any=None  ,				__SCREAMING_SNAKE_CASE			:   Tuple=None  ,				**__SCREAMING_SNAKE_CASE			:   List[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."  ,				__SCREAMING_SNAKE_CASE  ,				)
    __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__(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	self.image_processor

  def __call__(		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Dict=None  ,				__SCREAMING_SNAKE_CASE			:   Dict=None  ,				__SCREAMING_SNAKE_CASE			:   Tuple=None  ,				**__SCREAMING_SNAKE_CASE			:   List[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(__SCREAMING_SNAKE_CASE  ,				return_tensors=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   if images is not None:
    __a							=	self.image_processor(__SCREAMING_SNAKE_CASE  ,				return_tensors=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   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(**__SCREAMING_SNAKE_CASE							)  ,				tensor_type=__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   int  ,				*__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				**__SCREAMING_SNAKE_CASE			:   str							):
   return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				*__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]							):
   return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  @property
  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __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			:   Any							):
   warnings.warn(
       "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,				__SCREAMING_SNAKE_CASE  ,				)
   return self.image_processor_class


