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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCamelCase__											=   logging.get_logger(__name__)


if is_vision_available():
      import PIL





class 	A				(							UpperCAmelCase_	):
   __UpperCAmelCase					:					Optional[int]    =     ['pixel_values']


   def __init__(self	:		Tuple			,    __UpperCAmelCase	:		bool = True			,    __UpperCAmelCase	:		Dict[str, int] = None			,    __UpperCAmelCase	:		PILImageResampling = PILImageResampling.BICUBIC			,    __UpperCAmelCase	:		bool = True			,    __UpperCAmelCase	:		Dict[str, int] = None			,    __UpperCAmelCase	:		bool = True			,    __UpperCAmelCase	:		Union[int, float] = 1 / 2_5_5			,    __UpperCAmelCase	:		bool = True			,    __UpperCAmelCase	:		Optional[Union[float, List[float]]] = None			,    __UpperCAmelCase	:		Optional[Union[float, List[float]]] = None			,    __UpperCAmelCase	:		bool = True			,    **__UpperCAmelCase	:		Tuple			,    )		->							None:
         """simple docstring"""



         super().__init__(**__SCREAMING_SNAKE_CASE						)
         UpperCAmelCase__								=    size if size is not None else {"shortest_edge": 2_2_4}
         UpperCAmelCase__								=    get_size_dict(__SCREAMING_SNAKE_CASE			,    default_to_square=__SCREAMING_SNAKE_CASE						)
         UpperCAmelCase__								=    crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
         UpperCAmelCase__								=    get_size_dict(__SCREAMING_SNAKE_CASE			,    default_to_square=__SCREAMING_SNAKE_CASE			,    param_name="crop_size"						)

         UpperCAmelCase__								=    do_resize
         UpperCAmelCase__								=    size
         UpperCAmelCase__								=    resample
         UpperCAmelCase__								=    do_center_crop
         UpperCAmelCase__								=    crop_size
         UpperCAmelCase__								=    do_rescale
         UpperCAmelCase__								=    rescale_factor
         UpperCAmelCase__								=    do_normalize
         UpperCAmelCase__								=    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         UpperCAmelCase__								=    image_std if image_std is not None else OPENAI_CLIP_STD
         UpperCAmelCase__								=    do_convert_rgb


   def      lowercase_ (self	:		Optional[int]			,    __UpperCAmelCase	:		np.ndarray			,    __UpperCAmelCase	:		Dict[str, int]			,    __UpperCAmelCase	:		PILImageResampling = PILImageResampling.BICUBIC			,    __UpperCAmelCase	:		Optional[Union[str, ChannelDimension]] = None			,    **__UpperCAmelCase	:		List[Any]			,    )		->							np.ndarray:
         """simple docstring"""



         UpperCAmelCase__								=    get_size_dict(__SCREAMING_SNAKE_CASE			,    default_to_square=__SCREAMING_SNAKE_CASE						)
         if "shortest_edge" not in size:
               raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""						)
         UpperCAmelCase__								=    get_resize_output_image_size(__SCREAMING_SNAKE_CASE			,    size=size["shortest_edge"]			,    default_to_square=__SCREAMING_SNAKE_CASE						)
         return resize(__SCREAMING_SNAKE_CASE			,    size=__SCREAMING_SNAKE_CASE			,    resample=__SCREAMING_SNAKE_CASE			,    data_format=__SCREAMING_SNAKE_CASE			,    **__SCREAMING_SNAKE_CASE						)


   def      lowercase_ (self	:		List[str]			,    __UpperCAmelCase	:		np.ndarray			,    __UpperCAmelCase	:		Dict[str, int]			,    __UpperCAmelCase	:		Optional[Union[str, ChannelDimension]] = None			,    **__UpperCAmelCase	:		Optional[Any]			,    )		->							np.ndarray:
         """simple docstring"""



         UpperCAmelCase__								=    get_size_dict(__SCREAMING_SNAKE_CASE						)
         if "height" not in size or "width" not in size:
               raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""						)
         return center_crop(__SCREAMING_SNAKE_CASE			,    size=(size["height"], size["width"])			,    data_format=__SCREAMING_SNAKE_CASE			,    **__SCREAMING_SNAKE_CASE						)


   def      lowercase_ (self	:		Union[str, Any]			,    __UpperCAmelCase	:		np.ndarray			,    __UpperCAmelCase	:		Union[int, float]			,    __UpperCAmelCase	:		Optional[Union[str, ChannelDimension]] = None			,    **__UpperCAmelCase	:		Optional[Any]			,    )		->							Optional[Any]:
         """simple docstring"""



         return rescale(__SCREAMING_SNAKE_CASE			,    scale=__SCREAMING_SNAKE_CASE			,    data_format=__SCREAMING_SNAKE_CASE			,    **__SCREAMING_SNAKE_CASE						)


   def      lowercase_ (self	:		Union[str, Any]			,    __UpperCAmelCase	:		np.ndarray			,    __UpperCAmelCase	:		Union[float, List[float]]			,    __UpperCAmelCase	:		Union[float, List[float]]			,    __UpperCAmelCase	:		Optional[Union[str, ChannelDimension]] = None			,    **__UpperCAmelCase	:		Optional[int]			,    )		->							np.ndarray:
         """simple docstring"""



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





   def      lowercase_ (self	:		Dict			,    __UpperCAmelCase	:		ImageInput			,    __UpperCAmelCase	:		bool = None			,    __UpperCAmelCase	:		Dict[str, int] = None			,    __UpperCAmelCase	:		PILImageResampling = None			,    __UpperCAmelCase	:		bool = None			,    __UpperCAmelCase	:		int = None			,    __UpperCAmelCase	:		bool = None			,    __UpperCAmelCase	:		float = None			,    __UpperCAmelCase	:		bool = None			,    __UpperCAmelCase	:		Optional[Union[float, List[float]]] = None			,    __UpperCAmelCase	:		Optional[Union[float, List[float]]] = None			,    __UpperCAmelCase	:		bool = None			,    __UpperCAmelCase	:		Optional[Union[str, TensorType]] = None			,    __UpperCAmelCase	:		Optional[ChannelDimension] = ChannelDimension.FIRST			,    **__UpperCAmelCase	:		str			,    )		->							PIL.Image.Image:
         """simple docstring"""



         UpperCAmelCase__								=    do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase__								=    size if size is not None else self.size
         UpperCAmelCase__								=    get_size_dict(__SCREAMING_SNAKE_CASE			,    param_name="size"			,    default_to_square=__SCREAMING_SNAKE_CASE						)
         UpperCAmelCase__								=    resample if resample is not None else self.resample
         UpperCAmelCase__								=    do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase__								=    crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase__								=    get_size_dict(__SCREAMING_SNAKE_CASE			,    param_name="crop_size"			,    default_to_square=__SCREAMING_SNAKE_CASE						)
         UpperCAmelCase__								=    do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase__								=    rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase__								=    do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase__								=    image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase__								=    image_std if image_std is not None else self.image_std
         UpperCAmelCase__								=    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         UpperCAmelCase__								=    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."						)

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
               UpperCAmelCase__								=    [convert_to_rgb(__SCREAMING_SNAKE_CASE						) for image in images]

         # All transformations expect numpy arrays.
         UpperCAmelCase__								=    [to_numpy_array(__SCREAMING_SNAKE_CASE						) for image in images]

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

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

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

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

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

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






'''simple docstring'''



import warnings

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







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = ["image_processor", "tokenizer"]
 lowerCAmelCase__       = "CLIPImageProcessor"
 lowerCAmelCase__       = ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")
 def __init__(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			str=None   ,				**__SCREAMING_SNAKE_CASE   :			Tuple     )    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = 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   ,				)
           __SCREAMING_SNAKE_CASE      = kwargs.pop("""feature_extractor"""     )

      __SCREAMING_SNAKE_CASE      = 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     )
 def __call__(						self   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=None   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					int:


      """simple docstring"""




      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:
           __SCREAMING_SNAKE_CASE      = self.tokenizer(__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

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

      if text is not None and images is not None:
           __SCREAMING_SNAKE_CASE      = 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   :			List[str]   ,				*__SCREAMING_SNAKE_CASE   :			Tuple   ,				**__SCREAMING_SNAKE_CASE   :			List[Any]     )    ->					Optional[int]:


      """simple docstring"""




      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[Any]   ,				*__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					Optional[Any]:


      """simple docstring"""




      return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )


 @property
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.tokenizer.model_input_names
      __SCREAMING_SNAKE_CASE      = self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )






