





"""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 convert_to_rgb, 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


if is_vision_available():
  import PIL


lowerCamelCase__				  =   logging.get_logger(__name__)



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):
  SCREAMING_SNAKE_CASE__							:str	     =					["pixel_values"]

  def __init__(		self   :       int					,   __a   :       bool = True					,   __a   :       Dict[str, int] = None					,   __a   :       PILImageResampling = PILImageResampling.BICUBIC					,   __a   :       bool = True					,   __a   :       Union[int, float] = 1 / 255					,   __a   :       bool = True					,   __a   :       Optional[Union[float, List[float]]] = None					,   __a   :       Optional[Union[float, List[float]]] = None					,   __a   :       bool = True					,   **__a   :       str					,   )		->				None:
        super().__init__(**__a	)
        _UpperCamelCase      :	Optional[Any]						=       size if size is not None else {"height": 384, "width": 384}
        _UpperCamelCase      :	str						=       get_size_dict(__a					,   default_to_square=__a	)

        _UpperCamelCase      :	Optional[Any]						=       do_resize
        _UpperCamelCase      :	List[str]						=       size
        _UpperCamelCase      :	Dict						=       resample
        _UpperCamelCase      :	List[str]						=       do_rescale
        _UpperCamelCase      :	str						=       rescale_factor
        _UpperCamelCase      :	Tuple						=       do_normalize
        _UpperCamelCase      :	Any						=       image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        _UpperCamelCase      :	str						=       image_std if image_std is not None else OPENAI_CLIP_STD
        _UpperCamelCase      :	str						=       do_convert_rgb

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]					,   __a   :       np.ndarray					,   __a   :       Dict[str, int]					,   __a   :       PILImageResampling = PILImageResampling.BICUBIC					,   __a   :       Optional[Union[str, ChannelDimension]] = None					,   **__a   :       List[str]					,   )		->				np.ndarray:
        _UpperCamelCase      :	int						=       get_size_dict(__a					,   default_to_square=__a	)
        if "height" not in size or "width" not in size:
              raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''	)
        _UpperCamelCase      :	str						=       (size["height"], size["width"])
        return resize(__a					,   size=__a					,   resample=__a					,   data_format=__a					,   **__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]					,   __a   :       np.ndarray					,   __a   :       Union[int, float]					,   __a   :       Optional[Union[str, ChannelDimension]] = None					,   **__a   :       List[Any]					,   )		->				List[str]:
        return rescale(__a					,   scale=__a					,   data_format=__a					,   **__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]					,   __a   :       np.ndarray					,   __a   :       Union[float, List[float]]					,   __a   :       Union[float, List[float]]					,   __a   :       Optional[Union[str, ChannelDimension]] = None					,   **__a   :       Tuple					,   )		->				np.ndarray:
        return normalize(__a					,   mean=__a					,   std=__a					,   data_format=__a					,   **__a	)


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

        _UpperCamelCase      :	Union[str, Any]						=       size if size is not None else self.size
        _UpperCamelCase      :	Union[str, Any]						=       get_size_dict(__a					,   default_to_square=__a	)

        _UpperCamelCase      :	List[Any]						=       make_list_of_images(__a	)

        if not valid_images(__a	):
              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 or resample is None:
              raise ValueError("Size and resample must be specified if do_resize 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      :	List[Any]						=       [convert_to_rgb(__a	) for image in images]

        # All transformations expect numpy arrays.
        _UpperCamelCase      :	List[str]						=       [to_numpy_array(__a	) for image in images]

        if do_resize:
              _UpperCamelCase      :	List[str]						=       [self.resize(image=__a					,   size=__a					,   resample=__a	) for image in images]

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

        if do_normalize:
              _UpperCamelCase      :	int						=       [self.normalize(image=__a					,   mean=__a					,   std=__a	) for image in images]

        _UpperCamelCase      :	Union[str, Any]						=       [to_channel_dimension_format(__a					,   __a	) for image in images]

        _UpperCamelCase      :	Union[str, Any]						=       BatchFeature(data={"pixel_values": images}					,   tensor_type=__a	)

        return encoded_outputs







"""simple docstring"""


lowerCamelCase__				  =   "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowerCamelCase__				  =   [{"type": "code", "content": INSTALL_CONTENT}]
lowerCamelCase__				  =   {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}
