






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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


__a:     Union[str, Any]   					=					logging.get_logger(__name__)




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						List[Any]:
       _UpperCAmelCase							=    b.T
       _UpperCAmelCase							=    np.sum(np.square(__snake_case )   ,      axis=1 )
       _UpperCAmelCase							=    np.sum(np.square(__snake_case )   ,      axis=0 )
       _UpperCAmelCase							=    np.matmul(__snake_case   ,      __snake_case )
       _UpperCAmelCase							=    aa[:, None] - 2 * ab + ba[None, :]
       return d




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Tuple:
       _UpperCAmelCase							=    x.reshape(-1   ,      3 )
       _UpperCAmelCase							=    squared_euclidean_distance(__snake_case   ,      __snake_case )
       return np.argmin(__snake_case   ,      axis=1 )





class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  ['''pixel_values''']


  def __init__(      self		:			Dict       ,				lowerCamelCase		:			Optional[Union[List[List[int]], np.ndarray]] = None       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			Dict[str, int] = None       ,				lowerCamelCase		:			PILImageResampling = PILImageResampling.BILINEAR       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			bool = True       ,				**lowerCamelCase		:			Dict       ,				)						->	None:


         """simple docstring"""
         super().__init__(**lowerCamelCase							)
         _UpperCAmelCase							=    size if size is not None else {"""height""": 256, """width""": 256}
         _UpperCAmelCase							=    get_size_dict(lowerCamelCase							)
         _UpperCAmelCase							=    np.array(lowerCamelCase							) if clusters is not None else None
         _UpperCAmelCase							=    do_resize
         _UpperCAmelCase							=    size
         _UpperCAmelCase							=    resample
         _UpperCAmelCase							=    do_normalize
         _UpperCAmelCase							=    do_color_quantize


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			np.ndarray       ,				lowerCamelCase		:			Dict[str, int]       ,				lowerCamelCase		:			PILImageResampling = PILImageResampling.BILINEAR       ,				lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None       ,				**lowerCamelCase		:			Any       ,				)						->	np.ndarray:


         """simple docstring"""
         _UpperCAmelCase							=    get_size_dict(lowerCamelCase							)
         if "height" not in size or "width" not in size:
                raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""							)
         return resize(
             lowerCamelCase       ,				size=(size["""height"""], size["""width"""])       ,				resample=lowerCamelCase       ,				data_format=lowerCamelCase       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			np.ndarray       ,				lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None       ,				)						->	np.ndarray:


         """simple docstring"""
         _UpperCAmelCase							=    rescale(image=lowerCamelCase       ,				scale=1 / 127.5       ,				data_format=lowerCamelCase							)
         _UpperCAmelCase							=    image - 1
         return image





  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			ImageInput       ,				lowerCamelCase		:			bool = None       ,				lowerCamelCase		:			Dict[str, int] = None       ,				lowerCamelCase		:			PILImageResampling = None       ,				lowerCamelCase		:			bool = None       ,				lowerCamelCase		:			Optional[bool] = None       ,				lowerCamelCase		:			Optional[Union[List[List[int]], np.ndarray]] = None       ,				lowerCamelCase		:			Optional[Union[str, TensorType]] = None       ,				lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST       ,				**lowerCamelCase		:			List[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(lowerCamelCase							)
         _UpperCAmelCase							=    resample if resample is not None else self.resample
         _UpperCAmelCase							=    do_normalize if do_normalize is not None else self.do_normalize
         _UpperCAmelCase							=    do_color_quantize if do_color_quantize is not None else self.do_color_quantize
         _UpperCAmelCase							=    clusters if clusters is not None else self.clusters
         _UpperCAmelCase							=    np.array(lowerCamelCase							)

         _UpperCAmelCase							=    make_list_of_images(lowerCamelCase							)

         if not valid_images(lowerCamelCase							):
                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_color_quantize and clusters is None:
                raise ValueError("""Clusters must be specified if do_color_quantize is True."""							)

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

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

         if do_normalize:
                _UpperCAmelCase							=    [self.normalize(image=lowerCamelCase							) for image in images]

         if do_color_quantize:
                _UpperCAmelCase							=    [to_channel_dimension_format(lowerCamelCase       ,				ChannelDimension.LAST							) for image in images]
                # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
                _UpperCAmelCase							=    np.array(lowerCamelCase							)
                _UpperCAmelCase							=    color_quantize(lowerCamelCase       ,				lowerCamelCase							).reshape(images.shape[:-1]							)

                # flatten to (batch_size, height*width)
                _UpperCAmelCase							=    images.shape[0]
                _UpperCAmelCase							=    images.reshape(lowerCamelCase       ,				-1							)

                # We need to convert back to a list of images to keep consistent behaviour across processors.
                _UpperCAmelCase							=    list(lowerCamelCase							)
         else:
                _UpperCAmelCase							=    [to_channel_dimension_format(lowerCamelCase       ,				lowerCamelCase							) for image in images]

         _UpperCAmelCase							=    {"""input_ids""": images}
         return BatchFeature(data=lowerCamelCase       ,				tensor_type=lowerCamelCase							)


A_			:					Union[str, Any]    			=     '\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'

A_			:					str    			=     [{'type': 'code', 'content': INSTALL_CONTENT}]
A_			:					Any    			=     {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}



