



"""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 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


UpperCAmelCase						:		str			= logging.get_logger(__name__)


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						Any:







  '''simple docstring'''


  lowercase_								= b.T
  lowercase_								= np.sum(np.square(__lowerCAmelCase							) ,  axis=1							)
  lowercase_								= np.sum(np.square(__lowerCAmelCase							) ,  axis=0							)
  lowercase_								= np.matmul(__lowerCAmelCase ,  __lowerCAmelCase							)
  lowercase_								= aa[:, None] - 2 * ab + ba[None, :]
  return d


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						Tuple:







  '''simple docstring'''


  lowercase_								= x.reshape(-1 ,  3							)
  lowercase_								= squared_euclidean_distance(__lowerCAmelCase ,  __lowerCAmelCase							)
  return np.argmin(__lowerCAmelCase ,  axis=1							)


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   ["pixel_values"]

    def __init__(				self			:				Optional[Any] ,  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_			:				Tuple ,  ):







      """simple docstring"""







      super().__init__(**lowerCAmelCase_)
      lowercase_								= size if size is not None else {"""height""": 2_5_6, """width""": 2_5_6}
      lowercase_								= get_size_dict(lowerCAmelCase_)
      lowercase_								= np.array(lowerCAmelCase_) if clusters is not None else None
      lowercase_								= do_resize
      lowercase_								= size
      lowercase_								= resample
      lowercase_								= do_normalize
      lowercase_								= do_color_quantize

    def 					_UpperCAmelCase	(				self			:				Optional[Any] ,  lowerCAmelCase_			:				np.ndarray ,  lowerCAmelCase_			:				Dict[str, int] ,  lowerCAmelCase_			:				PILImageResampling = PILImageResampling.BILINEAR ,  lowerCAmelCase_			:				Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase_			:				Tuple ,  ):







      """simple docstring"""







      lowercase_								= 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 					_UpperCAmelCase	(				self			:				Optional[Any] ,  lowerCAmelCase_			:				np.ndarray ,  lowerCAmelCase_			:				Optional[Union[str, ChannelDimension]] = None ,  ):







      """simple docstring"""







      lowercase_								= rescale(image=lowerCAmelCase_ ,  scale=1 / 127.5 ,  data_format=lowerCAmelCase_)
      lowercase_								= image - 1
      return image




    def 					_UpperCAmelCase	(				self			:				Dict ,  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_			:				Any ,  ):







      """simple docstring"""







      lowercase_								= do_resize if do_resize is not None else self.do_resize
      lowercase_								= size if size is not None else self.size
      lowercase_								= get_size_dict(lowerCAmelCase_)
      lowercase_								= resample if resample is not None else self.resample
      lowercase_								= do_normalize if do_normalize is not None else self.do_normalize
      lowercase_								= do_color_quantize if do_color_quantize is not None else self.do_color_quantize
      lowercase_								= clusters if clusters is not None else self.clusters
      lowercase_								= np.array(lowerCAmelCase_)

      lowercase_								= 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.
      lowercase_								= [to_numpy_array(lowerCAmelCase_) for image in images]

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

      if do_normalize:
        lowercase_								= [self.normalize(image=lowerCAmelCase_) for image in images]

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

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

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

      lowercase_								= {"""input_ids""": images}
      return BatchFeature(data=lowerCAmelCase_ ,  tensor_type=lowerCAmelCase_)






"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase						:		List[Any]			= logging.get_logger(__name__)


UpperCAmelCase						:		List[Any]			= "▁"

UpperCAmelCase						:		Union[str, Any]			= {"vocab_file": "spiece.model"}

UpperCAmelCase						:		List[Any]			= {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

UpperCAmelCase						:		Optional[Any]			= {
    "google/reformer-crime-and-punishment": 52_4288,
}


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   VOCAB_FILES_NAMES
    lowercase__      =   PRETRAINED_VOCAB_FILES_MAP
    lowercase__      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__      =   ["input_ids", "attention_mask"]

    def __init__(				self			:				int ,  lowerCAmelCase_			:				Any ,  lowerCAmelCase_			:				Dict="</s>" ,  lowerCAmelCase_			:				Dict="<unk>" ,  lowerCAmelCase_			:				Dict=[] ,  lowerCAmelCase_			:				Optional[Dict[str, Any]] = None ,  **lowerCAmelCase_			:				List[str] ,  ):







      """simple docstring"""







      lowercase_								= {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          eos_token=lowerCAmelCase_ ,  unk_token=lowerCAmelCase_ ,  additional_special_tokens=lowerCAmelCase_ ,  sp_model_kwargs=self.sp_model_kwargs ,  **lowerCAmelCase_ ,  )

      lowercase_								= vocab_file
      lowercase_								= spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(lowerCAmelCase_)

    @property
    def 					_UpperCAmelCase	(				self			:				List[Any]):







      """simple docstring"""







      return self.sp_model.get_piece_size()

    def 					_UpperCAmelCase	(				self			:				Dict):







      """simple docstring"""







      lowercase_								= {self.convert_ids_to_tokens(lowerCAmelCase_): i for i in range(self.vocab_size)}
      vocab.update(self.added_tokens_encoder)
      return vocab

    def __getstate__(				self			:				Union[str, Any]):







      """simple docstring"""







      lowercase_								= self.__dict__.copy()
      lowercase_								= None
      return state

    def __setstate__(				self			:				Optional[Any] ,  lowerCAmelCase_			:				int):







      """simple docstring"""







      lowercase_								= d

      # for backward compatibility
      if not hasattr(self ,  """sp_model_kwargs"""):
        lowercase_								= {}

      lowercase_								= spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(self.vocab_file)

    def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				str):







      """simple docstring"""







      return self.sp_model.encode(lowerCAmelCase_ ,  out_type=lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				List[str]):







      """simple docstring"""







      return self.sp_model.piece_to_id(lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				str ,  lowerCAmelCase_			:				Any):







      """simple docstring"""







      if index < self.sp_model.get_piece_size():
        lowercase_								= self.sp_model.IdToPiece(lowerCAmelCase_)
      return token

    def 					_UpperCAmelCase	(				self			:				int ,  lowerCAmelCase_			:				Any):







      """simple docstring"""







      lowercase_								= []
      lowercase_								= """"""
      for token in tokens:
        # make sure that special tokens are not decoded using sentencepiece model
        if token in self.all_special_tokens:
          out_string += self.sp_model.decode(lowerCAmelCase_) + token
          lowercase_								= []
        else:
          current_sub_tokens.append(lowerCAmelCase_)
      out_string += self.sp_model.decode(lowerCAmelCase_)
      return out_string.strip()




    def 					_UpperCAmelCase	(				self			:				Optional[int] ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				Optional[str] = None):







      """simple docstring"""







      if not os.path.isdir(lowerCAmelCase_):
        logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
        return
      lowercase_								= os.path.join(
          lowerCAmelCase_ ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

      if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase_) and os.path.isfile(self.vocab_file):
        copyfile(self.vocab_file ,  lowerCAmelCase_)
      elif not os.path.isfile(self.vocab_file):
        with open(lowerCAmelCase_ ,  """wb""") as fi:
          lowercase_								= self.sp_model.serialized_model_proto()
          fi.write(lowerCAmelCase_)

      return (out_vocab_file,)

