

"""simple docstring"""






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_fnet import FNetTokenizer
else:
 __lowerCamelCase					    =		None

__lowerCamelCase					    =		logging.get_logger(__name__)
__lowerCamelCase					    =		{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase					    =		{
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json""",
    },
}

__lowerCamelCase					    =		{
    """google/fnet-base""": 5_12,
    """google/fnet-large""": 5_12,
}

__lowerCamelCase					    =		"""▁"""
class 							_lowercase       (  a_  ):
     _lowerCamelCase	 =							VOCAB_FILES_NAMES
     _lowerCamelCase	 =							PRETRAINED_VOCAB_FILES_MAP
     _lowerCamelCase	 =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _lowerCamelCase	 =							['''input_ids''', '''token_type_ids''']
     _lowerCamelCase	 =							FNetTokenizer







     def __init__(   self			,    UpperCamelCase_=None			,    UpperCamelCase_=None			,    UpperCamelCase_=False			,    UpperCamelCase_=True			,    UpperCamelCase_=True			,    UpperCamelCase_="<unk>"			,    UpperCamelCase_="[SEP]"			,    UpperCamelCase_="<pad>"			,    UpperCamelCase_="[CLS]"			,    UpperCamelCase_="[MASK]"			,    **UpperCamelCase_			,    ):
      # Mask token behave like a normal word, i.e. include the space before it and
      # is included in the raw text, there should be a match in a non-normalized sentence.
      __magic_name__	 =   (
          AddedToken(__SCREAMING_SNAKE_CASE			,    lstrip=__SCREAMING_SNAKE_CASE			,    rstrip=__SCREAMING_SNAKE_CASE			,    normalized=__SCREAMING_SNAKE_CASE							)
          if isinstance(__SCREAMING_SNAKE_CASE			,    __SCREAMING_SNAKE_CASE							)
          else mask_token
      )

      super().__init__(
          __SCREAMING_SNAKE_CASE			,    tokenizer_file=__SCREAMING_SNAKE_CASE			,    do_lower_case=__SCREAMING_SNAKE_CASE			,    remove_space=__SCREAMING_SNAKE_CASE			,    keep_accents=__SCREAMING_SNAKE_CASE			,    unk_token=__SCREAMING_SNAKE_CASE			,    sep_token=__SCREAMING_SNAKE_CASE			,    pad_token=__SCREAMING_SNAKE_CASE			,    cls_token=__SCREAMING_SNAKE_CASE			,    mask_token=__SCREAMING_SNAKE_CASE			,    **__SCREAMING_SNAKE_CASE			,    )

      __magic_name__	 =   do_lower_case
      __magic_name__	 =   remove_space
      __magic_name__	 =   keep_accents
      __magic_name__	 =   vocab_file
      __magic_name__	 =   False if not self.vocab_file else True







     def 						lowerCAmelCase__     (   self			,    UpperCamelCase_			,    UpperCamelCase_ = None							):
      __magic_name__	 =   [self.sep_token_id]
      __magic_name__	 =   [self.cls_token_id]
      if token_ids_a is None:
       return cls + token_ids_a + sep
      return cls + token_ids_a + sep + token_ids_a + sep







     def 						lowerCAmelCase__     (   self			,    UpperCamelCase_			,    UpperCamelCase_ = None							):
      __magic_name__	 =   [self.sep_token_id]
      __magic_name__	 =   [self.cls_token_id]

      if token_ids_a is None:
       return len(cls + token_ids_a + sep							) * [0]
      return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]







     def 						lowerCAmelCase__     (   self			,    UpperCamelCase_			,    UpperCamelCase_ = None							):
      if not os.path.isdir(__SCREAMING_SNAKE_CASE							):
       logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
       return
      __magic_name__	 =   os.path.join(
          __SCREAMING_SNAKE_CASE			,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

      if os.path.abspath(self.vocab_file							) != os.path.abspath(__SCREAMING_SNAKE_CASE							):
       copyfile(self.vocab_file			,    __SCREAMING_SNAKE_CASE							)

      return (out_vocab_file,)


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


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

  def __init__(		self			:   Any  ,				__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			:   Union[int, float] = 1 / 2_55  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, List[float]]] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, List[float]]] = None  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)
   __a							=	size if size is not None else {"height": 3_84, "width": 3_84}
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE  ,				default_to_square=__SCREAMING_SNAKE_CASE							)

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

  def 		_UpperCAmelCase (		self			:   Optional[int]  ,				__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			:   Any  ,				):
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE  ,				default_to_square=__SCREAMING_SNAKE_CASE							)
   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()}"""							)
   __a							=	(size["height"], size["width"])
   return resize(__SCREAMING_SNAKE_CASE  ,				size=__SCREAMING_SNAKE_CASE  ,				resample=__SCREAMING_SNAKE_CASE  ,				data_format=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

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

  def 		_UpperCAmelCase (		self			:   Optional[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			:   List[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[float] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[bool] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, List[float]]] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, List[float]]] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, TensorType]] = None  ,				__SCREAMING_SNAKE_CASE			:   bool = None  ,				__SCREAMING_SNAKE_CASE			:   ChannelDimension = ChannelDimension.FIRST  ,				**__SCREAMING_SNAKE_CASE			:   Any  ,				):
   __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_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							=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   __a							=	size if size is not None else self.size
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE  ,				default_to_square=__SCREAMING_SNAKE_CASE							)

   __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 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:
    __a							=	[convert_to_rgb(__SCREAMING_SNAKE_CASE							) for image in images]

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

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

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

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

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

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

   return encoded_outputs


