


"""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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


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



class 						snake_case_(				a__	):
     __UpperCamelCase     =    ['''pixel_values''']






     def __init__(		self     :    Tuple    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    Optional[Dict[str, int]] = None    , UpperCamelCase_     :    PILImageResampling = PILImageResampling.BILINEAR    , 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_     :    int    , ):
           super().__init__(**UpperCamelCase_ )
           lowerCAmelCase    :		Any				=      size if size is not None else {'''shortest_edge''': 2_5_6}
           lowerCAmelCase    :		List[str]				=      get_size_dict(UpperCamelCase_    , default_to_square=UpperCamelCase_ )
           lowerCAmelCase    :		Dict				=      crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
           lowerCAmelCase    :		List[Any]				=      get_size_dict(UpperCamelCase_ )
           lowerCAmelCase    :		Optional[Any]				=      do_resize
           lowerCAmelCase    :		List[Any]				=      size
           lowerCAmelCase    :		Optional[Any]				=      resample
           lowerCAmelCase    :		List[str]				=      do_center_crop
           lowerCAmelCase    :		Dict				=      crop_size
           lowerCAmelCase    :		Optional[Any]				=      do_rescale
           lowerCAmelCase    :		List[str]				=      rescale_factor
           lowerCAmelCase    :		List[Any]				=      do_normalize
           lowerCAmelCase    :		Union[str, Any]				=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           lowerCAmelCase    :		List[str]				=      image_std if image_std is not None else IMAGENET_STANDARD_STD






     def    lowerCamelCase__   (		self     :    Dict    , UpperCamelCase_     :    np.ndarray    , UpperCamelCase_     :    Dict[str, int]    , UpperCamelCase_     :    PILImageResampling = PILImageResampling.BICUBIC    , UpperCamelCase_     :    Optional[Union[str, ChannelDimension]] = None    , **UpperCamelCase_     :    int    , ):
           lowerCAmelCase    :		List[str]				=      get_size_dict(UpperCamelCase_    , default_to_square=UpperCamelCase_ )
           if "shortest_edge" not in size:
                 raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
           lowerCAmelCase    :		List[str]				=      get_resize_output_image_size(UpperCamelCase_    , size=size['''shortest_edge''']    , default_to_square=UpperCamelCase_ )
           return resize(UpperCamelCase_    , size=UpperCamelCase_    , resample=UpperCamelCase_    , data_format=UpperCamelCase_    , **UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Tuple    , UpperCamelCase_     :    np.ndarray    , UpperCamelCase_     :    Dict[str, int]    , UpperCamelCase_     :    Optional[Union[str, ChannelDimension]] = None    , **UpperCamelCase_     :    int    , ):
           lowerCAmelCase    :		str				=      get_size_dict(UpperCamelCase_ )
           return center_crop(UpperCamelCase_    , size=(size['''height'''], size['''width'''])    , data_format=UpperCamelCase_    , **UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    str    , UpperCamelCase_     :    np.ndarray    , UpperCamelCase_     :    float    , UpperCamelCase_     :    Optional[Union[str, ChannelDimension]] = None    , **UpperCamelCase_     :    List[str] ):
           return rescale(UpperCamelCase_    , scale=UpperCamelCase_    , data_format=UpperCamelCase_    , **UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Optional[Any]    , UpperCamelCase_     :    np.ndarray    , UpperCamelCase_     :    Union[float, List[float]]    , UpperCamelCase_     :    Union[float, List[float]]    , UpperCamelCase_     :    Optional[Union[str, ChannelDimension]] = None    , **UpperCamelCase_     :    int    , ):
           return normalize(UpperCamelCase_    , mean=UpperCamelCase_    , std=UpperCamelCase_    , data_format=UpperCamelCase_    , **UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Union[str, Any]    , UpperCamelCase_     :    ImageInput    , UpperCamelCase_     :    Optional[bool] = None    , UpperCamelCase_     :    Dict[str, int] = None    , UpperCamelCase_     :    PILImageResampling = None    , UpperCamelCase_     :    bool = None    , UpperCamelCase_     :    Dict[str, int] = None    , UpperCamelCase_     :    Optional[bool] = None    , UpperCamelCase_     :    Optional[float] = None    , UpperCamelCase_     :    Optional[bool] = None    , UpperCamelCase_     :    Optional[Union[float, List[float]]] = None    , UpperCamelCase_     :    Optional[Union[float, List[float]]] = None    , UpperCamelCase_     :    Optional[Union[str, TensorType]] = None    , UpperCamelCase_     :    Union[str, ChannelDimension] = ChannelDimension.FIRST    , **UpperCamelCase_     :    int    , ):
           lowerCAmelCase    :		List[Any]				=      do_resize if do_resize is not None else self.do_resize
           lowerCAmelCase    :		Dict				=      size if size is not None else self.size
           lowerCAmelCase    :		List[str]				=      get_size_dict(UpperCamelCase_    , default_to_square=UpperCamelCase_ )
           lowerCAmelCase    :		Dict				=      resample if resample is not None else self.resample
           lowerCAmelCase    :		int				=      do_center_crop if do_center_crop is not None else self.do_center_crop
           lowerCAmelCase    :		List[str]				=      crop_size if crop_size is not None else self.crop_size
           lowerCAmelCase    :		List[str]				=      get_size_dict(UpperCamelCase_ )
           lowerCAmelCase    :		List[str]				=      do_rescale if do_rescale is not None else self.do_rescale
           lowerCAmelCase    :		Optional[Any]				=      rescale_factor if rescale_factor is not None else self.rescale_factor
           lowerCAmelCase    :		List[str]				=      do_normalize if do_normalize is not None else self.do_normalize
           lowerCAmelCase    :		Union[str, Any]				=      image_mean if image_mean is not None else self.image_mean
           lowerCAmelCase    :		str				=      image_std if image_std is not None else self.image_std

           lowerCAmelCase    :		Union[str, Any]				=      make_list_of_images(UpperCamelCase_ )

           if not valid_images(UpperCamelCase_ ):
                 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.''' )

           # All transformations expect numpy arrays.
           lowerCAmelCase    :		str				=      [to_numpy_array(UpperCamelCase_ ) for image in images]

           if do_resize:
                 lowerCAmelCase    :		Any				=      [self.resize(image=UpperCamelCase_    , size=UpperCamelCase_    , resample=UpperCamelCase_ ) for image in images]

           if do_center_crop:
                 lowerCAmelCase    :		int				=      [self.center_crop(image=UpperCamelCase_    , size=UpperCamelCase_ ) for image in images]

           if do_rescale:
                 lowerCAmelCase    :		Optional[Any]				=      [self.rescale(image=UpperCamelCase_    , scale=UpperCamelCase_ ) for image in images]

           if do_normalize:
                 lowerCAmelCase    :		Tuple				=      [self.normalize(image=UpperCamelCase_    , mean=UpperCamelCase_    , std=UpperCamelCase_ ) for image in images]

           lowerCAmelCase    :		Union[str, Any]				=      [to_channel_dimension_format(UpperCamelCase_    , UpperCamelCase_ ) for image in images]

           lowerCAmelCase    :		Union[str, Any]				=      {'''pixel_values''': images}
           return BatchFeature(data=UpperCamelCase_    , tensor_type=UpperCamelCase_ )






"""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:
 snake_case__						:   str			 = None

snake_case__						:   Optional[Any]			 = logging.get_logger(__name__)
snake_case__						:   Optional[int]			 = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case__						:   Dict			 = {
    '''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''',
    },
}

snake_case__						:   Any			 = {
    '''google/fnet-base''': 512,
    '''google/fnet-large''': 512,
}

snake_case__						:   Dict			 = '''▁'''



class 						snake_case_(				a__	):
     __UpperCamelCase     =    VOCAB_FILES_NAMES
     __UpperCamelCase     =    PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase     =    ['''input_ids''', '''token_type_ids''']
     __UpperCamelCase     =    FNetTokenizer






     def __init__(		self     :    Union[str, Any]    , UpperCamelCase_     :    Union[str, Any]=None    , UpperCamelCase_     :    Union[str, Any]=None    , UpperCamelCase_     :    Any=False    , UpperCamelCase_     :    Any=True    , UpperCamelCase_     :    Dict=True    , UpperCamelCase_     :    Tuple="<unk>"    , UpperCamelCase_     :    List[str]="[SEP]"    , UpperCamelCase_     :    List[Any]="<pad>"    , UpperCamelCase_     :    Union[str, Any]="[CLS]"    , UpperCamelCase_     :    int="[MASK]"    , **UpperCamelCase_     :    Optional[Any]    , ):
           # 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.
           lowerCAmelCase    :		int				=      (
               AddedToken(UpperCamelCase_    , lstrip=UpperCamelCase_    , rstrip=UpperCamelCase_    , normalized=UpperCamelCase_ )
               if isinstance(UpperCamelCase_    , UpperCamelCase_ )
               else mask_token
           )

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

           lowerCAmelCase    :		Optional[int]				=      do_lower_case
           lowerCAmelCase    :		str				=      remove_space
           lowerCAmelCase    :		Any				=      keep_accents
           lowerCAmelCase    :		int				=      vocab_file
           lowerCAmelCase    :		List[str]				=      False if not self.vocab_file else True






     def    lowerCamelCase__   (		self     :    List[Any]    , UpperCamelCase_     :    List[int]    , UpperCamelCase_     :    Optional[List[int]] = None ):
           lowerCAmelCase    :		Optional[int]				=      [self.sep_token_id]
           lowerCAmelCase    :		Optional[Any]				=      [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     :    List[str]    , UpperCamelCase_     :    List[int]    , UpperCamelCase_     :    Optional[List[int]] = None ):
           lowerCAmelCase    :		List[str]				=      [self.sep_token_id]
           lowerCAmelCase    :		Optional[Any]				=      [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     :    List[Any]    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Optional[str] = None ):
           if not os.path.isdir(UpperCamelCase_ ):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
                 return
           lowerCAmelCase    :		str				=      os.path.join(
               UpperCamelCase_    , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

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

           return (out_vocab_file,)


