





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							:       Optional[int]					 =	None

lowerCAmelCase							:       Union[str, Any]					 =	logging.get_logger(__name__)
lowerCAmelCase							:       str					 =	{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase							:       str					 =	{
    """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							:       Union[str, Any]					 =	{
    """google/fnet-base""": 512,
    """google/fnet-large""": 512,
}

lowerCAmelCase							:       Any					 =	"""▁"""





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Dict		      =     VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__     :   Union[str, Any]		      =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__     :   Dict		      =     ["input_ids", "token_type_ids"]
				SCREAMING_SNAKE_CASE__     :   int		      =     FNetTokenizer






				def __init__(  self			, _lowerCAmelCase=None			, _lowerCAmelCase=None			, _lowerCAmelCase=False			, _lowerCAmelCase=True			, _lowerCAmelCase=True			, _lowerCAmelCase="<unk>"			, _lowerCAmelCase="[SEP]"			, _lowerCAmelCase="<pad>"			, _lowerCAmelCase="[CLS]"			, _lowerCAmelCase="[MASK]"			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   (
										    AddedToken(_lowerCAmelCase			, lstrip=_lowerCAmelCase			, rstrip=_lowerCAmelCase			, normalized=_lowerCAmelCase					)
										    if isinstance(_lowerCAmelCase			, _lowerCAmelCase					)
										    else mask_token
										)

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

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






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = None					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   [self.sep_token_id]
										__SCREAMING_SNAKE_CASE:				Tuple           =   [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 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = None					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   [self.sep_token_id]
										__SCREAMING_SNAKE_CASE:				List[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 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = None					):

										"""simple docstring"""




										if not os.path.isdir(_lowerCAmelCase					):
																logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
																return
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   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					):
																copyfile(self.vocab_file			, _lowerCAmelCase					)

										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 center_crop, 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, is_vision_available, logging


if is_vision_available():
		import PIL


lowerCamelCase__						=				logging.get_logger(__name__)







class 							lowerCAmelCase__   ( __lowercase						):
						UpperCamelCase_				:			str						=		["pixel_values"]

						def __init__(      self							,				a = True							,				a = None							,				a = PIL.Image.BICUBIC							,				a = True							,				a = None							,				a = 1 / 2_55							,				a = True							,				a = True							,				a = None							,				a = None							,				**a							,				)       ->   None:


								'''simple docstring'''

								super().__init__(**a					)
								_UpperCamelCase         =						size if size is not None else {"""height""": 2_56, """width""": 2_56}
								_UpperCamelCase         =						get_size_dict(a					)
								_UpperCamelCase         =						crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
								_UpperCamelCase         =						get_size_dict(a							,				param_name="""crop_size"""					)

								_UpperCamelCase         =						do_resize
								_UpperCamelCase         =						size
								_UpperCamelCase         =						resample
								_UpperCamelCase         =						do_center_crop
								_UpperCamelCase         =						crop_size
								_UpperCamelCase         =						do_rescale
								_UpperCamelCase         =						rescale_factor
								_UpperCamelCase         =						do_normalize
								_UpperCamelCase         =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								_UpperCamelCase         =						image_std if image_std is not None else IMAGENET_STANDARD_STD

						def 			A_					(      self							,				a							,				a							,				a = PIL.Image.BICUBIC							,				a = None							,				**a							,				)       ->   np.ndarray:


								'''simple docstring'''

								_UpperCamelCase         =						get_size_dict(a					)
								if "height" not in size or "width" not in size:
										raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
								return resize(
								    a							,				size=(size["""height"""], size["""width"""])							,				resample=a							,				data_format=a							,				**a					)

						def 			A_					(      self							,				a							,				a							,				a = None							,				**a							,				)       ->   np.ndarray:


								'''simple docstring'''

								_UpperCamelCase         =						get_size_dict(a					)
								if "height" not in size or "width" not in size:
										raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
								return center_crop(a							,				size=(size["""height"""], size["""width"""])							,				data_format=a							,				**a					)

						def 			A_					(      self							,				a							,				a							,				a = None							,				**a							,				)       ->   List[str]:


								'''simple docstring'''

								return rescale(a							,				scale=a							,				data_format=a							,				**a					)

						def 			A_					(      self							,				a							,				a							,				a							,				a = None							,				**a							,				)       ->   np.ndarray:


								'''simple docstring'''

								return normalize(a							,				mean=a							,				std=a							,				data_format=a							,				**a					)







						def 			A_					(      self							,				a							,				a = None							,				a = None							,				a=None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = ChannelDimension.FIRST							,				**a							,				)       ->   PIL.Image.Image:


								'''simple docstring'''

								_UpperCamelCase         =						do_resize if do_resize is not None else self.do_resize
								_UpperCamelCase         =						resample if resample is not None else self.resample
								_UpperCamelCase         =						do_center_crop if do_center_crop is not None else self.do_center_crop
								_UpperCamelCase         =						do_rescale if do_rescale is not None else self.do_rescale
								_UpperCamelCase         =						rescale_factor if rescale_factor is not None else self.rescale_factor
								_UpperCamelCase         =						do_normalize if do_normalize is not None else self.do_normalize
								_UpperCamelCase         =						image_mean if image_mean is not None else self.image_mean
								_UpperCamelCase         =						image_std if image_std is not None else self.image_std

								_UpperCamelCase         =						size if size is not None else self.size
								_UpperCamelCase         =						get_size_dict(a					)
								_UpperCamelCase         =						crop_size if crop_size is not None else self.crop_size
								_UpperCamelCase         =						get_size_dict(a							,				param_name="""crop_size"""					)

								_UpperCamelCase         =						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_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.
								_UpperCamelCase         =						[to_numpy_array(a					) for image in images]

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

								if do_center_crop:
										_UpperCamelCase         =						[self.center_crop(image=a							,				size=a					) for image in images]

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

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

								_UpperCamelCase         =						[to_channel_dimension_format(a							,				a					) for image in images]

								_UpperCamelCase         =						{"""pixel_values""": images}
								return BatchFeature(data=a							,				tensor_type=a					)



