'''simple docstring'''

import os
import re
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_			:			List[str]    	=   logging.get_logger(__name__)
lowerCAmelCase_			:			List[str]    	=   {"vocab_file": "spiece.model"}

lowerCAmelCase_			:			Optional[Any]    	=   {
    "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",
    },
}

lowerCAmelCase_			:			List[Any]    	=   {
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

lowerCAmelCase_			:			str    	=   "▁"





class 				lowercase    (    __lowerCamelCase			):
   lowerCamelCase_            =VOCAB_FILES_NAMES
   lowerCamelCase_            =PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_            =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_            =['input_ids', 'token_type_ids']


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

         lowercase_     			=   {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             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 ,		sp_model_kwargs=self.sp_model_kwargs ,		**__lowerCAmelCase ,		)

         lowercase_     			=   do_lower_case
         lowercase_     			=   remove_space
         lowercase_     			=   keep_accents
         lowercase_     			=   vocab_file

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


   @property
   def __UpperCAmelCase							( self					:						Any)   ->       Optional[Any]:
         return len(self.sp_model)


   def __UpperCAmelCase							( self					:						Tuple)   ->       List[Any]:
         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					:						str)   ->       Dict:
         lowercase_     			=   self.__dict__.copy()
         lowercase_     			=   None
         return state


   def __setstate__( self					:						Union[str, Any] ,		__lowerCAmelCase					:						Dict)   ->       List[str]:
         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					:						Dict)   ->       Any:
         if self.remove_space:
               lowercase_     			=   " ".join(inputs.strip().split())
         else:
               lowercase_     			=   inputs
         lowercase_     			=   outputs.replace("``" ,		"\"").replace("''" ,		"\"")

         if not self.keep_accents:
               lowercase_     			=   unicodedata.normalize("NFKD" ,		__lowerCAmelCase)
               lowercase_     			=   "".join([c for c in outputs if not unicodedata.combining(__lowerCAmelCase)])
         if self.do_lower_case:
               lowercase_     			=   outputs.lower()

         return outputs


   def __UpperCAmelCase							( self					:						int ,		__lowerCAmelCase					:						str)   ->       List[str]:
         lowercase_     			=   self.preprocess_text(__lowerCAmelCase)
         lowercase_     			=   self.sp_model.encode(__lowerCAmelCase ,		out_type=__lowerCAmelCase)
         lowercase_     			=   []
         for piece in pieces:
               if len(__lowerCAmelCase) > 1 and piece[-1] == str(",") and piece[-2].isdigit():
                     lowercase_     			=   self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowerCAmelCase ,		""))
                     if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                           if len(cur_pieces[0]) == 1:
                                 lowercase_     			=   cur_pieces[1:]
                           else:
                                 lowercase_     			=   cur_pieces[0][1:]
                     cur_pieces.append(piece[-1])
                     new_pieces.extend(__lowerCAmelCase)
               else:
                     new_pieces.append(__lowerCAmelCase)

         return new_pieces


   def __UpperCAmelCase							( self					:						str ,		__lowerCAmelCase					:						Union[str, Any])   ->       Any:
         return self.sp_model.PieceToId(__lowerCAmelCase)


   def __UpperCAmelCase							( self					:						Optional[Any] ,		__lowerCAmelCase					:						Dict)   ->       Optional[Any]:
         return self.sp_model.IdToPiece(__lowerCAmelCase)


   def __UpperCAmelCase							( self					:						List[Any] ,		__lowerCAmelCase					:						int)   ->       Tuple:
         lowercase_     			=   []
         lowercase_     			=   ""
         lowercase_     			=   False
         for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                     if not prev_is_special:
                           out_string += " "
                     out_string += self.sp_model.decode(__lowerCAmelCase) + token
                     lowercase_     			=   True
                     lowercase_     			=   []
               else:
                     current_sub_tokens.append(__lowerCAmelCase)
                     lowercase_     			=   False
         out_string += self.sp_model.decode(__lowerCAmelCase)
         return out_string.strip()


   def __UpperCAmelCase							( self					:						Any ,		__lowerCAmelCase					:						List[int] ,		__lowerCAmelCase					:						bool = False ,		__lowerCAmelCase					:						bool = None ,		__lowerCAmelCase					:						bool = True ,		**__lowerCAmelCase					:						Optional[int] ,		)   ->       str:
         lowercase_     			=   kwargs.pop("use_source_tokenizer" ,		__lowerCAmelCase)

         lowercase_     			=   self.convert_ids_to_tokens(__lowerCAmelCase ,		skip_special_tokens=__lowerCAmelCase)

         # To avoid mixing byte-level and unicode for byte-level BPT
         # we need to build string separately for added tokens and byte-level tokens
         # cf. https://github.com/huggingface/transformers/issues/1133
         lowercase_     			=   []
         lowercase_     			=   []
         for token in filtered_tokens:
               if skip_special_tokens and token in self.all_special_ids:
                     continue
               if token in self.added_tokens_encoder:
                     if current_sub_text:
                           sub_texts.append(self.convert_tokens_to_string(__lowerCAmelCase))
                           lowercase_     			=   []
                     sub_texts.append(__lowerCAmelCase)
               else:
                     current_sub_text.append(__lowerCAmelCase)
         if current_sub_text:
               sub_texts.append(self.convert_tokens_to_string(__lowerCAmelCase))

         # Mimic the behavior of the Rust tokenizer:
         # No space after <unk>
         if spaces_between_special_tokens:
               lowercase_     			=   re.sub(R"(<unk>) " ,		R"\1" ,		" ".join(__lowerCAmelCase))
         else:
               lowercase_     			=   "".join(__lowerCAmelCase)

         lowercase_     			=   (
             clean_up_tokenization_spaces
             if clean_up_tokenization_spaces is not None
             else self.clean_up_tokenization_spaces
         )
         if clean_up_tokenization_spaces:
               lowercase_     			=   self.clean_up_tokenization(__lowerCAmelCase)
               return clean_text
         else:
               return text


   def __UpperCAmelCase							( self					:						int ,		__lowerCAmelCase					:						List[int] ,		__lowerCAmelCase					:						Optional[List[int]] = None)   ->       List[int]:
         lowercase_     			=   [self.sep_token_id]
         lowercase_     			=   [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 __UpperCAmelCase							( self					:						str ,		__lowerCAmelCase					:						List[int] ,		__lowerCAmelCase					:						Optional[List[int]] = None ,		__lowerCAmelCase					:						bool = False)   ->       List[int]:

         if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__lowerCAmelCase ,		token_ids_a=__lowerCAmelCase ,		already_has_special_tokens=__lowerCAmelCase)

         if token_ids_a is not None:
               return [1] + ([0] * len(__lowerCAmelCase)) + [1] + ([0] * len(__lowerCAmelCase)) + [1]
         return [1] + ([0] * len(__lowerCAmelCase)) + [1]


   def __UpperCAmelCase							( self					:						Optional[Any] ,		__lowerCAmelCase					:						List[int] ,		__lowerCAmelCase					:						Optional[List[int]] = None)   ->       List[int]:
         lowercase_     			=   [self.sep_token_id]
         lowercase_     			=   [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 __UpperCAmelCase							( self					:						List[Any] ,		__lowerCAmelCase					:						str ,		__lowerCAmelCase					:						Optional[str] = None)   ->       Tuple[str]:
         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,)







'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import ConvNextImageProcessor





class 				lowercase    (    unittest.TestCase			):


   def __init__( self					:						str ,		__lowerCAmelCase					:						Union[str, Any] ,		__lowerCAmelCase					:						Optional[int]=7 ,		__lowerCAmelCase					:						Optional[Any]=3 ,		__lowerCAmelCase					:						Optional[Any]=18 ,		__lowerCAmelCase					:						List[str]=30 ,		__lowerCAmelCase					:						List[str]=400 ,		__lowerCAmelCase					:						Tuple=True ,		__lowerCAmelCase					:						Optional[int]=None ,		__lowerCAmelCase					:						List[str]=0.875 ,		__lowerCAmelCase					:						int=True ,		__lowerCAmelCase					:						Optional[int]=[0.5, 0.5, 0.5] ,		__lowerCAmelCase					:						List[str]=[0.5, 0.5, 0.5] ,		)   ->       Optional[Any]:
         lowercase_     			=   size if size is not None else {"shortest_edge": 20}
         lowercase_     			=   parent
         lowercase_     			=   batch_size
         lowercase_     			=   num_channels
         lowercase_     			=   image_size
         lowercase_     			=   min_resolution
         lowercase_     			=   max_resolution
         lowercase_     			=   do_resize
         lowercase_     			=   size
         lowercase_     			=   crop_pct
         lowercase_     			=   do_normalize
         lowercase_     			=   image_mean
         lowercase_     			=   image_std


   def __UpperCAmelCase							( self					:						List[str])   ->       int:
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_pct": self.crop_pct,
         }





@require_torch
@require_vision
class 				lowercase    (    __lowerCamelCase			,					unittest.TestCase			):
   lowerCamelCase_            =ConvNextImageProcessor if is_vision_available() else None


   def __UpperCAmelCase							( self					:						Optional[Any])   ->       Tuple:
         lowercase_     			=   ConvNextImageProcessingTester(self)


   @property
   def __UpperCAmelCase							( self					:						Optional[int])   ->       int:
         return self.image_processor_tester.prepare_image_processor_dict()


   def __UpperCAmelCase							( self					:						Optional[int])   ->       Any:
         lowercase_     			=   self.image_processing_class(**self.image_processor_dict)
         self.assertTrue(hasattr(__lowerCAmelCase ,		"do_resize"))
         self.assertTrue(hasattr(__lowerCAmelCase ,		"size"))
         self.assertTrue(hasattr(__lowerCAmelCase ,		"crop_pct"))
         self.assertTrue(hasattr(__lowerCAmelCase ,		"do_normalize"))
         self.assertTrue(hasattr(__lowerCAmelCase ,		"image_mean"))
         self.assertTrue(hasattr(__lowerCAmelCase ,		"image_std"))


   def __UpperCAmelCase							( self					:						List[str])   ->       Any:
         lowercase_     			=   self.image_processing_class.from_dict(self.image_processor_dict)
         self.assertEqual(image_processor.size ,		{"shortest_edge": 20})

         lowercase_     			=   self.image_processing_class.from_dict(self.image_processor_dict ,		size=42)
         self.assertEqual(image_processor.size ,		{"shortest_edge": 42})


   def __UpperCAmelCase							( self					:						Any)   ->       Optional[int]:
         pass


   def __UpperCAmelCase							( self					:						List[Any])   ->       Any:
         # Initialize image_processing
         lowercase_     			=   self.image_processing_class(**self.image_processor_dict)
         # create random PIL images
         lowercase_     			=   prepare_image_inputs(self.image_processor_tester ,		equal_resolution=__lowerCAmelCase)
         for image in image_inputs:
               self.assertIsInstance(__lowerCAmelCase ,		Image.Image)

         # Test not batched input
         lowercase_     			=   image_processing(image_inputs[0] ,		return_tensors="pt").pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             ) ,		)

         # Test batched
         lowercase_     			=   image_processing(__lowerCAmelCase ,		return_tensors="pt").pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             ) ,		)


   def __UpperCAmelCase							( self					:						Union[str, Any])   ->       str:
         # Initialize image_processing
         lowercase_     			=   self.image_processing_class(**self.image_processor_dict)
         # create random numpy tensors
         lowercase_     			=   prepare_image_inputs(self.image_processor_tester ,		equal_resolution=__lowerCAmelCase ,		numpify=__lowerCAmelCase)
         for image in image_inputs:
               self.assertIsInstance(__lowerCAmelCase ,		np.ndarray)

         # Test not batched input
         lowercase_     			=   image_processing(image_inputs[0] ,		return_tensors="pt").pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             ) ,		)

         # Test batched
         lowercase_     			=   image_processing(__lowerCAmelCase ,		return_tensors="pt").pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             ) ,		)


   def __UpperCAmelCase							( self					:						List[str])   ->       List[str]:
         # Initialize image_processing
         lowercase_     			=   self.image_processing_class(**self.image_processor_dict)
         # create random PyTorch tensors
         lowercase_     			=   prepare_image_inputs(self.image_processor_tester ,		equal_resolution=__lowerCAmelCase ,		torchify=__lowerCAmelCase)
         for image in image_inputs:
               self.assertIsInstance(__lowerCAmelCase ,		torch.Tensor)

         # Test not batched input
         lowercase_     			=   image_processing(image_inputs[0] ,		return_tensors="pt").pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             ) ,		)

         # Test batched
         lowercase_     			=   image_processing(__lowerCAmelCase ,		return_tensors="pt").pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             ) ,		)






