'''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:
						__lowercase          =  None

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

__lowercase          =  {
    '''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''',
    },
}

__lowercase          =  {
    '''google/fnet-base''': 5_1_2,
    '''google/fnet-large''': 5_1_2,
}

__lowercase          =  '''▁'''






class 				a__(					UpperCamelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     List[str]      =       VOCAB_FILES_NAMES
			UpperCAmelCase_			:     List[str]      =       PRETRAINED_VOCAB_FILES_MAP
			UpperCAmelCase_			:     Dict      =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCAmelCase_			:     List[Any]      =       ['''input_ids''', '''token_type_ids''']
			UpperCAmelCase_			:     Tuple      =       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"""


					lowerCAmelCase   					=  (
					    AddedToken(_a       ,    lstrip=_a       ,    rstrip=_a       ,    normalized=_a)
					    if isinstance(_a       ,    _a)
					    else mask_token
					)

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

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



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None):




					"""simple docstring"""


					lowerCAmelCase   					=  [self.sep_token_id]
					lowerCAmelCase   					=  [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 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None):




					"""simple docstring"""


					lowerCAmelCase   					=  [self.sep_token_id]
					lowerCAmelCase   					=  [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 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None):




					"""simple docstring"""


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

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

					return (out_vocab_file,)










from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import numpy as np
  import tensorflow as tf

  from transformers import TFCamembertModel







@require_tf
@require_sentencepiece
@require_tokenizers
class 						lowercase							(   unittest.TestCase		):

     @slow
     def 					a__	(  self				)						->							Any:
           _A							:     Tuple			=						TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""				)

           _A							:     List[Any]			=						tf.convert_to_tensor(
               [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]		,     dtype=tf.intaa		,     )  # J'aime le camembert !"

           _A							:     List[str]			=						model(_a				)["""last_hidden_state"""]
           _A							:     Union[str, Any]			=						tf.TensorShape((1, 10, 768)				)
           self.assertEqual(output.shape		,     _a				)
           # compare the actual values for a slice.
           _A							:     List[Any]			=						tf.convert_to_tensor(
               [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]		,     dtype=tf.floataa		,     )
           # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
           # camembert.eval()
           # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

           self.assertTrue(np.allclose(output[:, :3, :3].numpy()		,     expected_slice.numpy()		,     atol=1e-4				)				)

