

"""simple docstring"""





import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __init__(      self							:      List[str], UpperCAmelCase__							:      str = "▁", UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Union[str, AddedToken] = "<unk>", UpperCAmelCase__							:      Union[str, AddedToken] = "</s>", UpperCAmelCase__							:      Union[str, AddedToken] = "<pad>", ):
    __lowercase								=						{
        "pad": {"id": 0, "token": pad_token},
        "eos": {"id": 1, "token": eos_token},
        "unk": {"id": 2, "token": unk_token},
    }

    __lowercase								=						[None] * len(self.special_tokens     )
    for token_dict in self.special_tokens.values():
       __lowercase								=						token_dict["token"]

    __lowercase								=						Tokenizer(Unigram()     )

    __lowercase								=						normalizers.Sequence(
        [
            normalizers.Nmt(),
            normalizers.NFKC(),
            normalizers.Replace(Regex(" {2,}"     ), " "     ),
            normalizers.Lowercase(),
        ]     )
    __lowercase								=						pre_tokenizers.Sequence(
        [
            pre_tokenizers.Metaspace(replacement=UpperCAmelCase__, add_prefix_space=UpperCAmelCase__     ),
            pre_tokenizers.Digits(individual_digits=UpperCAmelCase__     ),
            pre_tokenizers.Punctuation(),
        ]     )
    __lowercase								=						decoders.Metaspace(replacement=UpperCAmelCase__, add_prefix_space=UpperCAmelCase__     )

    __lowercase								=						TemplateProcessing(
        single=F"""$A {self.special_tokens["eos"]["token"]}""", special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])], )

    __lowercase								=						{
        "model": "SentencePieceUnigram",
        "replacement": replacement,
        "add_prefix_space": add_prefix_space,
    }

    super().__init__(UpperCAmelCase__, UpperCAmelCase__     )


 def  _lowercase			(      self							:      str, UpperCAmelCase__							:      Union[str, List[str]], UpperCAmelCase__							:      int = 8_0_0_0, UpperCAmelCase__							:      bool = True, ):
    __lowercase								=						trainers.UnigramTrainer(
        vocab_size=UpperCAmelCase__, special_tokens=self.special_tokens_list, show_progress=UpperCAmelCase__, )

    if isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
       __lowercase								=						[files]
    self._tokenizer.train(UpperCAmelCase__, trainer=UpperCAmelCase__     )

    self.add_unk_id()


 def  _lowercase			(      self							:      str, UpperCAmelCase__							:      Union[Iterator[str], Iterator[Iterator[str]]], UpperCAmelCase__							:      int = 8_0_0_0, UpperCAmelCase__							:      bool = True, ):
    __lowercase								=						trainers.UnigramTrainer(
        vocab_size=UpperCAmelCase__, special_tokens=self.special_tokens_list, show_progress=UpperCAmelCase__, )

    self._tokenizer.train_from_iterator(UpperCAmelCase__, trainer=UpperCAmelCase__     )

    self.add_unk_id()



 def  _lowercase			(      self							:      str     ):
    __lowercase								=						json.loads(self._tokenizer.to_str()     )

    __lowercase								=						self.special_tokens["unk"]["id"]

    __lowercase								=						Tokenizer.from_str(json.dumps(UpperCAmelCase__     )     )







"""simple docstring"""





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  _lowerCAmelCase  (     unittest.TestCase       ):







 """simple docstring"""







 @slow
 def  _lowercase			(      self							:      Optional[Any]     ):
    __lowercase								=						TFCamembertModel.from_pretrained("jplu/tf-camembert-base"     )

    __lowercase								=						tf.convert_to_tensor(
        [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]], dtype=tf.intaa, )  # J'aime le camembert !"

    __lowercase								=						model(UpperCAmelCase__     )["last_hidden_state"]
    __lowercase								=						tf.TensorShape((1, 1_0, 7_6_8)     )
    self.assertEqual(output.shape, UpperCAmelCase__     )
    # compare the actual values for a slice.
    __lowercase								=						tf.convert_to_tensor(
        [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]], 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     )     )




