

from __future__ import annotations

from collections import deque





class 				__lowerCAmelCase  :




  def __init__(       self							,     lowerCAmelCase      )						->				Optional[int]:
        '''simple docstring'''






        _lowercase			 =[]
        self.adlist.append(
            {'value': '', 'next_states': [], 'fail_state': 0, 'output': []}      )

        for keyword in keywords:
              self.add_keyword(lowerCAmelCase      )
        self.set_fail_transitions()




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase      )						->				int | None:
        '''simple docstring'''






        for state in self.adlist[current_state]["next_states"]:
              if char == self.adlist[state]["value"]:
                    return state
        return None




  def 						A__  (       self							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        _lowercase			 =0
        for character in keyword:
              _lowercase			 =self.find_next_state(lowerCAmelCase							,     lowerCAmelCase      )
              if next_state is None:
                    self.adlist.append(
                        {
                            'value': character,
                            'next_states': [],
                            'fail_state': 0,
                            'output': [],
                        }      )
                    self.adlist[current_state]["next_states"].append(len(self.adlist      ) - 1      )
                    _lowercase			 =len(self.adlist      ) - 1
              else:
                    _lowercase			 =next_state
        self.adlist[current_state]["output"].append(lowerCAmelCase      )




  def 						A__  (       self      )						->				None:
        '''simple docstring'''






        _lowercase			 =deque()
        for node in self.adlist[0]["next_states"]:
              q.append(lowerCAmelCase      )
              _lowercase			 =0
        while q:
              _lowercase			 =q.popleft()
              for child in self.adlist[r]["next_states"]:
                    q.append(lowerCAmelCase      )
                    _lowercase			 =self.adlist[r]['fail_state']
                    while (
                        self.find_next_state(lowerCAmelCase							,     self.adlist[child]['value']      ) is None
                        and state != 0
                    ):
                          _lowercase			 =self.adlist[state]['fail_state']
                    _lowercase			 =self.find_next_state(
                        lowerCAmelCase							,     self.adlist[child]['value']      )
                    if self.adlist[child]["fail_state"] is None:
                          _lowercase			 =0
                    _lowercase			 =(
                        self.adlist[child]['output']
                        + self.adlist[self.adlist[child]['fail_state']]['output']
                    )




  def 						A__  (       self							,     lowerCAmelCase      )						->				dict[str, list[int]]:
        '''simple docstring'''






        _lowercase			 ={}  # returns a dict with keywords and list of its occurrences
        _lowercase			 =0
        for i in range(len(lowerCAmelCase      )      ):
              while (
                  self.find_next_state(lowerCAmelCase							,     string[i]      ) is None
                  and current_state != 0
              ):
                    _lowercase			 =self.adlist[current_state]['fail_state']
              _lowercase			 =self.find_next_state(lowerCAmelCase							,     string[i]      )
              if next_state is None:
                    _lowercase			 =0
              else:
                    _lowercase			 =next_state
                    for key in self.adlist[current_state]["output"]:
                          if key not in result:
                                _lowercase			 =[]
                          result[key].append(i - len(lowerCAmelCase      ) + 1      )
        return result


if __name__ == "__main__":
  import doctest

  doctest.testmod()





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							):




  @slow
  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        _lowercase			 =TFCamembertModel.from_pretrained('jplu/tf-camembert-base'      )

        _lowercase			 =tf.convert_to_tensor(
            [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]							,     dtype=tf.intaa							,     )  # J'aime le camembert !"

        _lowercase			 =model(lowerCAmelCase      )['last_hidden_state']
        _lowercase			 =tf.TensorShape((1, 10, 768)      )
        self.assertEqual(output.shape							,     lowerCAmelCase      )
        # compare the actual values for a slice.
        _lowercase			 =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      )      )


