





from __future__ import annotations

from collections import deque





class 					A__       :




   """simple docstring"""



   def __init__(   self   ,    lowercase)     ->		int:







          '''simple docstring'''

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

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

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase)     ->		Any:







          '''simple docstring'''

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

   def 				__lowercase						(   self   ,    lowercase)     ->		Dict:







          '''simple docstring'''

          a__		:      int					=							0
          for character in keyword:
                 a__		:      str					=							self.find_next_state(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_)
                 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)
                        a__		:      List[Any]					=							len(self.adlist) - 1
                 else:
                        a__		:      Union[str, Any]					=							next_state
          self.adlist[current_state]["output"].append(SCREAMING_SNAKE_CASE_)

   def 				__lowercase						(   self)     ->		Any:







          '''simple docstring'''

          a__		:      Tuple					=							deque()
          for node in self.adlist[0]["next_states"]:
                 q.append(SCREAMING_SNAKE_CASE_)
                 a__		:      List[str]					=							0
          while q:
                 a__		:      Optional[Any]					=							q.popleft()
                 for child in self.adlist[r]["next_states"]:
                        q.append(SCREAMING_SNAKE_CASE_)
                        a__		:      int					=							self.adlist[r]['fail_state']
                        while (
                            self.find_next_state(SCREAMING_SNAKE_CASE_   ,    self.adlist[child]['value']) is None
                            and state != 0
                        ):
                               a__		:      int					=							self.adlist[state]['fail_state']
                        a__		:      int					=							self.find_next_state(
                            SCREAMING_SNAKE_CASE_   ,    self.adlist[child]['value'])
                        if self.adlist[child]["fail_state"] is None:
                               a__		:      Dict					=							0
                        a__		:      Optional[int]					=							(
                            self.adlist[child]['output']
                            + self.adlist[self.adlist[child]['fail_state']]['output']
                        )

   def 				__lowercase						(   self   ,    lowercase)     ->		Optional[Any]:







          '''simple docstring'''

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


if __name__ == "__main__":
    import doctest

    doctest.testmod()




"""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 			snake_case  (				unittest.TestCase ):

							@slow
							def _lowercase       (self     ):

									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_    			=			tf.convert_to_tensor(
									    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]							,							dtype=tf.intaa							,							)  # J'aime le camembert !"

									SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_     )['''last_hidden_state''']
									SCREAMING_SNAKE_CASE_    			=			tf.TensorShape((1, 10, 7_68)     )
									self.assertEqual(output.shape							,							SCREAMING_SNAKE_CASE_     )
									# compare the actual values for a slice.
									SCREAMING_SNAKE_CASE_    			=			tf.convert_to_tensor(
									    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]							,							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     )     )