






import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase__							  =						get_tests_dir("fixtures/test_sentencepiece.model")
UpperCAmelCase__							  =						get_tests_dir("fixtures/test_sentencepiece_bpe.model")

UpperCAmelCase__							  =						"pt" if is_torch_available() else "tf"






@require_sentencepiece
@require_tokenizers
class 						lowercase_   (      lowercase					,    unittest.TestCase  ):






      '''simple docstring'''




      __snake_case	    =    CamembertTokenizer
      __snake_case	    =    CamembertTokenizerFast
      __snake_case	    =    True
      __snake_case	    =    True







      def  __lowerCAmelCase   ( self :		str    )       ->Union[str, Any]:




       """simple docstring"""


       super().setUp()

       # We have a SentencePiece fixture for testing
       a						    = CamembertTokenizer(__UpperCAmelCase    )
       tokenizer.save_pretrained(self.tmpdirname    )







      def  __lowerCAmelCase   ( self :		List[Any]    )       ->List[Any]:




       """simple docstring"""


       a						    = '''<pad>'''
       a						    = 1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase    )					,   __UpperCAmelCase    )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase    )					,   __UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		int    )       ->Dict:




       """simple docstring"""


       a						    = list(self.get_tokenizer().get_vocab().keys()    )

       self.assertEqual(vocab_keys[0]					,   '''<s>NOTUSED'''    )
       self.assertEqual(vocab_keys[1]					,   '''<pad>'''    )
       self.assertEqual(vocab_keys[-1]					,   '''<mask>'''    )
       self.assertEqual(len(__UpperCAmelCase    )					,   1_004    )







      def  __lowerCAmelCase   ( self :		Optional[int]    )       ->int:




       """simple docstring"""


       self.assertEqual(self.get_tokenizer().vocab_size					,   1_005    )







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




       """simple docstring"""


       a						    = CamembertTokenizer(__UpperCAmelCase    )
       tokenizer.save_pretrained(self.tmpdirname    )
       a						    = CamembertTokenizerFast.from_pretrained(self.tmpdirname    )

       a						    = '''I was born in 92000, and this is falsé.'''

       a						    = tokenizer.encode(__UpperCAmelCase    )
       a						    = rust_tokenizer.encode(__UpperCAmelCase    )
       self.assertListEqual(__UpperCAmelCase					,   __UpperCAmelCase    )

       a						    = tokenizer.encode(__UpperCAmelCase					,   add_special_tokens=__UpperCAmelCase    )
       a						    = rust_tokenizer.encode(__UpperCAmelCase					,   add_special_tokens=__UpperCAmelCase    )
       self.assertListEqual(__UpperCAmelCase					,   __UpperCAmelCase    )

       # <unk> tokens are not the same for `rust` than for `slow`.
       # Because spm gives back raw token instead of `unk` in EncodeAsPieces
       # tokens = tokenizer.tokenize(sequence)
       a						    = tokenizer.convert_ids_to_tokens(__UpperCAmelCase    )
       a						    = rust_tokenizer.tokenize(__UpperCAmelCase    )
       self.assertListEqual(__UpperCAmelCase					,   __UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		str    )       ->Optional[int]:




       """simple docstring"""


       if not self.test_rust_tokenizer:
        return

       a						    = self.get_tokenizer()
       a						    = self.get_rust_tokenizer()

       a						    = '''I was born in 92000, and this is falsé.'''

       a						    = tokenizer.tokenize(__UpperCAmelCase    )
       a						    = rust_tokenizer.tokenize(__UpperCAmelCase    )
       self.assertListEqual(__UpperCAmelCase					,   __UpperCAmelCase    )

       a						    = tokenizer.encode(__UpperCAmelCase					,   add_special_tokens=__UpperCAmelCase    )
       a						    = rust_tokenizer.encode(__UpperCAmelCase					,   add_special_tokens=__UpperCAmelCase    )
       self.assertListEqual(__UpperCAmelCase					,   __UpperCAmelCase    )

       a						    = self.get_rust_tokenizer()
       a						    = tokenizer.encode(__UpperCAmelCase    )
       a						    = rust_tokenizer.encode(__UpperCAmelCase    )
       self.assertListEqual(__UpperCAmelCase					,   __UpperCAmelCase    )







      @slow
      def  __lowerCAmelCase   ( self :		List[str]    )       ->int:




       """simple docstring"""


       a						    = {'''input_ids''': [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 8, 930, 9, 6]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
       # fmt: on

       # camembert is a french model. So we also use french texts.
       a						    = [
           '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
           '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
           '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
           '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
           '''telles que la traduction et la synthèse de texte.''',
       ]

       self.tokenizer_integration_test_util(
           expected_encoding=__UpperCAmelCase					,   model_name='''camembert-base'''					,   revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''					,   sequences=__UpperCAmelCase					,   )










# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")






class 						lowercase_   :






      '''simple docstring'''




      def __init__( self :		Optional[int]					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		bool = True					,   __UpperCAmelCase :		bool = False    )       ->Tuple:




       """simple docstring"""


       a						    = scheduler
       a						    = optimizers if isinstance(__UpperCAmelCase					,   (list, tuple)    ) else [optimizers]
       a						    = split_batches
       a						    = step_with_optimizer
       a						    = GradientState()







      def  __lowerCAmelCase   ( self :		Tuple					,   *__UpperCAmelCase :		List[Any]					,   **__UpperCAmelCase :		Optional[Any]    )       ->int:




       """simple docstring"""


       if not self.step_with_optimizer:
        # No link between scheduler and optimizer -> just step
        self.scheduler.step(*__UpperCAmelCase					,   **__UpperCAmelCase    )
        return

       # Otherwise, first make sure the optimizer was stepped.
       if not self.gradient_state.sync_gradients:
        if self.gradient_state.adjust_scheduler:
         self.scheduler._step_count += 1
        return

       for opt in self.optimizers:
        if opt.step_was_skipped:
         return
       if self.split_batches:
        # Split batches -> the training dataloader batch size is not changed so one step per training step
        self.scheduler.step(*__UpperCAmelCase					,   **__UpperCAmelCase    )
       else:
        # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
        # num_processes steps per training step
        a						    = AcceleratorState().num_processes
        for _ in range(__UpperCAmelCase    ):
         # Special case when using OneCycle and `drop_last` was not used
         if hasattr(self.scheduler					,   '''total_steps'''    ):
          if self.scheduler._step_count <= self.scheduler.total_steps:
           self.scheduler.step(*__UpperCAmelCase					,   **__UpperCAmelCase    )
         else:
          self.scheduler.step(*__UpperCAmelCase					,   **__UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		Optional[int]    )       ->List[str]:




       """simple docstring"""


       return self.scheduler.get_last_lr()







      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->Any:




       """simple docstring"""


       return self.scheduler.state_dict()







      def  __lowerCAmelCase   ( self :		Union[str, Any]					,   __UpperCAmelCase :		Any    )       ->Tuple:




       """simple docstring"""


       self.scheduler.load_state_dict(__UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		Dict    )       ->Optional[int]:




       """simple docstring"""


       return self.scheduler.get_lr()







      def  __lowerCAmelCase   ( self :		int					,   *__UpperCAmelCase :		List[str]					,   **__UpperCAmelCase :		Optional[Any]    )       ->List[str]:




       """simple docstring"""


       return self.scheduler.print_lr(*__UpperCAmelCase					,   **__UpperCAmelCase    )


