


'''simple docstring'''



import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


A__						:   str				=    get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model""")





@require_sentencepiece
@require_tokenizers
class   UpperCAmelCase_    (_UpperCAmelCase   ,						unittest.TestCase    ):





    """simple docstring"""






    lowerCamelCase    :   int					=     GPTSwaTokenizer
    lowerCamelCase    :   List[str]					=     False
    lowerCamelCase    :   Optional[Any]					=     True
    lowerCamelCase    :   int					=     False







    def  lowercase_							(				self      )							->	Tuple:
         super().setUp()

         # We have a SentencePiece fixture for testing
         __lowerCamelCase  :      Tuple														=						GPTSwaTokenizer(SCREAMING_SNAKE_CASE_     ,	eos_token='<unk>'     ,	bos_token='<unk>'     ,	pad_token='<unk>'      )

         tokenizer.save_pretrained(self.tmpdirname      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	int:
         __lowerCamelCase  :      int														=						'This is a test'
         __lowerCamelCase  :      Tuple														=						'This is a test'
         return input_text, output_text







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase  :      Any														=						'<s>'
         __lowerCamelCase  :      List[Any]														=						1

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







    def  lowercase_							(				self      )							->	Optional[int]:
         __lowerCamelCase  :      Optional[Any]														=						list(self.get_tokenizer().get_vocab().keys()      )

         self.assertEqual(vocab_keys[0]     ,	'<unk>'      )
         self.assertEqual(vocab_keys[1]     ,	'<s>'      )
         self.assertEqual(vocab_keys[-1]     ,	'j'      )
         self.assertEqual(len(SCREAMING_SNAKE_CASE_      )     ,	20_00      )







    def  lowercase_							(				self      )							->	List[Any]:
         self.assertEqual(self.get_tokenizer().vocab_size     ,	20_00      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      int														=						GPTSwaTokenizer(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Optional[Any]														=						tokenizer.tokenize('This is a test'      )
         self.assertListEqual(SCREAMING_SNAKE_CASE_     ,	['▁This', '▁is', '▁a', '▁t', 'est']      )

         self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_      )     ,	[4_65, 2_87, 2_65, 6_31, 8_42]      )

         __lowerCamelCase  :      Optional[int]														=						tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
         # fmt: off
         self.assertListEqual(
             SCREAMING_SNAKE_CASE_     ,	['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']     ,	)
         # fmt: on

         __lowerCamelCase  :      Any														=						tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_      )
         self.assertListEqual(
             SCREAMING_SNAKE_CASE_     ,	[2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60]     ,	)

         __lowerCamelCase  :      Dict														=						tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_      )
         # fmt: off
         self.assertListEqual(
             SCREAMING_SNAKE_CASE_     ,	['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']      )
         # fmt: on







    def  lowercase_							(				self      )							->	Tuple:
         __lowerCamelCase  :      str														=						GPTSwaTokenizer(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Optional[int]														=						['This is a test', 'I was born in 92000, and this is falsé.']
         __lowerCamelCase  :      Tuple														=						[
             [4_65, 2_87, 2_65, 6_31, 8_42],
             [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60],
         ]

         # Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
         for text, expected_ids in zip(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
              self.assertListEqual(tokenizer.encode_fast(SCREAMING_SNAKE_CASE_      )     ,	SCREAMING_SNAKE_CASE_      )

         # Test that decode_fast returns the input text
         for text, token_ids in zip(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
              self.assertEqual(tokenizer.decode_fast(SCREAMING_SNAKE_CASE_      )     ,	SCREAMING_SNAKE_CASE_      )







    @slow
    def  lowercase_							(				self      )							->	Union[str, Any]:
         __lowerCamelCase  :      Union[str, Any]														=						[
             '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
             'Hey there, how are you doing this fine day?',
             'This is a text with a trailing spaces followed by a dot     .',
             'Häj sväjs lillebrör! =)',
             'Det är inget fel på Mr. Cool',
         ]

         # fmt: off
         __lowerCamelCase  :      Union[str, Any]														=						{'input_ids': [[6_34_23, 5, 68_11, 1_49_54, 2_82, 8_16, 38_21, 6_34_66, 6_34_25, 6_34_62, 18, 6_39_78, 6_78, 3_01, 13_20, 6_34_23, 6_34_55, 6_34_58, 18, 6_39_82, 42_46, 39_40, 19_01, 4_77_89, 55_47, 1_89_94], [1_96_30, 11_00, 6_34_46, 13_42, 6_33, 5_44, 44_88, 5_93, 51_02, 24_16, 6_34_95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [16_52, 4_28, 2_68, 19_36, 5_15, 2_68, 5_85_93, 2_24_13, 91_06, 5_46, 2_68, 3_32_13, 6_39_79, 6_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_51_30, 6_34_50, 9_24, 6_34_49, 22_49, 40_62, 15_58, 3_18, 6_35_04, 2_14_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_09, 3_77, 28_27, 25_59, 3_32, 65_75, 6_34_43, 2_68_01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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], [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]]}
         # fmt: on
         self.tokenizer_integration_test_util(
             expected_encoding=SCREAMING_SNAKE_CASE_     ,	model_name='AI-Sweden/gpt-sw3-126m'     ,	sequences=SCREAMING_SNAKE_CASE_     ,	)












'''simple docstring'''







# 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 					_a :





     '''simple docstring'''



     def __init__(				self							,__a							,__a							,__a = True							,__a = False							)      ->				Dict:
          snake_case			:      List[Any]        =			scheduler
          snake_case			:      List[Any]        =			optimizers if isinstance(__a							,(list, tuple)							) else [optimizers]
          snake_case			:      List[str]        =			split_batches
          snake_case			:      List[str]        =			step_with_optimizer
          snake_case			:      int        =			GradientState()







     def 			snake_case_      (				self							,*__a							,**__a							)      ->				Optional[Any]:
          if not self.step_with_optimizer:
               # No link between scheduler and optimizer -> just step
               self.scheduler.step(*__a							,**__a							)
               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(*__a							,**__a							)
          else:
               # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
               # num_processes steps per training step
               snake_case			:      List[str]        =			AcceleratorState().num_processes
               for _ in range(__a							):
                    # 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(*__a							,**__a							)
                    else:
                         self.scheduler.step(*__a							,**__a							)







     def 			snake_case_      (				self							)      ->				Dict:
          return self.scheduler.get_last_lr()







     def 			snake_case_      (				self							)      ->				int:
          return self.scheduler.state_dict()







     def 			snake_case_      (				self							,__a							)      ->				Any:
          self.scheduler.load_state_dict(__a							)







     def 			snake_case_      (				self							)      ->				Any:
          return self.scheduler.get_lr()







     def 			snake_case_      (				self							,*__a							,**__a							)      ->				Optional[Any]:
          return self.scheduler.print_lr(*__a							,**__a							)



