



"""simple docstring"""




import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase							:   List[Any]				=				get_tests_dir("fixtures/test_sentencepiece_bpe.model")






class       UpperCAmelCase_    (							_UpperCamelCase			,     unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:  List[Any]						=					BartphoTokenizer
      __SCREAMING_SNAKE_CASE		:  int						=					False
      __SCREAMING_SNAKE_CASE		:  str						=					True



      def 		snake_case_   (	self	:      Union[str, Any]		):
           super().setUp()

           _UpperCAmelCase     :      List[Any]														=       ["▁This", "▁is", "▁a", "▁t", "est"]
           _UpperCAmelCase     :      Dict														=       dict(zip(A	,      range(len(A		)		)		)		)
           _UpperCAmelCase     :      Dict														=       {"unk_token": "<unk>"}

           _UpperCAmelCase     :      Optional[Any]														=       os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES["monolingual_vocab_file"]		)
           with open(self.monolingual_vocab_file	,      "w"	,      encoding="utf-8"		) as fp:
                for token in vocab_tokens:
                     fp.write(f'{token} {vocab_tokens[token]}\n'		)

           _UpperCAmelCase     :      Optional[Any]														=       BartphoTokenizer(A	,      self.monolingual_vocab_file	,      **self.special_tokens_map		)
           tokenizer.save_pretrained(self.tmpdirname		)



      def 		snake_case_   (	self	:      Any	,      **A	:      List[Any]		):
           kwargs.update(self.special_tokens_map		)
           return BartphoTokenizer.from_pretrained(self.tmpdirname	,      **A		)



      def 		snake_case_   (	self	:      str	,      A	:      Any		):
           _UpperCAmelCase     :      Any														=       "This is a là test"
           _UpperCAmelCase     :      Any														=       "This is a<unk><unk> test"
           return input_text, output_text



      def 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      List[Any]														=       BartphoTokenizer(A	,      self.monolingual_vocab_file	,      **self.special_tokens_map		)
           _UpperCAmelCase     :      int														=       "This is a là test"
           _UpperCAmelCase     :      Union[str, Any]														=       "▁This ▁is ▁a ▁l à ▁t est".split()
           _UpperCAmelCase     :      str														=       tokenizer.tokenize(A		)
           self.assertListEqual(A	,      A		)

           _UpperCAmelCase     :      str														=       tokens + [tokenizer.unk_token]
           _UpperCAmelCase     :      List[str]														=       [4, 5, 6, 3, 3, 7, 8, 3]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(A		)	,      A		)





"""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       UpperCAmelCase_    :



      def __init__(	self	:      Optional[Any]	,      A	:      List[str]	,      A	:      List[str]	,      A	:      bool = True	,      A	:      bool = False		):
           _UpperCAmelCase     :      Dict														=       scheduler
           _UpperCAmelCase     :      str														=       optimizers if isinstance(A	,      (list, tuple)		) else [optimizers]
           _UpperCAmelCase     :      List[str]														=       split_batches
           _UpperCAmelCase     :      Dict														=       step_with_optimizer
           _UpperCAmelCase     :      Tuple														=       GradientState()



      def 		snake_case_   (	self	:      Optional[int]	,      *A	:      List[Any]	,      **A	:      Optional[int]		):
           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
                _UpperCAmelCase     :      int														=       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	:      Union[str, Any]		):
           return self.scheduler.get_last_lr()



      def 		snake_case_   (	self	:      str		):
           return self.scheduler.state_dict()



      def 		snake_case_   (	self	:      str	,      A	:      Optional[Any]		):
           self.scheduler.load_state_dict(A		)



      def 		snake_case_   (	self	:      Tuple		):
           return self.scheduler.get_lr()



      def 		snake_case_   (	self	:      Optional[int]	,      *A	:      List[str]	,      **A	:      Optional[Any]		):
           return self.scheduler.print_lr(*A	,      **A		)
