



from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


_lowercase    	=		logging.get_logger(__name__)

_lowercase    	=		{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowercase    	=		{
    "vocab_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
    },
    "merges_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
    },
}

_lowercase    	=		{"allegro/herbert-base-cased": 514}
_lowercase    	=		{}



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					VOCAB_FILES_NAMES
     UpperCamelCase__		     =					PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase__		     =					PRETRAINED_INIT_CONFIGURATION
     UpperCamelCase__		     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase__		     =					HerbertTokenizer




     def __init__(					self			,    a__=None			,    a__=None			,    a__=None			,    a__="<s>"			,    a__="<unk>"			,    a__="<pad>"			,    a__="<mask>"			,    a__="</s>"			,    **a__			,    ):
         super().__init__(
             a__			,    a__			,    tokenizer_file=a__			,    cls_token=a__			,    unk_token=a__			,    pad_token=a__			,    mask_token=a__			,    sep_token=a__			,    **a__			,    )




     def 		snake_case_		(					self			,    a__			,    a__ = None):
         A__					=       [self.cls_token_id]
         A__					=       [self.sep_token_id]
         if token_ids_a is None:
             return cls + token_ids_a + sep

         return cls + token_ids_a + sep + token_ids_a + sep




     def 		snake_case_		(					self			,    a__			,    a__ = None			,    a__ = False):
         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=a__			,    token_ids_a=a__			,    already_has_special_tokens=a__)

         if token_ids_a is None:
             return [1] + ([0] * len(a__)) + [1]
         return [1] + ([0] * len(a__)) + [1] + ([0] * len(a__)) + [1]




     def 		snake_case_		(					self			,    a__			,    a__ = None):
         A__					=       [self.sep_token_id]
         A__					=       [self.cls_token_id]

         if token_ids_a is None:
             return len(cls + token_ids_a + sep) * [0]
         return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]




     def 		snake_case_		(					self			,    a__			,    a__ = None):
         A__					=       self._tokenizer.model.save(a__			,    name=a__)
         return tuple(a__)





import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler



@dataclass
class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					0
     UpperCamelCase__		     =					False
     UpperCamelCase__		     =					3.0







class      _UpperCAmelCase      (							unittest.TestCase  ):




     def 		snake_case_		(					self):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         self.assertDictEqual(MockClass().to_kwargs()			,    {})
         self.assertDictEqual(MockClass(a=2).to_kwargs()			,    {'''a''': 2})
         self.assertDictEqual(MockClass(a=2			,    b=a__).to_kwargs()			,    {'''a''': 2, '''b''': True})
         self.assertDictEqual(MockClass(a=2			,    c=2.2_5).to_kwargs()			,    {'''a''': 2, '''c''': 2.2_5})




     @require_cuda
     def 		snake_case_		(					self):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         A__					=       GradScalerKwargs(init_scale=1_0_2_4			,    growth_factor=2)
         AcceleratorState._reset_state()
         A__					=       Accelerator(mixed_precision='''fp16'''			,    kwargs_handlers=[scaler_handler])
         print(accelerator.use_fpaa)
         A__					=       accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale			,    1_0_2_4.0)
         self.assertEqual(scaler._growth_factor			,    2.0)

         # Check the other values are at the default
         self.assertEqual(scaler._backoff_factor			,    0.5)
         self.assertEqual(scaler._growth_interval			,    2_0_0_0)
         self.assertEqual(scaler._enabled			,    a__)




     @require_multi_gpu
     def 		snake_case_		(					self):
         A__					=       ['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__)]
         execute_subprocess_async(a__			,    env=os.environ.copy())


if __name__ == "__main__":
   _lowercase    	=		DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
   _lowercase    	=		Accelerator(kwargs_handlers=[ddp_scaler])
   _lowercase    	=		torch.nn.Linear(100, 200)
   _lowercase    	=		accelerator.prepare(model)

   # Check the values changed in kwargs
   _lowercase    	=		""
   _lowercase    	=		model.bucket_bytes_cap // (1024 * 1024)
   if observed_bucket_cap_map != 15:
      error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
   if model.find_unused_parameters is not True:
      error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

   # Check the values of the defaults
   if model.dim != 0:
      error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
   if model.broadcast_buffers is not True:
      error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
   if model.gradient_as_bucket_view is not False:
      error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

   # Raise error at the end to make sure we don't stop at the first failure.
   if len(error_msg) > 0:
      raise ValueError(error_msg)
