'''simple docstring'''





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase  :Tuple  				=    logging.get_logger(__name__)

_lowerCAmelCase  :Tuple  				=    """▁"""

_lowerCAmelCase  :List[str]  				=    {"""vocab_file""": """sentencepiece.bpe.model"""}

_lowerCAmelCase  :List[Any]  				=    {
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    }
}

_lowerCAmelCase  :Optional[int]  				=    {
    """facebook/xglm-564M""": 2_048,
}






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Tuple							=						VOCAB_FILES_NAMES
    snake_case__							:   str							=						PRETRAINED_VOCAB_FILES_MAP
    snake_case__							:   List[Any]							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__							:   Tuple							=						["input_ids", "attention_mask"]






    def __init__(       self    ,							lowercase__    ,							lowercase__="<s>"    ,							lowercase__="</s>"    ,							lowercase__="</s>"    ,							lowercase__="<s>"    ,							lowercase__="<unk>"    ,							lowercase__="<pad>"    ,							lowercase__ = None    ,							**lowercase__    ,							)							-> None:
         SCREAMING_SNAKE_CASE					:			int    	=       {} if sp_model_kwargs is None else sp_model_kwargs

         # Compatibility with the original tokenizer
         SCREAMING_SNAKE_CASE					:			int    	=       7
         SCREAMING_SNAKE_CASE					:			Any    	=       [F"""<madeupword{i}>""" for i in range(self.num_madeup_words )]

         SCREAMING_SNAKE_CASE					:			int    	=       kwargs.get('additional_special_tokens'    ,							[] )
         kwargs["additional_special_tokens"] += [
             word for word in madeup_words if word not in kwargs["additional_special_tokens"]
         ]

         super().__init__(
             bos_token=lowercase__    ,							eos_token=lowercase__    ,							unk_token=lowercase__    ,							sep_token=lowercase__    ,							cls_token=lowercase__    ,							pad_token=lowercase__    ,							sp_model_kwargs=self.sp_model_kwargs    ,							**lowercase__    ,							)

         SCREAMING_SNAKE_CASE					:			int    	=       spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(str(lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       vocab_file

         # Original fairseq vocab and spm vocab must be "aligned":
         # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
         # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
         # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
         # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

         # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       1

         # Mimic fairseq token-to-id alignment for the first 4 token
         SCREAMING_SNAKE_CASE					:			int    	=       {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       len(self.sp_model )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       {F"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
         self.fairseq_tokens_to_ids.update(lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[str]    	=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}






    def __getstate__(       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			int    	=       self.__dict__.copy()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       None
         SCREAMING_SNAKE_CASE					:			int    	=       self.sp_model.serialized_model_proto()
         return state






    def __setstate__(       self    ,							lowercase__ )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       d

         # for backward compatibility
         if not hasattr(self    ,							'sp_model_kwargs' ):
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       {}

         SCREAMING_SNAKE_CASE					:			int    	=       spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None )							-> List[int]:

         if token_ids_a is None:
              return [self.sep_token_id] + token_ids_a
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       [self.sep_token_id]
         return sep + token_ids_a + sep + sep + token_ids_a






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None    ,							lowercase__ = False )							-> List[int]:

         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=lowercase__    ,							token_ids_a=lowercase__    ,							already_has_special_tokens=lowercase__ )

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






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None )							-> List[int]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [self.sep_token_id]

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






    @property
    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       {self.convert_ids_to_tokens(lowercase__ ): i for i in range(self.vocab_size )}
         vocab.update(self.added_tokens_encoder )
         return vocab






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> List[str]:
         return self.sp_model.encode(lowercase__    ,							out_type=lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Union[str, Any]:
         if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.sp_model.PieceToId(lowercase__ )

         # Need to return unknown token if the SP model returned 0
         return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> int:
         if index in self.fairseq_ids_to_tokens:
              return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset )






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       ''.join(lowercase__ ).replace(lowercase__    ,							' ' ).strip()
         return out_string






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None )							-> Tuple[str]:
         if not os.path.isdir(lowercase__ ):
              logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
              return
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       os.path.join(
             lowercase__    ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

         if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase__ ) and os.path.isfile(self.vocab_file ):
              copyfile(self.vocab_file    ,							lowercase__ )
         elif not os.path.isfile(self.vocab_file ):
              with open(lowercase__    ,							'wb' ) as fi:
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.sp_model.serialized_model_proto()
                   fi.write(lowercase__ )

         return (out_vocab_file,)




'''simple docstring'''





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 (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   int							=						0
    snake_case__							:   bool							=						False
    snake_case__							:   float							=						3.0






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> Tuple:
         # 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=lowercase__ ).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       _UpperCamelCase    (       self )							-> Dict:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       GradScalerKwargs(init_scale=1_024    ,							growth_factor=2 )
         AcceleratorState._reset_state()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       Accelerator(mixed_precision='fp16'    ,							kwargs_handlers=[scaler_handler] )
         print(accelerator.use_fpaa )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       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_000 )
         self.assertEqual(scaler._enabled    ,							lowercase__ )






    @require_multi_gpu
    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
         execute_subprocess_async(lowercase__    ,							env=os.environ.copy() )


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

 # Check the values changed in kwargs
 _lowerCAmelCase  :Dict  				=    """"""
 _lowerCAmelCase  :Optional[int]  				=    model.bucket_bytes_cap // (1_024 * 1_024)
 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)



