


'''simple docstring'''





import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_reformer import ReformerTokenizer
else:
    __lowerCamelCase       				=	None


__lowerCamelCase       				=	logging.get_logger(__name__)


__lowerCamelCase       				=	'''▁'''

__lowerCamelCase       				=	{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCamelCase       				=	{
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    },
    '''tokenizer_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'''
        )
    },
}

__lowerCamelCase       				=	{
    '''google/reformer-crime-and-punishment''': 52_4288,
}




class 							UpperCAmelCase ( _snake_case	):
 UpperCAmelCase				=				VOCAB_FILES_NAMES
 UpperCAmelCase				=				PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase				=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase				=				["input_ids", "attention_mask"]
 UpperCAmelCase				=				ReformerTokenizer


 def __init__(			self       :  Tuple ,					__lowerCamelCase       :  str=None ,					__lowerCamelCase       :  Dict=None ,					__lowerCamelCase       :  Dict="</s>" ,					__lowerCamelCase       :  Dict="<unk>" ,					__lowerCamelCase       :  List[str]=[] ,					**__lowerCamelCase       :  Tuple ,					):
    super().__init__(
        __lowerCamelCase ,					tokenizer_file=__lowerCamelCase ,					eos_token=__lowerCamelCase ,					unk_token=__lowerCamelCase ,					additional_special_tokens=__lowerCamelCase ,					**__lowerCamelCase ,					)

    UpperCAmelCase__  :List[Any]            =			vocab_file
    UpperCAmelCase__  :List[Any]            =			False if not self.vocab_file else True





 def 					__SCREAMING_SNAKE_CASE				(			self       :  Tuple ,					__lowerCamelCase       :  str ,					__lowerCamelCase       :  Optional[str] = None   ):
    if not self.can_save_slow_tokenizer:
       raise ValueError(
           '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
           '''tokenizer.'''   )

    if not os.path.isdir(__lowerCamelCase   ):
       logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''   )
       return
    UpperCAmelCase__  :str            =			os.path.join(
        __lowerCamelCase ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

    if os.path.abspath(self.vocab_file   ) != os.path.abspath(__lowerCamelCase   ):
       copyfile(self.vocab_file ,					__lowerCamelCase   )

    return (out_vocab_file,)







'''simple docstring'''





from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
    import torch

__lowerCamelCase       				=	logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
    from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):


    def 					a__    (				cls	:			Optional[int]	):
       return cls


def 					a__    (				UpperCamelCase_	:			Union[Tuple, List],      UpperCamelCase_	:			Optional[Union[List["torch.Generator"], "torch.Generator"]] = None,      UpperCamelCase_	:			Optional["torch.device"] = None,      UpperCamelCase_	:			Optional["torch.dtype"] = None,      UpperCamelCase_	:			Optional["torch.layout"] = None,      ):
   UpperCAmelCase__  :Union[str, Any]            =			device
   UpperCAmelCase__  :List[Any]            =			shape[0]

   UpperCAmelCase__  :Tuple            =			layout or torch.strided
   UpperCAmelCase__  :Union[str, Any]            =			device or torch.device('''cpu'''	)

   if generator is not None:
      UpperCAmelCase__  :str            =			generator.device.type if not isinstance(UpperCamelCase_,      UpperCamelCase_	) else generator[0].device.type
      if gen_device_type != device.type and gen_device_type == "cpu":
         UpperCAmelCase__  :Tuple            =			'''cpu'''
         if device != "mps":
            logger.info(
                F'''The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'''
                F''' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'''
                F''' slighly speed up this function by passing a generator that was created on the {device} device.'''	)
      elif gen_device_type != device.type and gen_device_type == "cuda":
         raise ValueError(F'''Cannot generate a {device} tensor from a generator of type {gen_device_type}.'''	)

   if isinstance(UpperCamelCase_,      UpperCamelCase_	):
      UpperCAmelCase__  :List[str]            =			(1,) + shape[1:]
      UpperCAmelCase__  :Optional[int]            =			[
          torch.randn(UpperCamelCase_,      generator=generator[i],      device=UpperCamelCase_,      dtype=UpperCamelCase_,      layout=UpperCamelCase_	)
          for i in range(UpperCamelCase_	)
      ]
      UpperCAmelCase__  :int            =			torch.cat(UpperCamelCase_,      dim=0	).to(UpperCamelCase_	)
   else:
      UpperCAmelCase__  :Optional[int]            =			torch.randn(UpperCamelCase_,      generator=UpperCamelCase_,      device=UpperCamelCase_,      dtype=UpperCamelCase_,      layout=UpperCamelCase_	).to(UpperCamelCase_	)

   return latents


def 					a__    (				UpperCamelCase_	:			Dict	):
   if is_torch_version('''<''',      '''2.0.0'''	) or not hasattr(UpperCamelCase_,      '''_dynamo'''	):
      return False
   return isinstance(UpperCamelCase_,      torch._dynamo.eval_frame.OptimizedModule	)



