






'''simple docstring'''


from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers




class 							lowerCAmelCase					:







   """simple docstring"""







   def __init__( self   ,   _A			)    ->   None:
    __a    :	Optional[int]									=						vocab
    __a    :	Dict									=						normalizers.BertNormalizer(
        clean_text=_A   ,   handle_chinese_chars=_A   ,   strip_accents=_A   ,   lowercase=_A   ,   )
    try:
     import rjieba
    except ImportError:
     raise ImportError(
         'You need to install rjieba to use RoFormerTokenizer. '
         'See https://pypi.org/project/rjieba/ for installation.'			)
    __a    :	Optional[Any]									=						rjieba



   def    __magic_name__    ( self   ,   _A   ,   _A			)    ->   List[NormalizedString]:
    __a    :	Optional[Any]									=						[]

    # this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
    for token, start, end in self.jieba.tokenize(str(_A			)   ,   hmm=_A			):
     if token in self.vocab:
      splits.append(normalized_string[start:end]			)
     else:
      __a    :	Any									=						self.normalizers.normalize_str(_A			).split()
      for token in token_list:
       if token:
        __a    :	str									=						start + len(_A			)
        splits.append(normalized_string[start:end]			)
        __a    :	List[str]									=						end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

    return splits



   def    __magic_name__    ( self   ,   _A			)    ->   int:
    pretok.split(self.jieba_split			)













'''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

SCREAMING_SNAKE_CASE_				      =							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   lowerCAmelCase__			(					cls		):
   return cls






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__ = None       ,		SCREAMING_SNAKE_CASE__ = None       ,		SCREAMING_SNAKE_CASE__ = None       ,		SCREAMING_SNAKE_CASE__ = None       ,		):
 __a    :	int									=						device
 __a    :	Union[str, Any]									=						shape[0]

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

 if generator is not None:
  __a    :	Optional[int]									=						generator.device.type if not isinstance(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		) else generator[0].device.type
  if gen_device_type != device.type and gen_device_type == "cpu":
   __a    :	int									=						'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(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
  __a    :	Optional[int]									=						(1,) + shape[1:]
  __a    :	Dict									=						[
      torch.randn(SCREAMING_SNAKE_CASE__       ,		generator=generator[i]       ,		device=SCREAMING_SNAKE_CASE__       ,		dtype=SCREAMING_SNAKE_CASE__       ,		layout=SCREAMING_SNAKE_CASE__		)
      for i in range(SCREAMING_SNAKE_CASE__		)
  ]
  __a    :	List[str]									=						torch.cat(SCREAMING_SNAKE_CASE__       ,		dim=0		).to(SCREAMING_SNAKE_CASE__		)
 else:
  __a    :	Tuple									=						torch.randn(SCREAMING_SNAKE_CASE__       ,		generator=SCREAMING_SNAKE_CASE__       ,		device=SCREAMING_SNAKE_CASE__       ,		dtype=SCREAMING_SNAKE_CASE__       ,		layout=SCREAMING_SNAKE_CASE__		).to(SCREAMING_SNAKE_CASE__		)

 return latents






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




