


'''simple docstring'''



import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


A__		: Dict												=  [nn.LayerNorm]

A__		: Dict												=  logging.get_logger(__name__)

A__		: Optional[int]												=  version.parse(version.parse(torch.__version__).base_version)

A__		: Dict												=  parsed_torch_version_base >= version.parse("""2.0""")
A__		: List[str]												=  parsed_torch_version_base >= version.parse("""1.12""")
A__		: int												=  parsed_torch_version_base >= version.parse("""1.11""")
A__		: Tuple												=  parsed_torch_version_base < version.parse("""1.11""")



def       UpperCAmelCase__ (     UpperCAmelCase_   :    Union[str, Any]				,   UpperCAmelCase_   :    List[Any]				,   UpperCAmelCase_   :    Dict				,   UpperCAmelCase_   :    Dict				,   self   :    Tuple							)					->		Tuple:
     from torch import _softmax_backward_data

     if is_torch_less_than_1_11:
          return _softmax_backward_data(UpperCAmelCase_				,   UpperCAmelCase_				,   parent.dim				,   self							)
     else:
          return _softmax_backward_data(UpperCAmelCase_				,   UpperCAmelCase_				,   parent.dim				,   self.dtype							)



def       UpperCAmelCase__ (     UpperCAmelCase_   :    nn.Linear				,   UpperCAmelCase_   :    torch.LongTensor				,   UpperCAmelCase_   :    int = 0							)					->		nn.Linear:
     __lowerCamelCase       :     Optional[int]        =		index.to(layer.weight.device							)
     __lowerCamelCase       :     Tuple        =		layer.weight.index_select(UpperCAmelCase_				,   UpperCAmelCase_							).clone().detach()
     if layer.bias is not None:
          if dim == 1:
               __lowerCamelCase       :     List[Any]        =		layer.bias.clone().detach()
          else:
               __lowerCamelCase       :     Optional[Any]        =		layer.bias[index].clone().detach()
     __lowerCamelCase       :     List[Any]        =		list(layer.weight.size()							)
     __lowerCamelCase       :     str        =		len(UpperCAmelCase_							)
     __lowerCamelCase       :     Dict        =		nn.Linear(new_size[1]				,   new_size[0]				,   bias=layer.bias is not None							).to(layer.weight.device							)
     __lowerCamelCase       :     Tuple        =		False
     new_layer.weight.copy_(W.contiguous()							)
     __lowerCamelCase       :     Tuple        =		True
     if layer.bias is not None:
          __lowerCamelCase       :     Dict        =		False
          new_layer.bias.copy_(b.contiguous()							)
          __lowerCamelCase       :     Union[str, Any]        =		True
     return new_layer





class        snake_case_  (nn.Module  ):





    """simple docstring"""






    def __init__(							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )    ->					Any:
         super().__init__()
         __lowerCamelCase       :     Union[str, Any]        =		nf
         __lowerCamelCase       :     int        =		nn.Parameter(torch.empty(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )     )
         __lowerCamelCase       :     Tuple        =		nn.Parameter(torch.zeros(SCREAMING_SNAKE_CASE_     )     )
         nn.init.normal_(self.weight      ,					std=0.0_2     )







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_     )    ->					List[str]:
         __lowerCamelCase       :     Union[str, Any]        =		x.size()[:-1] + (self.nf,)
         __lowerCamelCase       :     int        =		torch.addmm(self.bias      ,					x.view(-1      ,					x.size(-1     )     )      ,					self.weight     )
         __lowerCamelCase       :     int        =		x.view(SCREAMING_SNAKE_CASE_     )
         return x







def       UpperCAmelCase__ (     UpperCAmelCase_   :    ConvaD				,   UpperCAmelCase_   :    torch.LongTensor				,   UpperCAmelCase_   :    int = 1							)					->		ConvaD:
     __lowerCamelCase       :     Any        =		index.to(layer.weight.device							)
     __lowerCamelCase       :     Optional[int]        =		layer.weight.index_select(UpperCAmelCase_				,   UpperCAmelCase_							).clone().detach()
     if dim == 0:
          __lowerCamelCase       :     Union[str, Any]        =		layer.bias.clone().detach()
     else:
          __lowerCamelCase       :     Tuple        =		layer.bias[index].clone().detach()
     __lowerCamelCase       :     Dict        =		list(layer.weight.size()							)
     __lowerCamelCase       :     Tuple        =		len(UpperCAmelCase_							)
     __lowerCamelCase       :     Optional[int]        =		ConvaD(new_size[1]				,   new_size[0]							).to(layer.weight.device							)
     __lowerCamelCase       :     Union[str, Any]        =		False
     new_layer.weight.copy_(W.contiguous()							)
     __lowerCamelCase       :     Any        =		True
     __lowerCamelCase       :     Optional[Any]        =		False
     new_layer.bias.copy_(b.contiguous()							)
     __lowerCamelCase       :     List[Any]        =		True
     return new_layer



def       UpperCAmelCase__ (     UpperCAmelCase_   :    Union[nn.Linear, ConvaD]				,   UpperCAmelCase_   :    torch.LongTensor				,   UpperCAmelCase_   :    Optional[int] = None							)					->		Union[nn.Linear, ConvaD]:
     if isinstance(UpperCAmelCase_				,   nn.Linear							):
          return prune_linear_layer(UpperCAmelCase_				,   UpperCAmelCase_				,   dim=0 if dim is None else dim							)
     elif isinstance(UpperCAmelCase_				,   UpperCAmelCase_							):
          return prune_convad_layer(UpperCAmelCase_				,   UpperCAmelCase_				,   dim=1 if dim is None else dim							)
     else:
          raise ValueError(F'Can\'t prune layer of class {layer.__class__}'							)



def       UpperCAmelCase__ (     UpperCAmelCase_   :    Callable[..., torch.Tensor]				,   UpperCAmelCase_   :    int				,   UpperCAmelCase_   :    int				,   *UpperCAmelCase_   :    Optional[int]							)					->		torch.Tensor:
     assert len(UpperCAmelCase_							) > 0, F'{input_tensors} has to be a tuple/list of tensors'

     # inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
     __lowerCamelCase       :     Any        =		len(inspect.signature(UpperCAmelCase_							).parameters							)
     if num_args_in_forward_chunk_fn != len(UpperCAmelCase_							):
          raise ValueError(
              F'forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(UpperCAmelCase_							)} input '
              'tensors are given'							)

     if chunk_size > 0:
          __lowerCamelCase       :     int        =		input_tensors[0].shape[chunk_dim]
          for input_tensor in input_tensors:
               if input_tensor.shape[chunk_dim] != tensor_shape:
                    raise ValueError(
                        F'All input tenors have to be of the same shape: {tensor_shape}, '
                        F'found shape {input_tensor.shape[chunk_dim]}'							)

          if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
               raise ValueError(
                   F'The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '
                   F'size {chunk_size}'							)

          __lowerCamelCase       :     Optional[Any]        =		input_tensors[0].shape[chunk_dim] // chunk_size

          # chunk input tensor into tuples
          __lowerCamelCase       :     List[Any]        =		tuple(input_tensor.chunk(UpperCAmelCase_				,   dim=UpperCAmelCase_							) for input_tensor in input_tensors							)
          # apply forward fn to every tuple
          __lowerCamelCase       :     str        =		tuple(forward_fn(*UpperCAmelCase_							) for input_tensors_chunk in zip(*UpperCAmelCase_							)							)
          # concatenate output at same dimension
          return torch.cat(UpperCAmelCase_				,   dim=UpperCAmelCase_							)

     return forward_fn(*UpperCAmelCase_							)



def       UpperCAmelCase__ (     UpperCAmelCase_   :    List[int]				,   UpperCAmelCase_   :    int				,   UpperCAmelCase_   :    int				,   UpperCAmelCase_   :    Set[int]							)					->		Tuple[Set[int], torch.LongTensor]:
     __lowerCamelCase       :     Dict        =		torch.ones(UpperCAmelCase_				,   UpperCAmelCase_							)
     __lowerCamelCase       :     Any        =		set(UpperCAmelCase_							) - already_pruned_heads  # Convert to set and remove already pruned heads
     for head in heads:
          # Compute how many pruned heads are before the head and move the index accordingly
          __lowerCamelCase       :     Tuple        =		head - sum(1 if h < head else 0 for h in already_pruned_heads							)
          __lowerCamelCase       :     str        =		0
     __lowerCamelCase       :     Tuple        =		mask.view(-1							).contiguous().eq(1							)
     __lowerCamelCase       :     torch.LongTensor        =		torch.arange(len(UpperCAmelCase_							)							)[mask].long()
     return heads, index



def       UpperCAmelCase__ (     *UpperCAmelCase_   :    Union[torch.Tensor, List[torch.Tensor]]				,   UpperCAmelCase_   :    Optional[str] = None							)					->		Tuple[torch.Tensor, ...]:
     return torch.meshgrid(*UpperCAmelCase_				,   indexing=UpperCAmelCase_							)



def       UpperCAmelCase__ (     UpperCAmelCase_   :    torch.Tensor							)					->		Tuple[torch.device, int, int]:
     return tensor.device, storage_ptr(UpperCAmelCase_							), storage_size(UpperCAmelCase_							)










'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


A__		: int												=  logging.get_logger(__name__)

A__		: Union[str, Any]												=  {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

A__		: int												=  {
    """vocab_file""": {
        """microsoft/mpnet-base""": """https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """microsoft/mpnet-base""": """https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json""",
    },
}

A__		: int												=  {
    """microsoft/mpnet-base""": 512,
}

A__		: Optional[Any]												=  {
    """microsoft/mpnet-base""": {"""do_lower_case""": True},
}





class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  : int												=      VOCAB_FILES_NAMES
    lowerCamelCase  : int												=      PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase  : List[Any]												=      PRETRAINED_INIT_CONFIGURATION
    lowerCamelCase  : int												=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase  : Tuple												=      MPNetTokenizer
    lowerCamelCase  : List[Any]												=      ['input_ids', 'attention_mask']







    def __init__(							self      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_=True      ,					SCREAMING_SNAKE_CASE_="<s>"      ,					SCREAMING_SNAKE_CASE_="</s>"      ,					SCREAMING_SNAKE_CASE_="</s>"      ,					SCREAMING_SNAKE_CASE_="<s>"      ,					SCREAMING_SNAKE_CASE_="[UNK]"      ,					SCREAMING_SNAKE_CASE_="<pad>"      ,					SCREAMING_SNAKE_CASE_="<mask>"      ,					SCREAMING_SNAKE_CASE_=True      ,					SCREAMING_SNAKE_CASE_=None      ,					**SCREAMING_SNAKE_CASE_      ,					)    ->					Any:
         super().__init__(
             SCREAMING_SNAKE_CASE_      ,					tokenizer_file=SCREAMING_SNAKE_CASE_      ,					do_lower_case=SCREAMING_SNAKE_CASE_      ,					bos_token=SCREAMING_SNAKE_CASE_      ,					eos_token=SCREAMING_SNAKE_CASE_      ,					sep_token=SCREAMING_SNAKE_CASE_      ,					cls_token=SCREAMING_SNAKE_CASE_      ,					unk_token=SCREAMING_SNAKE_CASE_      ,					pad_token=SCREAMING_SNAKE_CASE_      ,					mask_token=SCREAMING_SNAKE_CASE_      ,					tokenize_chinese_chars=SCREAMING_SNAKE_CASE_      ,					strip_accents=SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_      ,					)

         __lowerCamelCase       :     Union[str, Any]        =		json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
         if (
             pre_tok_state.get('lowercase'      ,					SCREAMING_SNAKE_CASE_     ) != do_lower_case
             or pre_tok_state.get('strip_accents'      ,					SCREAMING_SNAKE_CASE_     ) != strip_accents
         ):
              __lowerCamelCase       :     List[Any]        =		getattr(SCREAMING_SNAKE_CASE_      ,					pre_tok_state.pop('type'     )     )
              __lowerCamelCase       :     List[str]        =		do_lower_case
              __lowerCamelCase       :     str        =		strip_accents
              __lowerCamelCase       :     Union[str, Any]        =		pre_tok_class(**SCREAMING_SNAKE_CASE_     )

         __lowerCamelCase       :     Tuple        =		do_lower_case







    @property
    def 				lowercase_    (							self     )    ->					str:
         if self._mask_token is None:
              if self.verbose:
                   logger.error('Using mask_token, but it is not set yet.'     )
              return None
         return str(self._mask_token     )







    @mask_token.setter
    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_     )    ->					Union[str, Any]:
         __lowerCamelCase       :     Any        =		AddedToken(SCREAMING_SNAKE_CASE_      ,					lstrip=SCREAMING_SNAKE_CASE_      ,					rstrip=SCREAMING_SNAKE_CASE_     ) if isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ) else value
         __lowerCamelCase       :     int        =		value







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_=None     )    ->					Union[str, Any]:
         __lowerCamelCase       :     Dict        =		[self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
              return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = None     )    ->					List[int]:
         __lowerCamelCase       :     List[Any]        =		[self.sep_token_id]
         __lowerCamelCase       :     List[Any]        =		[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 + sep + token_ids_a + sep     ) * [0]







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = None     )    ->					Tuple[str]:
         __lowerCamelCase       :     Optional[Any]        =		self._tokenizer.model.save(SCREAMING_SNAKE_CASE_      ,					name=SCREAMING_SNAKE_CASE_     )
         return tuple(SCREAMING_SNAKE_CASE_     )






