"""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


__UpperCamelCase										=     [nn.LayerNorm]

__UpperCamelCase										=     logging.get_logger(__name__)

__UpperCamelCase										=     version.parse(version.parse(torch.__version__).base_version)

__UpperCamelCase										=     parsed_torch_version_base >= version.parse('''2.0''')
__UpperCamelCase										=     parsed_torch_version_base >= version.parse('''1.12''')
__UpperCamelCase										=     parsed_torch_version_base >= version.parse('''1.11''')
__UpperCamelCase										=     parsed_torch_version_base < version.parse('''1.11''')







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase		,   self   )							-> Union[str, Any]:
     from torch import _softmax_backward_data

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







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase = 0   )							-> Any:
     snake_case_   			=   index.to(layer.weight.device   )
     snake_case_   			=   layer.weight.index_select(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_   ).clone().detach()
     if layer.bias is not None:
          if dim == 1:
               snake_case_   			=   layer.bias.clone().detach()
          else:
               snake_case_   			=   layer.bias[index].clone().detach()
     snake_case_   			=   list(layer.weight.size()   )
     snake_case_   			=   len(SCREAMING_SNAKE_CASE_   )
     snake_case_   			=   nn.Linear(new_size[1]		,   new_size[0]		,   bias=layer.bias is not None   ).to(layer.weight.device   )
     snake_case_   			=   False
     new_layer.weight.copy_(W.contiguous()   )
     snake_case_   			=   True
     if layer.bias is not None:
          snake_case_   			=   False
          new_layer.bias.copy_(b.contiguous()   )
          snake_case_   			=   True
     return new_layer




class 					UpperCamelCase   (							nn.Module  ):







       def __init__(					self,				lowerCAmelCase__,				lowerCAmelCase__) -> List[Any]:
            super().__init__()
            snake_case_   			=   nf
            snake_case_   			=   nn.Parameter(torch.empty(lowercase__,				lowercase__))
            snake_case_   			=   nn.Parameter(torch.zeros(lowercase__))
            nn.init.normal_(self.weight,				std=0.02)







       def 						a_		(					self,				lowerCAmelCase__) -> str:
            snake_case_   			=   x.size()[:-1] + (self.nf,)
            snake_case_   			=   torch.addmm(self.bias,				x.view(-1,				x.size(-1)),				self.weight)
            snake_case_   			=   x.view(lowercase__)
            return x







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase = 1   )							-> List[Any]:
     snake_case_   			=   index.to(layer.weight.device   )
     snake_case_   			=   layer.weight.index_select(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_   ).clone().detach()
     if dim == 0:
          snake_case_   			=   layer.bias.clone().detach()
     else:
          snake_case_   			=   layer.bias[index].clone().detach()
     snake_case_   			=   list(layer.weight.size()   )
     snake_case_   			=   len(SCREAMING_SNAKE_CASE_   )
     snake_case_   			=   ConvaD(new_size[1]		,   new_size[0]   ).to(layer.weight.device   )
     snake_case_   			=   False
     new_layer.weight.copy_(W.contiguous()   )
     snake_case_   			=   True
     snake_case_   			=   False
     new_layer.bias.copy_(b.contiguous()   )
     snake_case_   			=   True
     return new_layer







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase = None   )							-> Any:
     if isinstance(SCREAMING_SNAKE_CASE_		,   nn.Linear   ):
          return prune_linear_layer(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   dim=0 if dim is None else dim   )
     elif isinstance(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_   ):
          return prune_convad_layer(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   dim=1 if dim is None else dim   )
     else:
          raise ValueError(f'Can\'t prune layer of class {layer.__class__}'   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase		,   *UpperCAmelCase   )							-> str:
     assert len(SCREAMING_SNAKE_CASE_   ) > 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
     snake_case_   			=   len(inspect.signature(SCREAMING_SNAKE_CASE_   ).parameters   )
     if num_args_in_forward_chunk_fn != len(SCREAMING_SNAKE_CASE_   ):
          raise ValueError(
              f'forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(SCREAMING_SNAKE_CASE_   )} input '
              'tensors are given'   )

     if chunk_size > 0:
          snake_case_   			=   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}'   )

          snake_case_   			=   input_tensors[0].shape[chunk_dim] // chunk_size

          # chunk input tensor into tuples
          snake_case_   			=   tuple(input_tensor.chunk(SCREAMING_SNAKE_CASE_		,   dim=SCREAMING_SNAKE_CASE_   ) for input_tensor in input_tensors   )
          # apply forward fn to every tuple
          snake_case_   			=   tuple(forward_fn(*SCREAMING_SNAKE_CASE_   ) for input_tensors_chunk in zip(*SCREAMING_SNAKE_CASE_   )   )
          # concatenate output at same dimension
          return torch.cat(SCREAMING_SNAKE_CASE_		,   dim=SCREAMING_SNAKE_CASE_   )

     return forward_fn(*SCREAMING_SNAKE_CASE_   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase   )							-> Optional[Any]:
     snake_case_   			=   torch.ones(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_   )
     snake_case_   			=   set(SCREAMING_SNAKE_CASE_   ) - 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
          snake_case_   			=   head - sum(1 if h < head else 0 for h in already_pruned_heads   )
          snake_case_   			=   0
     snake_case_   			=   mask.view(-1   ).contiguous().eq(1   )
     snake_case_   			=   torch.arange(len(SCREAMING_SNAKE_CASE_   )   )[mask].long()
     return heads, index







def    UpperCAmelCase       (						*UpperCAmelCase		,   UpperCAmelCase = None   )							-> str:
     return torch.meshgrid(*SCREAMING_SNAKE_CASE_		,   indexing=SCREAMING_SNAKE_CASE_   )







def    UpperCAmelCase       (						UpperCAmelCase   )							-> Optional[Any]:
     return tensor.device, storage_ptr(SCREAMING_SNAKE_CASE_   ), storage_size(SCREAMING_SNAKE_CASE_   )



"""simple docstring"""




from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


snake_case					  =						logging.get_logger(__name__)




class 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				UpperCAmelCase_							: Dict		    =							["input_ids", "attention_mask"]


				def __init__(							self						,      lowercase__="[PAD]"						,      lowercase__="[BOS]"						,      lowercase__="[EOS]"						,      lowercase__="[MASK]"						,      lowercase__="[CLS]"						,      lowercase__="[SEP]"						,      lowercase__=2048						,      **lowercase__						,      )       ->       None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else pad_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else bos_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else eos_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else mask_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else cls_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else sep_token

									super().__init__(
									    pad_token=lowercase__						,      bos_token=lowercase__						,      eos_token=lowercase__						,      mask_token=lowercase__						,      cls_token=lowercase__						,      sep_token=lowercase__						,      model_max_length=lowercase__						,      **lowercase__						,      )

									SCREAMING_SNAKE_CASE           =      2**8  # utf is 8 bits

									# define special tokens dict
									SCREAMING_SNAKE_CASE           =      {
									    self.pad_token: 0,
									    self.bos_token: 1,
									    self.eos_token: 2,
									    self.mask_token: 3,
									    self.cls_token: 4,
									    self.sep_token: 5,
									}
									SCREAMING_SNAKE_CASE           =      len(self.special_tokens_encoder     )
									SCREAMING_SNAKE_CASE           =      {v: k for k, v in self.special_tokens_encoder.items()}


				def 				A   (							self     )       ->       Dict[str, int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.special_tokens_encoder.copy()
									vocab.update(self.added_tokens_encoder     )
									for i in range(self._utf_vocab_size     ):
														SCREAMING_SNAKE_CASE           =      chr(lowercase__     )
														SCREAMING_SNAKE_CASE           =      i + len(self.special_tokens_encoder     )
									return vocab


				@property
				def 				A   (							self     )       ->       List[Any]:
									"""simple docstring"""







									return self._utf_vocab_size + self._num_special_tokens


				def 				A   (							self						,      lowercase__						,      lowercase__ = None						,      lowercase__ = False     )       ->       List[int]:
									"""simple docstring"""







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

									# normal case: some special tokens
									if token_ids_a is None:
														return [1] + [0] * len(lowercase__     ) + [1]
									return [1] + ([0] * len(lowercase__     )) + [1] + ([0] * len(lowercase__     )) + [1]


				def 				A   (							self						,      lowercase__						,      lowercase__ = None     )       ->       List[int]:
									"""simple docstring"""







									if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									else:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]


				def 				A   (							self						,      lowercase__     )       ->       List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      [chr(lowercase__     ) for i in text.encode('utf-8'     )]
									return tokens


				def 				A   (							self						,      lowercase__     )       ->       str:
									"""simple docstring"""







									if token in self.special_tokens_encoder:
														SCREAMING_SNAKE_CASE           =      self.special_tokens_encoder[token]
									elif token in self.added_tokens_encoder:
														SCREAMING_SNAKE_CASE           =      self.added_tokens_encoder[token]
									elif len(lowercase__     ) != 1:
														SCREAMING_SNAKE_CASE           =      self.unk_token_id
									else:
														SCREAMING_SNAKE_CASE           =      ord(lowercase__     ) + self._num_special_tokens
									return token_id


				def 				A   (							self						,      lowercase__     )       ->       int:
									"""simple docstring"""







									if index in self.special_tokens_decoder:
														SCREAMING_SNAKE_CASE           =      self.special_tokens_decoder[index]
									elif index in self.added_tokens_decoder:
														SCREAMING_SNAKE_CASE           =      self.added_tokens_decoder[index]
									else:
														SCREAMING_SNAKE_CASE           =      chr(index - self._num_special_tokens     )
									return token


				def 				A   (							self						,      lowercase__     )       ->       Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      b''
									for token in tokens:
														if token in self.special_tokens_decoder:
																			SCREAMING_SNAKE_CASE           =      self.special_tokens_decoder[token].encode('utf-8'     )
														elif token in self.added_tokens_decoder:
																			SCREAMING_SNAKE_CASE           =      self.special_tokens_decoder[token].encode('utf-8'     )
														elif token in self.special_tokens_encoder:
																			SCREAMING_SNAKE_CASE           =      token.encode('utf-8'     )
														elif token in self.added_tokens_encoder:
																			SCREAMING_SNAKE_CASE           =      token.encode('utf-8'     )
														else:
																			SCREAMING_SNAKE_CASE           =      bytes([ord(lowercase__     )]     )
														bstring += tok_string
									SCREAMING_SNAKE_CASE           =      bstring.decode('utf-8'						,      errors='replace'     )
									return string




				def 				A   (							self						,      lowercase__						,      lowercase__ = None     )       ->       Tuple[str]:
									"""simple docstring"""







									return ()
