


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


__lowerCamelCase   	=  logging.get_logger(__name__)

__lowerCamelCase   	=  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCamelCase   	=  {
    '''vocab_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''allenai/longformer-base-4096''': (
            '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'''
        ),
    },
}

__lowerCamelCase   	=  {
    '''allenai/longformer-base-4096''': 4096,
    '''allenai/longformer-large-4096''': 4096,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 4096,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 4096,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 4096,
}


class 	a__						(    lowerCAmelCase_     ):
       lowerCamelCase__:  List[Any]				     =  VOCAB_FILES_NAMES
       lowerCamelCase__:  Any				     =  PRETRAINED_VOCAB_FILES_MAP
       lowerCamelCase__:  List[Any]				     =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCamelCase__:  List[Any]				     =  ["""input_ids""", """attention_mask"""]
       lowerCamelCase__:  int				     =  LongformerTokenizer

       def __init__( self   :  Union[str, Any]   ,				lowerCamelCase_   :  str=None   ,				lowerCamelCase_   :  List[Any]=None   ,				lowerCamelCase_   :  Any=None   ,				lowerCamelCase_   :  Any="replace"   ,				lowerCamelCase_   :  str="<s>"   ,				lowerCamelCase_   :  Dict="</s>"   ,				lowerCamelCase_   :  Optional[int]="</s>"   ,				lowerCamelCase_   :  Optional[int]="<s>"   ,				lowerCamelCase_   :  Dict="<unk>"   ,				lowerCamelCase_   :  Any="<pad>"   ,				lowerCamelCase_   :  List[str]="<mask>"   ,				lowerCamelCase_   :  Dict=False   ,				lowerCamelCase_   :  Optional[int]=True   ,				**lowerCamelCase_   :  Optional[Any]   ,				):
            super().__init__(
                lowerCamelCase_   ,				lowerCamelCase_   ,				tokenizer_file=lowerCamelCase_   ,				errors=lowerCamelCase_   ,				bos_token=lowerCamelCase_   ,				eos_token=lowerCamelCase_   ,				sep_token=lowerCamelCase_   ,				cls_token=lowerCamelCase_   ,				unk_token=lowerCamelCase_   ,				pad_token=lowerCamelCase_   ,				mask_token=lowerCamelCase_   ,				add_prefix_space=lowerCamelCase_   ,				trim_offsets=lowerCamelCase_   ,				**lowerCamelCase_   ,				)

            a_     :  Optional[Any]				      =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
            if pre_tok_state.get("""add_prefix_space"""   ,				lowerCamelCase_     ) != add_prefix_space:
                 a_     :  int				      =     getattr(lowerCamelCase_   ,				pre_tok_state.pop("""type"""     )     )
                 a_     :  str				      =     add_prefix_space
                 a_     :  str				      =     pre_tok_class(**lowerCamelCase_     )

            a_     :  Optional[Any]				      =     add_prefix_space

            a_     :  str				      =     """post_processor"""
            a_     :  str				      =     getattr(self.backend_tokenizer   ,				lowerCamelCase_   ,				lowerCamelCase_     )
            if tokenizer_component_instance:
                 a_     :  Any				      =     json.loads(tokenizer_component_instance.__getstate__()     )

                 # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                 if "sep" in state:
                      a_     :  Optional[Any]				      =     tuple(state["""sep"""]     )
                 if "cls" in state:
                      a_     :  str				      =     tuple(state["""cls"""]     )

                 a_     :  Optional[Any]				      =     False

                 if state.get("""add_prefix_space"""   ,				lowerCamelCase_     ) != add_prefix_space:
                      a_     :  List[Any]				      =     add_prefix_space
                      a_     :  Any				      =     True

                 if state.get("""trim_offsets"""   ,				lowerCamelCase_     ) != trim_offsets:
                      a_     :  int				      =     trim_offsets
                      a_     :  str				      =     True

                 if changes_to_apply:
                      a_     :  Any				      =     getattr(lowerCamelCase_   ,				state.pop("""type"""     )     )
                      a_     :  Union[str, Any]				      =     component_class(**lowerCamelCase_     )
                      setattr(self.backend_tokenizer   ,				lowerCamelCase_   ,				lowerCamelCase_     )

       @property
       def     UpperCAmelCase( self   :  Dict     ):
            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     UpperCAmelCase( self   :  Optional[int]   ,				lowerCamelCase_   :  int     ):
            a_     :  List[str]				      =     AddedToken(lowerCamelCase_   ,				lstrip=lowerCamelCase_   ,				rstrip=lowerCamelCase_     ) if isinstance(lowerCamelCase_   ,				lowerCamelCase_     ) else value
            a_     :  Optional[int]				      =     value

       def     UpperCAmelCase( self   :  str   ,				*lowerCamelCase_   :  int   ,				**lowerCamelCase_   :  List[Any]     ):
            a_     :  Any				      =     kwargs.get("""is_split_into_words"""   ,				lowerCamelCase_     )
            assert self.add_prefix_space or not is_split_into_words, (
                F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                "to use it with pretokenized inputs."
            )

            return super()._batch_encode_plus(*lowerCamelCase_   ,				**lowerCamelCase_     )

       def     UpperCAmelCase( self   :  Union[str, Any]   ,				*lowerCamelCase_   :  Dict   ,				**lowerCamelCase_   :  int     ):
            a_     :  List[str]				      =     kwargs.get("""is_split_into_words"""   ,				lowerCamelCase_     )

            assert self.add_prefix_space or not is_split_into_words, (
                F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                "to use it with pretokenized inputs."
            )

            return super()._encode_plus(*lowerCamelCase_   ,				**lowerCamelCase_     )

       def     UpperCAmelCase( self   :  int   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  Optional[str] = None     ):
            a_     :  List[Any]				      =     self._tokenizer.model.save(lowerCamelCase_   ,				name=lowerCamelCase_     )
            return tuple(lowerCamelCase_     )

       def     UpperCAmelCase( self   :  str   ,				lowerCamelCase_   :  Any   ,				lowerCamelCase_   :  Dict=None     ):
            a_     :  List[Any]				      =     [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     UpperCAmelCase( self   :  Optional[Any]   ,				lowerCamelCase_   :  List[int]   ,				lowerCamelCase_   :  Optional[List[int]] = None     ):
            a_     :  Any				      =     [self.sep_token_id]
            a_     :  Optional[int]				      =     [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]







'''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					:			Optional[Any]				     =					[nn.LayerNorm]

_UpperCAmelCase					:			Dict				     =					logging.get_logger(__name__)

_UpperCAmelCase					:			Tuple				     =					version.parse(version.parse(torch.__version__).base_version)

_UpperCAmelCase					:			Optional[int]				     =					parsed_torch_version_base >= version.parse("""2.0""")
_UpperCAmelCase					:			Optional[int]				     =					parsed_torch_version_base >= version.parse("""1.12""")
_UpperCAmelCase					:			List[str]				     =					parsed_torch_version_base >= version.parse("""1.11""")
_UpperCAmelCase					:			Optional[int]				     =					parsed_torch_version_base < version.parse("""1.11""")

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					self):
						from torch import _softmax_backward_data

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

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


class    a__					(			nn.Module   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase							,      __lowercase		):
										super().__init__()
										__lowerCAmelCase											=					nf
										__lowerCAmelCase											=					nn.Parameter(torch.empty(__lowercase							,      __lowercase		)		)
										__lowerCAmelCase											=					nn.Parameter(torch.zeros(__lowercase		)		)
										nn.init.normal_(self.weight							,      std=0.0_2		)




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					x.size()[:-1] + (self.nf,)
										__lowerCAmelCase											=					torch.addmm(self.bias							,      x.view(-1							,      x.size(-1		)		)							,      self.weight		)
										__lowerCAmelCase											=					x.view(__lowercase		)
										return x

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase = 1):
						__lowerCAmelCase											=					index.to(layer.weight.device)
						__lowerCAmelCase											=					layer.weight.index_select(lowerCamelCase,					lowerCamelCase).clone().detach()
						if dim == 0:
												__lowerCAmelCase											=					layer.bias.clone().detach()
						else:
												__lowerCAmelCase											=					layer.bias[index].clone().detach()
						__lowerCAmelCase											=					list(layer.weight.size())
						__lowerCAmelCase											=					len(lowerCamelCase)
						__lowerCAmelCase											=					ConvaD(new_size[1],					new_size[0]).to(layer.weight.device)
						__lowerCAmelCase											=					False
						new_layer.weight.copy_(W.contiguous())
						__lowerCAmelCase											=					True
						__lowerCAmelCase											=					False
						new_layer.bias.copy_(b.contiguous())
						__lowerCAmelCase											=					True
						return new_layer

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase = None):
						if isinstance(lowerCamelCase,					nn.Linear):
												return prune_linear_layer(lowerCamelCase,					lowerCamelCase,					dim=0 if dim is None else dim)
						elif isinstance(lowerCamelCase,					lowerCamelCase):
												return prune_convad_layer(lowerCamelCase,					lowerCamelCase,					dim=1 if dim is None else dim)
						else:
												raise ValueError(F"""Can't prune layer of class {layer.__class__}""")

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					*lowerCamelCase):
						assert len(lowerCamelCase) > 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											=					len(inspect.signature(lowerCamelCase).parameters)
						if num_args_in_forward_chunk_fn != len(lowerCamelCase):
												raise ValueError(
												    F"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(lowerCamelCase)} input """
												    '''tensors are given''')

						if chunk_size > 0:
												__lowerCAmelCase											=					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											=					input_tensors[0].shape[chunk_dim] // chunk_size

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

						return forward_fn(*lowerCamelCase)

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase):
						__lowerCAmelCase											=					torch.ones(lowerCamelCase,					lowerCamelCase)
						__lowerCAmelCase											=					set(lowerCamelCase) - 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											=					head - sum(1 if h < head else 0 for h in already_pruned_heads)
												__lowerCAmelCase											=					0
						__lowerCAmelCase											=					mask.view(-1).contiguous().eq(1)
						__lowerCAmelCase											=					torch.arange(len(lowerCamelCase))[mask].long()
						return heads, index

def 				__magic_name__(    *lowerCamelCase,					lowerCamelCase = None):
						return torch.meshgrid(*lowerCamelCase,					indexing=lowerCamelCase)




def 				__magic_name__(    lowerCamelCase):
						return tensor.device, storage_ptr(lowerCamelCase), storage_size(lowerCamelCase)

