




import os
from collections import deque

import torch
from torch.utils.data import Dataset







class __snake_case			(			a		):







   def __init__(      self		:						Optional[Any]					,						_snake_case		:						str=""					,						_snake_case		:						str="train"):

        """simple docstring"""


        assert os.path.isdir(_snake_case)

        UpperCAmelCase_          =							[]
        UpperCAmelCase_          =							os.listdir(_snake_case)
        for story_filename in story_filenames_list:
             if "summary" in story_filename:
                  continue
             UpperCAmelCase_          =							os.path.join(_snake_case					,						_snake_case)
             if not os.path.isfile(_snake_case):
                  continue
             self.documents.append(_snake_case)







   def __len__(      self		:						List[Any]):

        """simple docstring"""


        return len(self.documents)







   def __getitem__(      self		:						List[str]					,						_snake_case		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							self.documents[idx]
        UpperCAmelCase_          =							document_path.split('''/''')[-1]
        with open(_snake_case					,						encoding='''utf-8''') as source:
             UpperCAmelCase_          =							source.read()
             UpperCAmelCase_     ,       UpperCAmelCase_          =							process_story(_snake_case)
        return document_name, story_lines, summary_lines







def    A						(__A   :    Dict       )   ->      Optional[int]:







     """simple docstring"""


     UpperCAmelCase_          =							list(filter(lambda __A       : len(__A       ) != 0   ,				[line.strip() for line in raw_story.split('''\n'''       )]       )       )

     # for some unknown reason some lines miss a period, add it
     UpperCAmelCase_          =							[_add_missing_period(__A       ) for line in nonempty_lines]

     # gather article lines
     UpperCAmelCase_          =							[]
     UpperCAmelCase_          =							deque(__A       )
     while True:
          try:
               UpperCAmelCase_          =							lines.popleft()
               if element.startswith('''@highlight'''       ):
                    break
               story_lines.append(__A       )
          except IndexError:
               # if "@highlight" is absent from the file we pop
               # all elements until there is None, raising an exception.
               return story_lines, []

    # gather summary lines
     UpperCAmelCase_          =							list(filter(lambda __A       : not t.startswith('''@highlight'''       )   ,				__A       )       )

     return story_lines, summary_lines


def    A						(__A   :    Optional[int]       )   ->      Optional[int]:







     """simple docstring"""


     UpperCAmelCase_          =							['''.''', '''!''', '''?''', '''...''', '''\'''', '''`''', '''"''', '''\u2019''', '''\u2019''', ''')''']
     if line.startswith('''@highlight'''       ):
          return line
     if line[-1] in END_TOKENS:
          return line
     return line + "."


def    A						(__A   :    List[Any]   ,				__A   :    str   ,				__A   :    List[Any]       )   ->      List[str]:







     """simple docstring"""


     if len(__A       ) > block_size:
          return sequence[:block_size]
     else:
          sequence.extend([pad_token_id] * (block_size - len(__A       ))       )
          return sequence


def    A						(__A   :    str   ,				__A   :    Dict       )   ->      Any:







     """simple docstring"""


     UpperCAmelCase_          =							torch.ones_like(__A       )
     UpperCAmelCase_          =							sequence == pad_token_id
     UpperCAmelCase_          =							0
     return mask


def    A						(__A   :    int   ,				__A   :    Dict   ,				__A   :    List[Any]       )   ->      Optional[int]:







     """simple docstring"""


     UpperCAmelCase_          =							[tokenizer.encode(__A       ) for line in story_lines]
     UpperCAmelCase_          =							[token for sentence in story_lines_token_ids for token in sentence]
     UpperCAmelCase_          =							[tokenizer.encode(__A       ) for line in summary_lines]
     UpperCAmelCase_          =							[token for sentence in summary_lines_token_ids for token in sentence]

     return story_token_ids, summary_token_ids


def    A						(__A   :    int   ,				__A   :    Dict       )   ->      List[Any]:







     """simple docstring"""


     UpperCAmelCase_          =							[]
     for sequence in batch:
          UpperCAmelCase_          =							-1
          UpperCAmelCase_          =							[]
          for s in sequence:
               if s == separator_token_id:
                    sentence_num += 1
               embeddings.append(sentence_num % 2       )
          batch_embeddings.append(__A       )
     return torch.tensor(__A       )












from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_ : int    		=       logging.get_logger(__name__)

snake_case_ : str    		=       {}







class __snake_case			(			a		):
   UpperCAmelCase__  :    str          =  '''llama'''
   UpperCAmelCase__  :    Dict          =  ['''past_key_values''']







   def __init__(      self		:						str					,						_snake_case		:						List[str]=32000					,						_snake_case		:						int=4096					,						_snake_case		:						List[str]=11008					,						_snake_case		:						Optional[int]=32					,						_snake_case		:						List[Any]=32					,						_snake_case		:						Tuple=None					,						_snake_case		:						int="silu"					,						_snake_case		:						List[Any]=2048					,						_snake_case		:						List[str]=0.0_2					,						_snake_case		:						Any=1e-6					,						_snake_case		:						List[str]=True					,						_snake_case		:						Optional[Any]=0					,						_snake_case		:						Dict=1					,						_snake_case		:						List[Any]=2					,						_snake_case		:						str=1					,						_snake_case		:						Union[str, Any]=False					,						_snake_case		:						str=None					,						**_snake_case		:						List[Any]					,						):

        """simple docstring"""


        UpperCAmelCase_          =							vocab_size
        UpperCAmelCase_          =							max_position_embeddings
        UpperCAmelCase_          =							hidden_size
        UpperCAmelCase_          =							intermediate_size
        UpperCAmelCase_          =							num_hidden_layers
        UpperCAmelCase_          =							num_attention_heads

        # for backward compatibility
        if num_key_value_heads is None:
             UpperCAmelCase_          =							num_attention_heads

        UpperCAmelCase_          =							num_key_value_heads
        UpperCAmelCase_          =							hidden_act
        UpperCAmelCase_          =							initializer_range
        UpperCAmelCase_          =							rms_norm_eps
        UpperCAmelCase_          =							pretraining_tp
        UpperCAmelCase_          =							use_cache
        UpperCAmelCase_          =							rope_scaling
        self._rope_scaling_validation()

        super().__init__(
            pad_token_id=_snake_case					,						bos_token_id=_snake_case					,						eos_token_id=_snake_case					,						tie_word_embeddings=_snake_case					,						**_snake_case					,						)







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        if self.rope_scaling is None:
             return

        if not isinstance(self.rope_scaling					,						_snake_case) or len(self.rope_scaling) != 2:
             raise ValueError(
                 '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
                 F"""got {self.rope_scaling}""")
        UpperCAmelCase_          =							self.rope_scaling.get('''type'''					,						_snake_case)
        UpperCAmelCase_          =							self.rope_scaling.get('''factor'''					,						_snake_case)
        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
             raise ValueError(
                 F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}""")
        if rope_scaling_factor is None or not isinstance(_snake_case					,						_snake_case) or rope_scaling_factor <= 1.0:
             raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}""")






