




"""simple docstring"""


import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_A													=      logging.get_logger(__name__)

_A													=      {"""vocab_file""": """vocab.json"""}

_A													=      {
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

_A													=      {"""mgp-str""": 2_7}


class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Dict     				=     VOCAB_FILES_NAMES
   _lowerCamelCase    :Union[str, Any]     				=     PRETRAINED_VOCAB_FILES_MAP
   _lowerCamelCase    :Optional[int]     				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






   def __init__(		self       :		Optional[int]   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		Union[str, Any]="[GO]"   ,     UpperCamelCase       :		Any="[GO]"   ,     UpperCamelCase       :		Tuple="[s]"   ,     UpperCamelCase       :		List[Any]="[GO]"   ,     **UpperCamelCase       :		Dict   )			->						Union[str, Any]:

         """simple docstring"""
         super().__init__(
             unk_token=UpperCamelCase   ,     bos_token=UpperCamelCase   ,     eos_token=UpperCamelCase   ,     pad_token=UpperCamelCase   ,     **UpperCamelCase   ,     )

         with open(UpperCamelCase   ,     encoding="""utf-8"""   ) as vocab_handle:
               lowerCAmelCase__      :			Optional[Any]   =	json.load(UpperCamelCase   )
         lowerCAmelCase__      :			Union[str, Any]   =	{v: k for k, v in self.vocab.items()}






   @property
   def 	_lowerCAmelCase   (		self       :		int   )			->						Dict:

         """simple docstring"""
         return len(self.vocab   )






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Any:

         """simple docstring"""
         return dict(self.vocab   ,     **self.added_tokens_encoder   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   ,     UpperCamelCase       :		Tuple   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	[]
         for s in text:
               char_tokens.extend(UpperCamelCase   )
         return char_tokens






   def 	_lowerCAmelCase   (		self       :		List[Any]   ,     UpperCamelCase       :		str   )			->						Tuple:

         """simple docstring"""
         return self.vocab.get(UpperCamelCase   ,     self.vocab.get(self.unk_token   )   )






   def 	_lowerCAmelCase   (		self       :		List[str]   ,     UpperCamelCase       :		int   )			->						Any:

         """simple docstring"""
         return self.decoder.get(UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Dict   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		Optional[str] = None   )			->						Tuple[str]:

         """simple docstring"""
         if not os.path.isdir(UpperCamelCase   ):
               logger.error("""Vocabulary path ({}) should be a directory""".format(UpperCamelCase   )   )
               return
         lowerCAmelCase__      :			Union[str, Any]   =	os.path.join(
             UpperCamelCase   ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

         with open(UpperCamelCase   ,     """w"""   ,     encoding="""utf-8"""   ) as f:
               f.write(json.dumps(self.vocab   ,     indent=2   ,     sort_keys=UpperCamelCase   ,     ensure_ascii=UpperCamelCase   ) + """\n"""   )

         return (vocab_file,)











"""simple docstring"""


import os
from collections import deque

import torch
from torch.utils.data import Dataset


class _lowerCamelCase    (					a_						):






   def __init__(		self       :		Tuple   ,     UpperCamelCase       :		List[Any]=""   ,     UpperCamelCase       :		List[str]="train"   )			->						List[Any]:

         """simple docstring"""
         assert os.path.isdir(UpperCamelCase   )

         lowerCAmelCase__      :			Union[str, Any]   =	[]
         lowerCAmelCase__      :			List[Any]   =	os.listdir(UpperCamelCase   )
         for story_filename in story_filenames_list:
               if "summary" in story_filename:
                     continue
               lowerCAmelCase__      :			Any   =	os.path.join(UpperCamelCase   ,     UpperCamelCase   )
               if not os.path.isfile(UpperCamelCase   ):
                     continue
               self.documents.append(UpperCamelCase   )






   def __len__(		self       :		List[Any]   )			->						int:

         """simple docstring"""
         return len(self.documents   )






   def __getitem__(		self       :		str   ,     UpperCamelCase       :		Union[str, Any]   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	self.documents[idx]
         lowerCAmelCase__      :			List[Any]   =	document_path.split("""/"""   )[-1]
         with open(UpperCamelCase   ,     encoding="""utf-8"""   ) as source:
               lowerCAmelCase__      :			List[str]   =	source.read()
               lowerCAmelCase__ ,   lowerCAmelCase__      :			Union[str, Any]   =	process_story(UpperCamelCase   )
         return document_name, story_lines, summary_lines






def 	lowercase_				(			__UpperCAmelCase				)						->    Union[str, Any]:
      lowerCAmelCase__      :			Tuple   =	list(filter(lambda __UpperCAmelCase				: len(__UpperCAmelCase				) != 0    ,		[line.strip() for line in raw_story.split("""\n"""				)]				)				)

      # for some unknown reason some lines miss a period, add it
      lowerCAmelCase__      :			List[str]   =	[_add_missing_period(__UpperCAmelCase				) for line in nonempty_lines]

      # gather article lines
      lowerCAmelCase__      :			Optional[int]   =	[]
      lowerCAmelCase__      :			Optional[int]   =	deque(__UpperCAmelCase				)
      while True:
            try:
                  lowerCAmelCase__      :			List[Any]   =	lines.popleft()
                  if element.startswith("""@highlight"""				):
                        break
                  story_lines.append(__UpperCAmelCase				)
            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
      lowerCAmelCase__      :			Any   =	list(filter(lambda __UpperCAmelCase				: not t.startswith("""@highlight"""				)    ,		__UpperCAmelCase				)				)

      return story_lines, summary_lines



def 	lowercase_				(			__UpperCAmelCase				)						->    Optional[int]:
      lowerCAmelCase__      :			List[str]   =	[""".""", """!""", """?""", """...""", """'""", """`""", """\"""", """\u2019""", """\u2019""", """)"""]
      if line.startswith("""@highlight"""				):
            return line
      if line[-1] in END_TOKENS:
            return line
      return line + "."



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[Any]:
      if len(__UpperCAmelCase				) > block_size:
            return sequence[:block_size]
      else:
            sequence.extend([pad_token_id] * (block_size - len(__UpperCAmelCase				))				)
            return sequence



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Tuple:
      lowerCAmelCase__      :			List[str]   =	torch.ones_like(__UpperCAmelCase				)
      lowerCAmelCase__      :			List[str]   =	sequence == pad_token_id
      lowerCAmelCase__      :			str   =	0
      return mask



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[Any]:
      lowerCAmelCase__      :			Tuple   =	[tokenizer.encode(__UpperCAmelCase				) for line in story_lines]
      lowerCAmelCase__      :			List[str]   =	[token for sentence in story_lines_token_ids for token in sentence]
      lowerCAmelCase__      :			int   =	[tokenizer.encode(__UpperCAmelCase				) for line in summary_lines]
      lowerCAmelCase__      :			Union[str, Any]   =	[token for sentence in summary_lines_token_ids for token in sentence]

      return story_token_ids, summary_token_ids



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[str]:
      lowerCAmelCase__      :			Tuple   =	[]
      for sequence in batch:
            lowerCAmelCase__      :			Union[str, Any]   =	-1
            lowerCAmelCase__      :			List[str]   =	[]
            for s in sequence:
                  if s == separator_token_id:
                        sentence_num += 1
                  embeddings.append(sentence_num % 2				)
            batch_embeddings.append(__UpperCAmelCase				)
      return torch.tensor(__UpperCAmelCase				)





