
import os
from collections import deque

import torch
from torch.utils.data import Dataset







class   lowerCamelCase	(						A_						):





     def __init__(self   :					Optional[int]       ,					_A   :					str=""       ,					_A   :					List[str]="train"     )		->		Union[str, Any]:
         assert os.path.isdir(_A     )

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





     def __len__(self   :					Union[str, Any]     )		->		List[str]:
         return len(self.documents     )





     def __getitem__(self   :					Tuple       ,					_A   :					Dict     )		->		Optional[int]:
         snake_case				  =      self.documents[idx]
         snake_case				  =      document_path.split("/"     )[-1]
         with open(_A       ,					encoding="utf-8"     ) as source:
             snake_case				  =      source.read()
             snake_case  ,							snake_case				  =      process_story(_A     )
         return document_name, story_lines, summary_lines



def 		lowercase_    (     A__	)							->							List[Any]:
    """simple docstring"""

    snake_case				  =      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
    snake_case				  =      [_add_missing_period(A__	) for line in nonempty_lines]

    # gather article lines
    snake_case				  =      []
    snake_case				  =      deque(A__	)
    while True:
        try:
            snake_case				  =      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
    snake_case				  =      list(filter(lambda A__	: not t.startswith("@highlight"	)				,	A__	)	)

    return story_lines, summary_lines



def 		lowercase_    (     A__	)							->							List[str]:
    """simple docstring"""

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



def 		lowercase_    (     A__				,	A__				,	A__	)							->							List[Any]:
    """simple docstring"""

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



def 		lowercase_    (     A__				,	A__	)							->							List[str]:
    """simple docstring"""

    snake_case				  =      torch.ones_like(A__	)
    snake_case				  =      sequence == pad_token_id
    snake_case				  =      0
    return mask



def 		lowercase_    (     A__				,	A__				,	A__	)							->							Optional[int]:
    """simple docstring"""

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

    return story_token_ids, summary_token_ids





def 		lowercase_    (     A__				,	A__	)							->							Tuple:
    """simple docstring"""

    snake_case				  =      []
    for sequence in batch:
        snake_case				  =      -1
        snake_case				  =      []
        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__	)



import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class   lowerCamelCase	(						A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							str     =	PhobertTokenizer
     UpperCAmelCase__       :							List[Any]     =	False





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		List[str]:
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         snake_case				  =      ["T@@", "i", "I", "R@@", "r", "e@@"]
         snake_case				  =      dict(zip(_A       ,					range(len(_A     )     )     )     )
         snake_case				  =      ["#version: 0.2", "l à</w>"]
         snake_case				  =      {"unk_token": "<unk>"}

         snake_case				  =      os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES["vocab_file"]     )
         snake_case				  =      os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES["merges_file"]     )

         with open(self.vocab_file       ,					"w"       ,					encoding="utf-8"     ) as fp:
             for token in vocab_tokens:
                 fp.write(f'{token} {vocab_tokens[token]}\n'     )
         with open(self.merges_file       ,					"w"       ,					encoding="utf-8"     ) as fp:
             fp.write("\n".join(_A     )     )





     def 	UpperCAmelCase(self   :					Optional[int]       ,					**_A   :					Optional[int]     )		->		Union[str, Any]:
         kwargs.update(self.special_tokens_map     )
         return PhobertTokenizer.from_pretrained(self.tmpdirname       ,					**_A     )





     def 	UpperCAmelCase(self   :					Tuple       ,					_A   :					Optional[Any]     )		->		str:
         snake_case				  =      "Tôi là VinAI Research"
         snake_case				  =      "T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
         return input_text, output_text





     def 	UpperCAmelCase(self   :					Any     )		->		Union[str, Any]:
         snake_case				  =      PhobertTokenizer(self.vocab_file       ,					self.merges_file       ,					**self.special_tokens_map     )
         snake_case				  =      "Tôi là VinAI Research"
         snake_case				  =      "T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
         snake_case				  =      tokenizer.tokenize(_A     )
         print(_A     )
         self.assertListEqual(_A       ,					_A     )

         snake_case				  =      tokens + [tokenizer.unk_token]

         snake_case				  =      [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_A     )       ,					_A     )

