
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

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


A     :	int    = logging.get_logger(__name__)

A     :	Union[str, Any]    = '''▁'''

A     :	List[str]    = {'''vocab_file''': '''prophetnet.tokenizer'''}

A     :	List[str]    = {
    '''vocab_file''': {
        '''microsoft/xprophetnet-large-wiki100-cased''': (
            '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
        ),
    }
}

A     :	Dict    = {
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

A     :	str    = {
    '''microsoft/xprophetnet-large-wiki100-cased''': 5_1_2,
}


def     __lowerCAmelCase			(			a__  )       ->  Tuple:
				__a										=    collections.OrderedDict()
				with open(_UpperCAmelCase						,    '''r'''						,    encoding='''utf-8'''  ) as reader:
								__a										=    reader.readlines()
				for index, token in enumerate(_UpperCAmelCase  ):
								__a										=    token.rstrip('''\n'''  )
								__a										=    index
				return vocab







class 			__A( __UpperCAmelCase			):
				snake_case_						   =    VOCAB_FILES_NAMES
				snake_case_						   =    PRETRAINED_VOCAB_FILES_MAP
				snake_case_						   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				snake_case_						   =    ['''input_ids''', '''attention_mask''']




				def __init__(     self		,     _snake_case		,     _snake_case="[SEP]"		,     _snake_case="[SEP]"		,     _snake_case="[SEP]"		,     _snake_case="[UNK]"		,     _snake_case="[PAD]"		,     _snake_case="[CLS]"		,     _snake_case="[MASK]"		,     _snake_case = None		,     **_snake_case		,     )    ->      Tuple:


								'''simple docstring'''




								__a										=    {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=__SCREAMING_SNAKE_CASE		,     eos_token=__SCREAMING_SNAKE_CASE		,     sep_token=__SCREAMING_SNAKE_CASE		,     unk_token=__SCREAMING_SNAKE_CASE		,     pad_token=__SCREAMING_SNAKE_CASE		,     cls_token=__SCREAMING_SNAKE_CASE		,     mask_token=__SCREAMING_SNAKE_CASE		,     sp_model_kwargs=self.sp_model_kwargs		,     **__SCREAMING_SNAKE_CASE		,     )

								try:
												import sentencepiece as spm
								except ImportError:
												logger.warning(
												    '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
												    ''' pip install sentencepiece'''				)
												raise

								__a										=    spm.SentencePieceProcessor(**self.sp_model_kwargs				)
								self.sp_model.Load(str(__SCREAMING_SNAKE_CASE				)				)
								__a										=    vocab_file

								# Original fairseq vocab and spm vocab must be "aligned":
								# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
								# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
								# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
								# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

								# put special tokens and [unused] tokens into the vocab
								__a										=    {'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

								for i in range(10				):
												__a										=    F"""[unused{i}]"""
												__a										=    5 + i

								# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
								__a										=    12
								__a										=    {v: k for k, v in self.fairseq_tokens_to_ids.items()}
								for k in self.fairseq_tokens_to_ids.keys():
												self.unique_no_split_tokens.append(__SCREAMING_SNAKE_CASE				)




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


								'''simple docstring'''




								__a										=    self.__dict__.copy()
								__a										=    None
								return state




				def __setstate__(     self		,     _snake_case				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    d
								try:
												import sentencepiece as spm
								except ImportError:
												logger.warning(
												    '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
												    ''' pip install sentencepiece'''				)
												raise

								# for backward compatibility
								if not hasattr(self		,     '''sp_model_kwargs'''				):
												__a										=    {}

								__a										=    spm.SentencePieceProcessor(**self.sp_model_kwargs				)
								self.sp_model.Load(self.vocab_file				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None		,     _snake_case = False				)    ->      List[Any]:


								'''simple docstring'''




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

								if token_ids_a is None:
												return ([0] * len(__SCREAMING_SNAKE_CASE				)) + [1]
								return ([0] * len(__SCREAMING_SNAKE_CASE				)) + [1] + ([0] * len(__SCREAMING_SNAKE_CASE				)) + [1]




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    [self.sep_token_id]

								if token_ids_a is None:
												return len(token_ids_a + sep				) * [0]
								return len(token_ids_a + sep + sep + token_ids_a + sep				) * [0]




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								return len(self.sp_model				) + self.fairseq_offset




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE				): i for i in range(self.vocab_size				)}
								vocab.update(self.added_tokens_encoder				)
								return vocab




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Any:


								'''simple docstring'''




								return self.sp_model.encode(__SCREAMING_SNAKE_CASE		,     out_type=__SCREAMING_SNAKE_CASE				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      List[str]:


								'''simple docstring'''




								if token in self.fairseq_tokens_to_ids:
												return self.fairseq_tokens_to_ids[token]
								__a										=    self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE				)

								# Need to return unknown token if the SP model returned 0
								return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      int:


								'''simple docstring'''




								if index in self.fairseq_ids_to_tokens:
												return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    ''''''.join(__SCREAMING_SNAKE_CASE				).replace(__SCREAMING_SNAKE_CASE		,     ''' '''				).strip()
								return out_string




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None				)    ->      Any:


								'''simple docstring'''




								if not os.path.isdir(__SCREAMING_SNAKE_CASE				):
												logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""				)
												return
								__a										=    os.path.join(
								    __SCREAMING_SNAKE_CASE		,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

								if os.path.abspath(self.vocab_file				) != os.path.abspath(__SCREAMING_SNAKE_CASE				) and os.path.isfile(self.vocab_file				):
												copyfile(self.vocab_file		,     __SCREAMING_SNAKE_CASE				)
								elif not os.path.isfile(self.vocab_file				):
												with open(__SCREAMING_SNAKE_CASE		,     '''wb'''				) as fi:
																__a										=    self.sp_model.serialized_model_proto()
																fi.write(__SCREAMING_SNAKE_CASE				)

								return (out_vocab_file,)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None				)    ->      List[Any]:


								'''simple docstring'''




								if token_ids_a is None:
												return token_ids_a + [self.sep_token_id]
								__a										=    [self.sep_token_id]
								return token_ids_a + sep + token_ids_a + sep




from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests





def      __snake_case	(			_UpperCAmelCase = "isbn/0140328726"				):
    __a						       =				olid.strip().strip('''/'''				)  # Remove leading/trailing whitespace & slashes
    if new_olid.count('''/'''				) != 1:
        __a						       =				f'{olid} is not a valid Open Library olid'
        raise ValueError(_UpperCAmelCase				)
    return requests.get(f'https://openlibrary.org/{new_olid}.json'				).json()





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				{
        '''title''': '''Title''',
        '''publish_date''': '''Publish date''',
        '''authors''': '''Authors''',
        '''number_of_pages''': '''Number of pages:''',
        '''first_sentence''': '''First sentence''',
        '''isbn_10''': '''ISBN (10)''',
        '''isbn_13''': '''ISBN (13)''',
    }
    __a						       =				{better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
    __a						       =				[
        get_openlibrary_data(author['''key''']				)['''name'''] for author in data['''Authors''']
    ]
    __a						       =				data['''First sentence''']['''value''']
    for key, value in data.items():
        if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
            __a						       =				''', '''.join(_UpperCAmelCase				)
    return data


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 while True:
  __snake_case						:List[Any]      		=     input('''\nEnter the ISBN code to search (or \'quit\' to stop): ''').strip()
  if isbn.lower() in ("", "q", "quit", "exit", "stop"):
   break

  if len(isbn) not in (10, 13) or not isbn.isdigit():
   print(f'Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.')
   continue

  print(f'\nSearching Open Library for ISBN: {isbn}...\n')

  try:
   __snake_case						:Optional[Any]      		=     summarize_book(get_openlibrary_data(f'isbn/{isbn}'))
   print('''\n'''.join(f'{key}: {value}' for key, value in book_summary.items()))
  except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
   print(f'Sorry, there are no results for ISBN: {isbn}.')





