



'''simple docstring'''

# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


a							   =				get_logger(__name__)






class 						a_		:
							UpperCAmelCase			:		Dict        =				'dummy_data'
							UpperCAmelCase			:		Dict        =				'datasets'
							UpperCAmelCase			:		str        =				False


							def __init__(				self   :						Union[str, Any]      ,						a_   :						Dict      ,						a_   :						Optional[int]      ,						a_   :						List[Any]      ,						a_   :						Optional[int] = None      ,						a_   :						Union[str, Any] = False      ,						a_   :						Union[str, Any] = True      ,						a_   :						Tuple = None      ,						)			->					int:
										snake_case:    Any      							=0
										snake_case:    Union[str, Any]      							=dataset_name
										snake_case:    Optional[int]      							=cache_dir
										snake_case:    Optional[int]      							=use_local_dummy_data
										snake_case:    List[Any]      							=config
										# download_callbacks take a single url as input
										snake_case:    Union[str, Any]      							=download_callbacks or []
										# if False, it doesn't load existing files and it returns the paths of the dummy files relative
										# to the dummy_data zip file root
										snake_case:    Any      							=load_existing_dummy_data

										# TODO(PVP, QL) might need to make this more general
										snake_case:    Optional[int]      							=str(a_			)
										# to be downloaded
										snake_case:    Tuple      							=None
										snake_case:    Union[str, Any]      							=None


							@property
							def    UpperCamelCase     (				self   :						Tuple			)			->					str:
										if self._dummy_file is None:
													snake_case:    Tuple      							=self.download_dummy_data()
										return self._dummy_file


							@property
							def    UpperCamelCase     (				self   :						List[Any]			)			->					Any:
										if self.config is not None:
													# structure is dummy / config_name / version_name
													return os.path.join('dummy'      ,						self.config.name      ,						self.version_name			)
										# structure is dummy / version_name
										return os.path.join('dummy'      ,						self.version_name			)


							@property
							def    UpperCamelCase     (				self   :						List[str]			)			->					Dict:
										return os.path.join(self.dummy_data_folder      ,						'dummy_data.zip'			)


							def    UpperCamelCase     (				self   :						str			)			->					List[Any]:
										snake_case:    Dict      							=(
										    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
										)

										snake_case:    Any      							=cached_path(
										    a_      ,						cache_dir=self.cache_dir      ,						extract_compressed_file=a_      ,						force_extract=a_			)

										return os.path.join(a_      ,						self.dummy_file_name			)


							@property
							def    UpperCamelCase     (				self   :						Dict			)			->					Tuple:
										return os.path.join(self.datasets_scripts_dir      ,						self.dataset_name      ,						self.dummy_zip_file			)


							@property
							def    UpperCamelCase     (				self   :						Any			)			->					Dict:
										if self._bucket_url is None:
													snake_case:    int      							=hf_github_url(self.dataset_name      ,						self.dummy_zip_file.replace(os.sep      ,						'/'			)			)
										return self._bucket_url


							@property
							def    UpperCamelCase     (				self   :						Dict			)			->					int:
										# return full path if its a dir
										if os.path.isdir(self.dummy_file			):
													return self.dummy_file
										# else cut off path to file -> example `xsum`.
										return "/".join(self.dummy_file.replace(os.sep      ,						'/'			).split('/'			)[:-1]			)


							def    UpperCamelCase     (				self   :						int      ,						a_   :						int      ,						*a_   :						Optional[Any]			)			->					Tuple:
										if self.load_existing_dummy_data:
													# dummy data is downloaded and tested
													snake_case:    str      							=self.dummy_file
										else:
													# dummy data cannot be downloaded and only the path to dummy file is returned
													snake_case:    Optional[Any]      							=self.dummy_file_name

										# special case when data_url is a dict
										if isinstance(a_      ,						a_			):
													return self.create_dummy_data_dict(a_      ,						a_			)
										elif isinstance(a_      ,						(list, tuple)			):
													return self.create_dummy_data_list(a_      ,						a_			)
										else:
													return self.create_dummy_data_single(a_      ,						a_			)


							def    UpperCamelCase     (				self   :						Optional[Any]      ,						a_   :						Tuple      ,						*a_   :						Dict			)			->					Optional[Any]:
										return self.download_and_extract(a_			)


							def    UpperCamelCase     (				self   :						str      ,						a_   :						Dict      ,						a_   :						Optional[int]			)			->					Union[str, Any]:
										return self.download_and_extract(a_			)


							def    UpperCamelCase     (				self   :						Any      ,						a_   :						Union[str, Any]      ,						*a_   :						Union[str, Any]      ,						**a_   :						List[str]			)			->					Dict:
										return path


							def    UpperCamelCase     (				self   :						Tuple			)			->					Any:
										return {}


							def    UpperCamelCase     (				self   :						Union[str, Any]      ,						a_   :						Dict      ,						a_   :						int			)			->					Any:
										snake_case:    Optional[Any]      							={}
										for key, single_urls in data_url.items():
													for download_callback in self.download_callbacks:
																if isinstance(a_      ,						a_			):
																			for single_url in single_urls:
																						download_callback(a_			)
																else:
																			snake_case:    Optional[int]      							=single_urls
																			download_callback(a_			)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
													if isinstance(a_      ,						a_			):
																snake_case:    Optional[int]      							=[os.path.join(a_      ,						urllib.parse.quote_plus(Path(a_			).name			)			) for x in single_urls]
													else:
																snake_case:    List[str]      							=single_urls
																snake_case:    List[Any]      							=os.path.join(a_      ,						urllib.parse.quote_plus(Path(a_			).name			)			)
													snake_case:    Dict      							=value

										# make sure that values are unique
										if all(isinstance(a_      ,						a_			) for i in dummy_data_dict.values()			) and len(set(dummy_data_dict.values()			)			) < len(
										    dummy_data_dict.values()			):
													# append key to value to make its name unique
													snake_case:    Optional[int]      							={key: value + key for key, value in dummy_data_dict.items()}

										return dummy_data_dict


							def    UpperCamelCase     (				self   :						str      ,						a_   :						int      ,						a_   :						Any			)			->					Optional[Any]:
										snake_case:    Any      							=[]
										# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
										snake_case:    str      							=all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}'      ,						a_			)			) for url in data_url			)
										snake_case:    Optional[int]      							=all(
										    url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'			) for url in data_url			)
										if data_url and (is_tf_records or is_pubmed_records):
													snake_case:    Dict      							=[data_url[0]] * len(a_			)
										for single_url in data_url:
													for download_callback in self.download_callbacks:
																download_callback(a_			)
													# we force the name of each key to be the last file / folder name of the url path
													# if the url has arguments, we need to encode them with urllib.parse.quote_plus
													snake_case:    str      							=os.path.join(a_      ,						urllib.parse.quote_plus(single_url.split('/'			)[-1]			)			)
													dummy_data_list.append(a_			)
										return dummy_data_list


							def    UpperCamelCase     (				self   :						Optional[Any]      ,						a_   :						Optional[Any]      ,						a_   :						List[str]			)			->					Optional[Any]:
										for download_callback in self.download_callbacks:
													download_callback(a_			)
										# we force the name of each key to be the last file / folder name of the url path
										# if the url has arguments, we need to encode them with urllib.parse.quote_plus
										snake_case:    str      							=os.path.join(a_      ,						urllib.parse.quote_plus(data_url.split('/'			)[-1]			)			)
										if os.path.exists(a_			) or not self.load_existing_dummy_data:
													return value
										else:
													# Backward compatibility, maybe deprecate at one point.
													# For many datasets with single url calls to dl_manager.download_and_extract,
													# the dummy_data.zip file is actually the zipped downloaded file
													# while now we expected the dummy_data.zip file to be a directory containing
													# the downloaded file.
													return path_to_dummy_data


							def    UpperCamelCase     (				self   :						int			)			->					Optional[int]:
										pass


							def    UpperCamelCase     (				self   :						str			)			->					List[Any]:
										pass


							def    UpperCamelCase     (				self   :						int      ,						a_   :						Optional[int]			)			->					List[Any]:
										def _iter_archive_members(a_   :						int			):
													# this preserves the order of the members inside the ZIP archive
													snake_case:    Optional[Any]      							=Path(self.dummy_file			).parent
													snake_case:    int      							=path.relative_to(a_			)
													with ZipFile(self.local_path_to_dummy_data			) as zip_file:
																snake_case:    List[str]      							=zip_file.namelist()
													for member in members:
																if member.startswith(relative_path.as_posix()			):
																			yield dummy_parent_path.joinpath(a_			)

										snake_case:    int      							=Path(a_			)
										snake_case:    Optional[int]      							=_iter_archive_members(a_			) if self.use_local_dummy_data else path.rglob('*'			)
										for file_path in file_paths:
													if file_path.is_file() and not file_path.name.startswith(('.', '__')			):
																yield file_path.relative_to(a_			).as_posix(), file_path.open('rb'			)




							def    UpperCamelCase     (				self   :						str      ,						a_   :						List[Any]			)			->					Dict:
										if not isinstance(a_      ,						a_			):
													snake_case:    List[str]      							=[paths]
										for path in paths:
													if os.path.isfile(a_			):
																if os.path.basename(a_			).startswith(('.', '__')			):
																			return
																yield path
													else:
																for dirpath, dirnames, filenames in os.walk(a_			):
																			if os.path.basename(a_			).startswith(('.', '__')			):
																						continue
																			dirnames.sort()
																			for filename in sorted(a_			):
																						if filename.startswith(('.', '__')			):
																									continue
																						yield os.path.join(a_      ,						a_			)






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_roberta import RobertaTokenizer


__magic_name__  							=	logging.get_logger(__name__)

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

__magic_name__  							=	{
    '''vocab_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/tokenizer.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/tokenizer.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json''',
        '''roberta-base-openai-detector''': (
            '''https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'''
        ),
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'''
        ),
    },
}

__magic_name__  							=	{
    '''roberta-base''': 512,
    '''roberta-large''': 512,
    '''roberta-large-mnli''': 512,
    '''distilroberta-base''': 512,
    '''roberta-base-openai-detector''': 512,
    '''roberta-large-openai-detector''': 512,
}




class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Dict     						=  VOCAB_FILES_NAMES
      _A    :		List[str]     						=  PRETRAINED_VOCAB_FILES_MAP
      _A    :		Any     						=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _A    :		str     						=  ['input_ids', 'attention_mask']
      _A    :		Dict     						=  RobertaTokenizer
      def __init__(      self		,						lowerCamelCase=None		,						lowerCamelCase=None		,						lowerCamelCase=None		,						lowerCamelCase="replace"		,						lowerCamelCase="<s>"		,						lowerCamelCase="</s>"		,						lowerCamelCase="</s>"		,						lowerCamelCase="<s>"		,						lowerCamelCase="<unk>"		,						lowerCamelCase="<pad>"		,						lowerCamelCase="<mask>"		,						lowerCamelCase=False		,						lowerCamelCase=True		,						**lowerCamelCase		,						):
          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		,						)

          snake_case__    =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
          if pre_tok_state.get("add_prefix_space"		,						lowerCamelCase			) != add_prefix_space:
              snake_case__    =					getattr(lowerCamelCase		,						pre_tok_state.pop("type"			)			)
              snake_case__    =					add_prefix_space
              snake_case__    =					pre_tok_class(**lowerCamelCase			)

          snake_case__    =					add_prefix_space

          snake_case__    =					"post_processor"
          snake_case__    =					getattr(self.backend_tokenizer		,						lowerCamelCase		,						lowerCamelCase			)
          if tokenizer_component_instance:
              snake_case__    =					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:
                  snake_case__    =					tuple(state["sep"]			)
              if "cls" in state:
                  snake_case__    =					tuple(state["cls"]			)

              snake_case__    =					False

              if state.get("add_prefix_space"		,						lowerCamelCase			) != add_prefix_space:
                  snake_case__    =					add_prefix_space
                  snake_case__    =					True

              if state.get("trim_offsets"		,						lowerCamelCase			) != trim_offsets:
                  snake_case__    =					trim_offsets
                  snake_case__    =					True

              if changes_to_apply:
                  snake_case__    =					getattr(lowerCamelCase		,						state.pop("type"			)			)
                  snake_case__    =					component_class(**lowerCamelCase			)
                  setattr(self.backend_tokenizer		,						lowerCamelCase		,						lowerCamelCase			)
      @property
      def A_      (      self			):
          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 A_      (      self		,						lowerCamelCase			):
          snake_case__    =					AddedToken(lowerCamelCase		,						lstrip=lowerCamelCase		,						rstrip=lowerCamelCase			) if isinstance(lowerCamelCase		,						lowerCamelCase			) else value
          snake_case__    =					value
      def A_      (      self		,						*lowerCamelCase		,						**lowerCamelCase			):
          snake_case__    =					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 A_      (      self		,						*lowerCamelCase		,						**lowerCamelCase			):
          snake_case__    =					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 A_      (      self		,						lowerCamelCase		,						lowerCamelCase = None			):
          snake_case__    =					self._tokenizer.model.save(lowerCamelCase		,						name=lowerCamelCase			)
          return tuple(lowerCamelCase			)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase=None			):
          snake_case__    =					[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 A_      (      self		,						lowerCamelCase		,						lowerCamelCase = None			):
          snake_case__    =					[self.sep_token_id]
          snake_case__    =					[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]



