
# 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


UpperCAmelCase       =					get_logger(__name__)







class 			A_  :




	'''simple docstring'''


	_UpperCamelCase	:	Dict									=			"""dummy_data"""
	_UpperCamelCase	:	Optional[int]									=			"""datasets"""
	_UpperCamelCase	:	Tuple									=			False





	def __init__(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case = None						,       snake_case = False						,       snake_case = True						,       snake_case = None						,       ):
		lowercase	    =       0
		lowercase	    =       dataset_name
		lowercase	    =       cache_dir
		lowercase	    =       use_local_dummy_data
		lowercase	    =       config
		# download_callbacks take a single url as input
		lowercase	    =       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
		lowercase	    =       load_existing_dummy_data

		# TODO(PVP, QL) might need to make this more general
		lowercase	    =       str(snake_case			)
		# to be downloaded
		lowercase	    =       None
		lowercase	    =       None





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		if self._dummy_file is None:
			lowercase	    =       self.download_dummy_data()
		return self._dummy_file





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		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       SCREAMING_SNAKE_CASE__		(			self			):
		return os.path.join(self.dummy_data_folder						,       'dummy_data.zip'			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       (
		    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
		)

		lowercase	    =       cached_path(
		    snake_case						,       cache_dir=self.cache_dir						,       extract_compressed_file=snake_case						,       force_extract=snake_case			)

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





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		return os.path.join(self.datasets_scripts_dir						,       self.dataset_name						,       self.dummy_zip_file			)





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		if self._bucket_url is None:
			lowercase	    =       hf_github_url(self.dataset_name						,       self.dummy_zip_file.replace(os.sep						,       '/'			)			)
		return self._bucket_url





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		# 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       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       *snake_case			):
		if self.load_existing_dummy_data:
			# dummy data is downloaded and tested
			lowercase	    =       self.dummy_file
		else:
			# dummy data cannot be downloaded and only the path to dummy file is returned
			lowercase	    =       self.dummy_file_name

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





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       *snake_case			):
		return self.download_and_extract(snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case			):
		return self.download_and_extract(snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       *snake_case						,       **snake_case			):
		return path





	def       SCREAMING_SNAKE_CASE__		(			self			):
		return {}





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case			):
		lowercase	    =       {}
		for key, single_urls in data_url.items():
			for download_callback in self.download_callbacks:
				if isinstance(snake_case						,       snake_case			):
					for single_url in single_urls:
						download_callback(snake_case			)
				else:
					lowercase	    =       single_urls
					download_callback(snake_case			)
            # 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(snake_case						,       snake_case			):
				lowercase	    =       [os.path.join(snake_case						,       urllib.parse.quote_plus(Path(snake_case			).name			)			) for x in single_urls]
			else:
				lowercase	    =       single_urls
				lowercase	    =       os.path.join(snake_case						,       urllib.parse.quote_plus(Path(snake_case			).name			)			)
			lowercase	    =       value

		# make sure that values are unique
		if all(isinstance(snake_case						,       snake_case			) 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
			lowercase	    =       {key: value + key for key, value in dummy_data_dict.items()}

		return dummy_data_dict





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case			):
		lowercase	    =       []
		# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
		lowercase	    =       all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}'						,       snake_case			)			) for url in data_url			)
		lowercase	    =       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):
			lowercase	    =       [data_url[0]] * len(snake_case			)
		for single_url in data_url:
			for download_callback in self.download_callbacks:
				download_callback(snake_case			)
			# 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
			lowercase	    =       os.path.join(snake_case						,       urllib.parse.quote_plus(single_url.split('/'			)[-1]			)			)
			dummy_data_list.append(snake_case			)
		return dummy_data_list





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case			):
		for download_callback in self.download_callbacks:
			download_callback(snake_case			)
		# 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
		lowercase	    =       os.path.join(snake_case						,       urllib.parse.quote_plus(data_url.split('/'			)[-1]			)			)
		if os.path.exists(snake_case			) 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       SCREAMING_SNAKE_CASE__		(			self			):
		pass





	def       SCREAMING_SNAKE_CASE__		(			self			):
		pass





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		def _iter_archive_members(snake_case			):
			# this preserves the order of the members inside the ZIP archive
			lowercase	    =       Path(self.dummy_file			).parent
			lowercase	    =       path.relative_to(snake_case			)
			with ZipFile(self.local_path_to_dummy_data			) as zip_file:
				lowercase	    =       zip_file.namelist()
			for member in members:
				if member.startswith(relative_path.as_posix()			):
					yield dummy_parent_path.joinpath(snake_case			)

		lowercase	    =       Path(snake_case			)
		lowercase	    =       _iter_archive_members(snake_case			) 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(snake_case			).as_posix(), file_path.open('rb'			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		if not isinstance(snake_case						,       snake_case			):
			lowercase	    =       [paths]
		for path in paths:
			if os.path.isfile(snake_case			):
				if os.path.basename(snake_case			).startswith(('.', '__')			):
					return
				yield path
			else:
				for dirpath, dirnames, filenames in os.walk(snake_case			):
					if os.path.basename(snake_case			).startswith(('.', '__')			):
						continue
					dirnames.sort()
					for filename in sorted(snake_case			):
						if filename.startswith(('.', '__')			):
							continue
						yield os.path.join(snake_case						,       snake_case			)


'''simple docstring'''


import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
       import torch


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


__snake_case						  =		logging.get_logger(__name__)
__snake_case						  =		{"""vocab_file""": """spiece.model"""}

__snake_case						  =		{
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

__snake_case						  =		{
    """AI-Sweden/gpt-sw3-126m""": 2048,
    """AI-Sweden/gpt-sw3-350m""": 2048,
    """AI-Sweden/gpt-sw3-1.6b""": 2048,
    """AI-Sweden/gpt-sw3-6.7b""": 2048,
    """AI-Sweden/gpt-sw3-20b""": 2048,
}





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						VOCAB_FILES_NAMES
  A_    							=						PRETRAINED_VOCAB_FILES_MAP
  A_    							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A_    							=						['''input_ids''', '''attention_mask''']




  def __init__(		self      :       str   ,				lowercase_      :       List[str]   ,				lowercase_      :       List[str]=False   ,				lowercase_      :       List[str]=False   ,				lowercase_      :       Optional[int]=False   ,				lowercase_      :       Dict=None   ,				lowercase_      :       Union[str, Any]=None   ,				lowercase_      :       List[Any]=None   ,				lowercase_      :       Union[str, Any]=None   ,				lowercase_      :       Optional[Dict[str, Any]] = None   ,				**lowercase_      :       Tuple   ,				):







    '''simple docstring'''


    lowercase_     =       {} if sp_model_kwargs is None else sp_model_kwargs

    lowercase_     =       kwargs.get("""name_or_path"""      )
    if name_or_path is None:
      logger.warning(
          """name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"""
          """ you are testing the model, this can safely be ignored"""      )
      lowercase_     =       """None"""

    # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
    lowercase_     =       """<|endoftext|>""" if eos_token is None else eos_token
    lowercase_     =       """<unk>""" if unk_token is None else unk_token
    if "gpt-sw3-7b" in name_or_path:
      lowercase_     =       unk_token if pad_token is None else pad_token
      lowercase_     =       eos_token if bos_token is None else bos_token
    else:
      lowercase_     =       """<pad>""" if pad_token is None else pad_token
      lowercase_     =       """<s>""" if bos_token is None else bos_token

    super().__init__(
        do_lower_case=lowercase_   ,				remove_space=lowercase_   ,				keep_accents=lowercase_   ,				bos_token=lowercase_   ,				eos_token=lowercase_   ,				unk_token=lowercase_   ,				pad_token=lowercase_   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**lowercase_   ,				)

    lowercase_     =       do_lower_case
    lowercase_     =       remove_space
    lowercase_     =       keep_accents
    lowercase_     =       vocab_file

    lowercase_     =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
    self.sp_model.Load(lowercase_      )

    # Used for whitespace normalization in input texts
    # fmt : off
    lowercase_     =       {""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
    # fmt : on

    # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
    lowercase_     =       re.compile(
        F"""[{"".join(map(lowercase_   ,				list(range(0   ,				9      )      ) + list(range(11   ,				32      )      ) + list(range(127   ,				160      )      ) + [160, 173, 8_203]      )      )}]"""      )




  def __getstate__(		self      :       Tuple      ):







    '''simple docstring'''


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




  def __setstate__(		self      :       Tuple   ,				lowercase_      :       Union[str, Any]      ):







    '''simple docstring'''


    lowercase_     =       d

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

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




  @property
  # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


    return len(self.sp_model      )




  def 		lowerCamelCase__  (		self      :       List[Any]   ,				lowercase_      :       str      ):







    '''simple docstring'''


    lowercase_     =       self.non_printing_characters_re.sub(""""""   ,				lowercase_      )

    # Normalize whitespaces
    lowercase_     =       """""".join([char if char not in self.whitespaces else """ """ for char in text]      )

    # NFC Unicode normalization
    lowercase_     =       unicodedata.normalize("""NFC"""   ,				lowercase_      )
    return text




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       str   ,				**lowercase_      :       Any      ):







    '''simple docstring'''


    lowercase_     =       self.preprocess_text(lowercase_      )
    return self.sp_model.encode(lowercase_   ,				out_type=lowercase_      )




  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       str      ):







    '''simple docstring'''


    return self.sp_model.PieceToId(lowercase_      )




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       int      ):







    '''simple docstring'''


    return self.sp_model.IdToPiece(lowercase_      )




  @staticmethod
  def 		lowerCamelCase__  (		lowercase_      :       str      ):







    '''simple docstring'''


    return out_string




  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       List[str]      ):







    '''simple docstring'''


    lowercase_     =       []
    lowercase_     =       """"""
    lowercase_     =       False
    for token in tokens:
      # make sure that special tokens are not decoded using sentencepiece model
      if token in self.all_special_tokens:
        # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
        if not prev_is_special:
          out_string += " "

        out_string += self.sp_model.decode(lowercase_      ) + token
        lowercase_     =       True
        lowercase_     =       []
      else:
        current_sub_tokens.append(lowercase_      )
        lowercase_     =       False
    out_string += self.sp_model.decode(lowercase_      )

    return out_string




  def 		lowerCamelCase__  (		self      :       Tuple      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       Tuple   ,				lowercase_      :       str   ,				lowercase_      :       Optional[str] = None      ):







    '''simple docstring'''


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

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

    return (out_vocab_file,)




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       Union[str, List[str]]   ,				lowercase_      :       Union[str, bool] = False      ):







    '''simple docstring'''


    if isinstance(lowercase_   ,				lowercase_      ):
      lowercase_     =       self.preprocess_text(lowercase_      )
      lowercase_     =       self.sp_model.encode(lowercase_      )
    else:
      lowercase_     =       [self.preprocess_text(lowercase_      ) for t in text]
      lowercase_     =       self.sp_model.encode(lowercase_      )

    if return_tensors is True or return_tensors == "pt":
      lowercase_     =       torch.tensor(lowercase_      )

    return token_ids




  def 		lowerCamelCase__  (		self      :       Union[str, Any]   ,				lowercase_      :       Union[int, List[int]]      ):







    '''simple docstring'''


    return self.sp_model.decode(lowercase_      )





  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       "Conversation"      ):







    '''simple docstring'''


    lowercase_     =       [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
    lowercase_     =       (
        F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(lowercase_      ) + F"""{self.bos_token}Bot:"""
    )
    return self.encode(text=lowercase_      )

