'''simple docstring'''


import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__snake_case						  =		"""src/transformers"""
__snake_case						  =		"""docs/source/en"""
__snake_case						  =		"""."""

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Tuple:
  with open(SCREAMING_SNAKE_CASE_ ,	"""r""" ,	encoding="""utf-8""" ,	newline="""\n"""						) as f:
    lowercase_     =       f.readlines()
  # Find the start prompt.
  lowercase_     =       0
  while not lines[start_index].startswith(SCREAMING_SNAKE_CASE_						):
    start_index += 1
  start_index += 1

  lowercase_     =       start_index
  while not lines[end_index].startswith(SCREAMING_SNAKE_CASE_						):
    end_index += 1
  end_index -= 1

  while len(lines[start_index]						) <= 1:
    start_index += 1
  while len(lines[end_index]						) <= 1:
    end_index -= 1
  end_index += 1
  return "".join(lines[start_index:end_index]						), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
__snake_case						  =		"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
__snake_case						  =		re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
__snake_case						  =		re.compile(r"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__snake_case						  =		re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the transformers module imported is the one in the repo.
__snake_case						  =		direct_transformers_import(TRANSFORMERS_PATH)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Dict:
  lowercase_     =       re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" ,	SCREAMING_SNAKE_CASE_						)
  return [m.group(0						) for m in matches]

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Optional[Any]:
  lowercase_     =       2 if text == """✅""" or text == """❌""" else len(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       (width - text_length) // 2
  lowercase_     =       width - text_length - left_indent
  return " " * left_indent + text + " " * right_indent

def   A_      (       )			->Tuple:
  lowercase_     =       transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  lowercase_     =       {
      name: config_maping_names[code]
      for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
      if code in config_maping_names
  }
  lowercase_     =       {name: config.replace("""Config""" ,	""""""						) for name, config in model_name_to_config.items()}

  # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)

  # Let's lookup through all transformers object (once).
  for attr_name in dir(SCREAMING_SNAKE_CASE_						):
    lowercase_     =       None
    if attr_name.endswith("""Tokenizer"""						):
      lowercase_     =       slow_tokenizers
      lowercase_     =       attr_name[:-9]
    elif attr_name.endswith("""TokenizerFast"""						):
      lowercase_     =       fast_tokenizers
      lowercase_     =       attr_name[:-13]
    elif _re_tf_models.match(SCREAMING_SNAKE_CASE_						) is not None:
      lowercase_     =       tf_models
      lowercase_     =       _re_tf_models.match(SCREAMING_SNAKE_CASE_						).groups()[0]
    elif _re_flax_models.match(SCREAMING_SNAKE_CASE_						) is not None:
      lowercase_     =       flax_models
      lowercase_     =       _re_flax_models.match(SCREAMING_SNAKE_CASE_						).groups()[0]
    elif _re_pt_models.match(SCREAMING_SNAKE_CASE_						) is not None:
      lowercase_     =       pt_models
      lowercase_     =       _re_pt_models.match(SCREAMING_SNAKE_CASE_						).groups()[0]

    if lookup_dict is not None:
      while len(SCREAMING_SNAKE_CASE_						) > 0:
        if attr_name in model_name_to_prefix.values():
          lowercase_     =       True
          break
        # Try again after removing the last word in the name
        lowercase_     =       """""".join(camel_case_split(SCREAMING_SNAKE_CASE_						)[:-1]						)

    # Let's build that table!
  lowercase_     =       list(model_name_to_config.keys()						)
  model_names.sort(key=str.lower						)
  lowercase_     =       ["""Model""", """Tokenizer slow""", """Tokenizer fast""", """PyTorch support""", """TensorFlow support""", """Flax Support"""]
  # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
  lowercase_     =       [len(SCREAMING_SNAKE_CASE_						) + 2 for c in columns]
  lowercase_     =       max([len(SCREAMING_SNAKE_CASE_						) for name in model_names]						) + 2

  # Build the table per se
  lowercase_     =       """|""" + """|""".join([_center_text(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						) for c, w in zip(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)]						) + """|\n"""
  # Use ":-----:" format to center-aligned table cell texts
  table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths]						) + "|\n"

  lowercase_     =       {True: """✅""", False: """❌"""}
  for name in model_names:
    lowercase_     =       model_name_to_prefix[name]
    lowercase_     =       [
        name,
        check[slow_tokenizers[prefix]],
        check[fast_tokenizers[prefix]],
        check[pt_models[prefix]],
        check[tf_models[prefix]],
        check[flax_models[prefix]],
    ]
    table += "|" + "|".join([_center_text(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						) for l, w in zip(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)]						) + "|\n"
  return table

def   A_      (       SCREAMING_SNAKE_CASE_=False						)			->Dict:
  lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     =       _find_text_in_file(
      filename=os.path.join(SCREAMING_SNAKE_CASE_ ,	"""index.md"""						) ,	start_prompt="""<!--This table is updated automatically from the auto modules""" ,	end_prompt="""<!-- End table-->""" ,	)
  lowercase_     =       get_model_table_from_auto_modules()

  if current_table != new_table:
    if overwrite:
      with open(os.path.join(SCREAMING_SNAKE_CASE_ ,	"""index.md"""						) ,	"""w""" ,	encoding="""utf-8""" ,	newline="""\n"""						) as f:
        f.writelines(lines[:start_index] + [new_table] + lines[end_index:]						)
    else:
      raise ValueError(
          """The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this."""						)


if __name__ == "__main__":
       __snake_case						  =		argparse.ArgumentParser()
       parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
       __snake_case						  =		parser.parse_args()

       check_model_table(args.fix_and_overwrite)


'''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_      )

