

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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
					from ...tokenization_utils_base import TextInput
from ...utils import logging


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = {'vocab_file': 'spiece.model'}

SCREAMING_SNAKE_CASE_            = {
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
SCREAMING_SNAKE_CASE_            = {
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}

SCREAMING_SNAKE_CASE_            = '▁'

class 			a					(		UpperCAmelCase			):
						_lowercase        =     VOCAB_FILES_NAMES
						_lowercase        =     PRETRAINED_VOCAB_FILES_MAP
						_lowercase        =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowercase        =     ["input_ids", "attention_mask"]


						def __init__(    self  ,   A_  ,   A_="</s>"  ,   A_="<unk>"  ,   A_="<pad>"  ,   A_=100  ,   A_=None  ,   A_ = None  ,   A_=True  ,   **A_  ,   ):







										'''simple docstring'''


										if extra_ids > 0 and additional_special_tokens is None:
														_UpperCAmelCase    :						Union[str, Any]							=      [f'<extra_id_{i}>' for i in range(A_		)]
										elif extra_ids > 0 and additional_special_tokens is not None:
														# Check that we have the right number of extra_id special tokens
														_UpperCAmelCase    :						Union[str, Any]							=      len(set(filter(lambda A_		: bool("extra_id" in str(A_		)		)  ,   A_		)		)		)
														if extra_tokens != extra_ids:
																		raise ValueError(
																		    f'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
																		    " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
																		    " tokens"		)
										if legacy:
														logger.warning_once(
														    f'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
														    " read the related pull request available at https://github.com/huggingface/transformers/pull/24565"		)

										_UpperCAmelCase    :						Optional[Any]							=      legacy
										_UpperCAmelCase    :						Union[str, Any]							=      {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    eos_token=A_  ,   unk_token=A_  ,   pad_token=A_  ,   extra_ids=A_  ,   additional_special_tokens=A_  ,   sp_model_kwargs=self.sp_model_kwargs  ,   legacy=A_  ,   **A_  ,   )

										_UpperCAmelCase    :						Tuple							=      vocab_file
										_UpperCAmelCase    :						Optional[int]							=      extra_ids

										_UpperCAmelCase    :						Dict							=      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
										self.sp_model.Load(A_		)


						@staticmethod
						def     _UpperCAmelCase			(    A_  ,   A_  ,   A_		):







										'''simple docstring'''


										if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
														_UpperCAmelCase    :						str							=      TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
														if init_max_model_length is not None and init_max_model_length != max_model_length:
																		return init_max_model_length
														elif init_max_model_length is None:
																		warnings.warn(
																		    "This tokenizer was incorrectly instantiated with a model max length of"
																		    f' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
																		    " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
																		    " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
																		    f' {pretrained_model_name_or_path} automatically truncating your input to'
																		    f' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
																		    f' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
																		    " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
																		    " instantiate this tokenizer with `model_max_length` set to your preferred value."  ,   A_  ,   )

										return max_model_length


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return self.sp_model.get_piece_size() + self._extra_ids


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      {self.convert_ids_to_tokens(A_		): i for i in range(self.vocab_size		)}
										vocab.update(self.added_tokens_encoder		)
										return vocab


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None  ,   A_ = False		):







										'''simple docstring'''


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

										# normal case: some special tokens
										if token_ids_a is None:
														return ([0] * len(A_		)) + [1]
										return ([0] * len(A_		)) + [1] + ([0] * len(A_		)) + [1]


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return list(
										    set(filter(lambda A_		: bool(re.search(R"<extra_id_\d+>"  ,   A_		)		) is not None  ,   self.additional_special_tokens		)		)		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return [self._convert_token_to_id(A_		) for token in self.get_sentinel_tokens()]


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										if len(A_		) > 0 and token_ids[-1] == self.eos_token_id:
														warnings.warn(
														    f'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
														    " eos tokens being added."		)
														return token_ids
										else:
														return token_ids + [self.eos_token_id]


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      [self.eos_token_id]

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


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      self._add_eos_if_not_present(A_		)
										if token_ids_a is None:
														return token_ids_a
										else:
														_UpperCAmelCase    :						Dict							=      self._add_eos_if_not_present(A_		)
														return token_ids_a + token_ids_a


						def __getstate__(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.__dict__.copy()
										_UpperCAmelCase    :						Optional[Any]							=      None
										return state


						def __setstate__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      d

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

										_UpperCAmelCase    :						Union[str, Any]							=      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
										self.sp_model.Load(self.vocab_file		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   **A_		):







										'''simple docstring'''


										if not self.legacy:
														_UpperCAmelCase    :						Dict							=      SPIECE_UNDERLINE + text.replace(A_  ,   " "		)
										return super().tokenize(A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   **A_		):







										'''simple docstring'''


										if not self.legacy:
														_UpperCAmelCase    :						int							=      text.startswith(A_		)
														if is_first:
																		_UpperCAmelCase    :						int							=      text[1:]

										_UpperCAmelCase    :						Any							=      self.sp_model.encode(A_  ,   out_type=A_		)

										if not self.legacy and not is_first and not text.startswith(" "		) and tokens[0].startswith(A_		):
														_UpperCAmelCase    :						str							=      ([tokens[0][1:]] if len(tokens[0]		) > 1 else []) + tokens[1:]
										return tokens


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										if token.startswith("<extra_id_"		):
														_UpperCAmelCase    :						Union[str, Any]							=      re.match(R"<extra_id_(\d+)>"  ,   A_		)
														_UpperCAmelCase    :						Tuple							=      int(match.group(1		)		)
														return self.vocab_size - num - 1
										return self.sp_model.piece_to_id(A_		)


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										if index < self.sp_model.get_piece_size():
														_UpperCAmelCase    :						Dict							=      self.sp_model.IdToPiece(A_		)
										else:
														_UpperCAmelCase    :						str							=      f'<extra_id_{self.vocab_size - 1 - index}>'
										return token


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      []
										_UpperCAmelCase    :						Optional[Any]							=      ""
										_UpperCAmelCase    :						Dict							=      False
										for token in tokens:
														# make sure that special tokens are not decoded using sentencepiece model
														if token in self.all_special_tokens:
																		if not prev_is_special:
																						out_string += " "
																		out_string += self.sp_model.decode(A_		) + token
																		_UpperCAmelCase    :						List[str]							=      True
																		_UpperCAmelCase    :						str							=      []
														else:
																		current_sub_tokens.append(A_		)
																		_UpperCAmelCase    :						Tuple							=      False
										out_string += self.sp_model.decode(A_		)
										return out_string.strip()





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None		):







										'''simple docstring'''


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

										if os.path.abspath(self.vocab_file		) != os.path.abspath(A_		) and os.path.isfile(self.vocab_file		):
														copyfile(self.vocab_file  ,   A_		)
										elif not os.path.isfile(self.vocab_file		):
														with open(A_  ,   "wb"		) as fi:
																		_UpperCAmelCase    :						List[str]							=      self.sp_model.serialized_model_proto()
																		fi.write(A_		)

										return (out_vocab_file,)





import inspect
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_config_docstrings.py
SCREAMING_SNAKE_CASE_            = 'src/transformers'


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

SCREAMING_SNAKE_CASE_            = transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
SCREAMING_SNAKE_CASE_            = re.compile(R'\[(.+?)\]\((https://huggingface\.co/.+?)\)')


SCREAMING_SNAKE_CASE_            = {
    'DecisionTransformerConfig',
    'EncoderDecoderConfig',
    'MusicgenConfig',
    'RagConfig',
    'SpeechEncoderDecoderConfig',
    'TimmBackboneConfig',
    'VisionEncoderDecoderConfig',
    'VisionTextDualEncoderConfig',
    'LlamaConfig',
}

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Dict	)				->    Union[str, Any]:
				_UpperCAmelCase    :						List[Any]							=      None

				# source code of `config_class`
				_UpperCAmelCase    :						int							=      inspect.getsource(lowerCAmelCase	)
				_UpperCAmelCase    :						Tuple							=      _re_checkpoint.findall(lowerCAmelCase	)

				# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
				# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
				for ckpt_name, ckpt_link in checkpoints:
								# allow the link to end with `/`
								if ckpt_link.endswith("/"	):
												_UpperCAmelCase    :						Dict							=      ckpt_link[:-1]

								# verify the checkpoint name corresponds to the checkpoint link
								_UpperCAmelCase    :						Any							=      F'https://huggingface.co/{ckpt_name}'
								if ckpt_link == ckpt_link_from_name:
												_UpperCAmelCase    :						Union[str, Any]							=      ckpt_name
												break

				return checkpoint

def 							__SCREAMING_SNAKE_CASE							(			)				->    Dict:
				_UpperCAmelCase    :						Any							=      []

				for config_class in list(CONFIG_MAPPING.values()	):
								# Skip deprecated models
								if "models.deprecated" in config_class.__module__:
												continue
								_UpperCAmelCase    :						int							=      get_checkpoint_from_config_class(lowerCAmelCase	)

								_UpperCAmelCase    :						Tuple							=      config_class.__name__
								if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
												configs_without_checkpoint.append(lowerCAmelCase	)

				if len(lowerCAmelCase	) > 0:
								_UpperCAmelCase    :						int							=      "\n".join(sorted(lowerCAmelCase	)	)
								raise ValueError(F'The following configurations don\'t contain any valid checkpoint:\n{message}'	)


if __name__ == "__main__":
					check_config_docstrings_have_checkpoints()


