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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


lowerCamelCase_						: str         =  logging.get_logger(__name__)

lowerCamelCase_						: Optional[Any]         =  {"""vocab_file""": """spiece.model"""}

lowerCamelCase_						: List[str]         =  {
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    }
}

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			def __init__(       self :       Optional[int]						,	snake_case_ :       Any						,	snake_case_ :       int=False						,	snake_case_ :       int=True						,	snake_case_ :       Tuple=False						,	snake_case_ :       List[Any]="<s>"						,	snake_case_ :       Optional[Any]="</s>"						,	snake_case_ :       Union[str, Any]="<unk>"						,	snake_case_ :       Union[str, Any]="<sep>"						,	snake_case_ :       str="<pad>"						,	snake_case_ :       List[Any]="<cls>"						,	snake_case_ :       Optional[int]="<mask>"						,	snake_case_ :       int=["<eop>", "<eod>"]						,	snake_case_ :       Optional[Dict[str, Any]] = None						,	**snake_case_ :       Optional[int]						,	):
							UpperCamelCase_:							Optional[int]	=						AddedToken(snake_case_						,	lstrip=snake_case_						,	rstrip=snake_case_      ) if isinstance(snake_case_						,	snake_case_      ) else mask_token

							UpperCamelCase_:							Tuple	=						{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    do_lower_case=snake_case_						,	remove_space=snake_case_						,	keep_accents=snake_case_						,	bos_token=snake_case_						,	eos_token=snake_case_						,	unk_token=snake_case_						,	sep_token=snake_case_						,	pad_token=snake_case_						,	cls_token=snake_case_						,	mask_token=snake_case_						,	additional_special_tokens=snake_case_						,	sp_model_kwargs=self.sp_model_kwargs						,	**snake_case_						,	)

							UpperCamelCase_:							Optional[Any]	=						3

							UpperCamelCase_:							int	=						do_lower_case
							UpperCamelCase_:							Union[str, Any]	=						remove_space
							UpperCamelCase_:							List[Any]	=						keep_accents
							UpperCamelCase_:							Any	=						vocab_file

							UpperCamelCase_:							int	=						spm.SentencePieceProcessor(**self.sp_model_kwargs      )
							self.sp_model.Load(snake_case_      )

							try:
											import jieba
							except ModuleNotFoundError as error:
											raise error.__class__(
											    """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
											    """See https://pypi.org/project/jieba/ for installation."""      )
							UpperCamelCase_:							List[Any]	=						jieba
							UpperCamelCase_:							Dict	=						str.maketrans(""" \n"""						,	"""\u2582\u2583"""      )

			@property
			# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
			def       lowerCAmelCase__	(       self :       Dict      ):
							return len(self.sp_model      )

			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							Any	=						{self.convert_ids_to_tokens(snake_case_      ): i for i in range(self.vocab_size      )}
							vocab.update(self.added_tokens_encoder      )
							return vocab

			def __getstate__(       self :       Dict      ):
							UpperCamelCase_:							Any	=						self.__dict__.copy()
							UpperCamelCase_:							Optional[int]	=						None
							return state

			def __setstate__(       self :       List[str]						,	snake_case_ :       List[Any]      ):
							UpperCamelCase_:							int	=						d

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

							UpperCamelCase_:							Tuple	=						spm.SentencePieceProcessor(**self.sp_model_kwargs      )
							self.sp_model.Load(self.vocab_file      )

			def       lowerCAmelCase__	(       self :       str						,	snake_case_ :       Union[str, Any]      ):
							if self.remove_space:
											UpperCamelCase_:							List[Any]	=						""" """.join(inputs.strip().split()      )
							else:
											UpperCamelCase_:							List[Any]	=						inputs
							UpperCamelCase_:							str	=						outputs.replace("""``"""						,	"""\""""      ).replace("""''"""						,	"""\""""      )

							if not self.keep_accents:
											UpperCamelCase_:							List[Any]	=						unicodedata.normalize("""NFKD"""						,	snake_case_      )
											UpperCamelCase_:							Any	=						"""""".join([c for c in outputs if not unicodedata.combining(snake_case_      )]      )
							if self.do_lower_case:
											UpperCamelCase_:							int	=						outputs.lower()

							return outputs

			def       lowerCAmelCase__	(       self :       List[str]						,	snake_case_ :       str      ):
							UpperCamelCase_:							str	=						self.preprocess_text(snake_case_      )
							UpperCamelCase_:							Optional[int]	=						self.sp_model.encode(snake_case_						,	out_type=snake_case_      )
							UpperCamelCase_:							Dict	=						[]
							for piece in pieces:
											if len(snake_case_      ) > 1 and piece[-1] == str(""","""      ) and piece[-2].isdigit():
															UpperCamelCase_:							Optional[Any]	=						self.sp_model.EncodeAsPieces(piece[:-1].replace(snake_case_						,	""""""      )      )
															if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																			if len(cur_pieces[0]      ) == 1:
																							UpperCamelCase_:							List[Any]	=						cur_pieces[1:]
																			else:
																							UpperCamelCase_:							List[str]	=						cur_pieces[0][1:]
															cur_pieces.append(piece[-1]      )
															new_pieces.extend(snake_case_      )
											else:
															new_pieces.append(snake_case_      )

							return new_pieces

			def       lowerCAmelCase__	(       self :       Optional[Any]						,	snake_case_ :       Optional[Any]      ):
							return self.sp_model.PieceToId(snake_case_      )

			def       lowerCAmelCase__	(       self :       str						,	snake_case_ :       str      ):
							return self.sp_model.IdToPiece(snake_case_      )

			def       lowerCAmelCase__	(       self :       int						,	snake_case_ :       Optional[int]      ):
							UpperCamelCase_:							List[Any]	=						"""""".join(snake_case_      ).replace(snake_case_						,	""" """      ).strip()
							return out_string

			def       lowerCAmelCase__	(       self :       Optional[int]						,	snake_case_ :       List[int]						,	snake_case_ :       Optional[List[int]] = None      ):
							UpperCamelCase_:							List[Any]	=						[self.sep_token_id]
							UpperCamelCase_:							List[Any]	=						[self.cls_token_id]
							if token_ids_a is None:
											return token_ids_a + sep + cls
							return token_ids_a + sep + token_ids_a + sep + cls

			def       lowerCAmelCase__	(       self :       Optional[Any]						,	snake_case_ :       List[int]						,	snake_case_ :       Optional[List[int]] = None						,	snake_case_ :       bool = False      ):

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

							if token_ids_a is not None:
											return ([0] * len(snake_case_      )) + [1] + ([0] * len(snake_case_      )) + [1, 1]
							return ([0] * len(snake_case_      )) + [1, 1]

			def       lowerCAmelCase__	(       self :       Optional[int]						,	snake_case_ :       List[int]						,	snake_case_ :       Optional[List[int]] = None      ):
							UpperCamelCase_:							str	=						[self.sep_token_id]
							UpperCamelCase_:							Any	=						[2]

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

			def       lowerCAmelCase__	(       self :       str						,	snake_case_ :       str						,	snake_case_ :       Optional[str] = None      ):
							if not os.path.isdir(snake_case_      ):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
											return
							UpperCamelCase_:							Optional[Any]	=						os.path.join(
							    snake_case_						,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

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

							return (out_vocab_file,)




			def       lowerCAmelCase__	(       self :       Union[str, Any]						,	*snake_case_ :       List[Any]						,	**snake_case_ :       List[str]      ):
							UpperCamelCase_:							List[str]	=						super()._decode(*snake_case_						,	**snake_case_      )
							UpperCamelCase_:							Tuple	=						text.replace(""" """						,	""""""      ).replace("""\u2582"""						,	""" """      ).replace("""\u2583"""						,	"""\n"""      )
							return text








import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Union[str, Any]:
				# Initialise PyTorch model
				UpperCamelCase_:							List[Any]	=						TaConfig.from_json_file(lowerCamelCase						)
				print(F'''Building PyTorch model from configuration: {config}'''						)
				UpperCamelCase_:							Any	=						TaForConditionalGeneration(lowerCamelCase						)

				# Load weights from tf checkpoint
				load_tf_weights_in_ta(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)

				# Save pytorch-model
				print(F'''Save PyTorch model to {pytorch_dump_path}'''						)
				model.save_pretrained(lowerCamelCase						)


if __name__ == "__main__":
							lowerCamelCase_						: Tuple         =  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
							)
							parser.add_argument(
							    """--config_file""",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
							    ),
							)
							parser.add_argument(
							    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							lowerCamelCase_						: Union[str, Any]         =  parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






