
import os
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 logging


UpperCAmelCase	      = logging.get_logger(__name__)

UpperCAmelCase	      = {'''vocab_file''': '''sentencepiece.bpe.model'''}

UpperCAmelCase	      = {
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
}

UpperCAmelCase	      = {
    '''moussaKam/mbarthez''': 1024,
    '''moussaKam/barthez''': 1024,
    '''moussaKam/barthez-orangesum-title''': 1024,
}

UpperCAmelCase	      = '''▁'''







class        A_   (			__a   ):




	'''simple docstring'''


	_UpperCamelCase     :     str							 =       VOCAB_FILES_NAMES
	_UpperCamelCase     :     Tuple							 =       PRETRAINED_VOCAB_FILES_MAP
	_UpperCamelCase     :     Optional[Any]							 =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_UpperCamelCase     :     str							 =       ["""input_ids""", """attention_mask"""]





	def __init__(       self		,    snake_case		,    snake_case="<s>"		,    snake_case="</s>"		,    snake_case="</s>"		,    snake_case="<s>"		,    snake_case="<unk>"		,    snake_case="<pad>"		,    snake_case="<mask>"		,    snake_case = None		,    **snake_case		,    ):
		lowercase       =    AddedToken(a_		,    lstrip=a_		,    rstrip=a_       ) if isinstance(a_		,    a_       ) else mask_token

		lowercase       =    {} if sp_model_kwargs is None else sp_model_kwargs

		super().__init__(
		    bos_token=a_		,    eos_token=a_		,    unk_token=a_		,    sep_token=a_		,    cls_token=a_		,    pad_token=a_		,    mask_token=a_		,    sp_model_kwargs=self.sp_model_kwargs		,    **a_		,    )

		lowercase       =    vocab_file
		lowercase       =    spm.SentencePieceProcessor(**self.sp_model_kwargs       )
		self.sp_model.Load(str(a_       )       )

		lowercase       =    {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

		lowercase       =    len(self.sp_model       ) - 1
		lowercase       =    {v: k for k, v in self.fairseq_tokens_to_ids.items()}





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case = None       ):



		if token_ids_a is None:
			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
		lowercase       =    [self.cls_token_id]
		lowercase       =    [self.sep_token_id]
		return cls + token_ids_a + sep + sep + token_ids_a + sep





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case = None		,    snake_case = False       ):



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

		if token_ids_a is None:
			return [1] + ([0] * len(a_       )) + [1]
		return [1] + ([0] * len(a_       )) + [1, 1] + ([0] * len(a_       )) + [1]





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case = None       ):
		lowercase       =    [self.sep_token_id]
		lowercase       =    [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]





	@property
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		return len(self.sp_model       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    {self.convert_ids_to_tokens(a_       ): i for i in range(self.vocab_size       )}
		vocab.update(self.added_tokens_encoder       )
		return vocab





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		return self.sp_model.encode(a_		,    out_type=a_       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):



		if token in self.fairseq_tokens_to_ids:
			return self.fairseq_tokens_to_ids[token]
		lowercase       =    self.sp_model.PieceToId(a_       )

		return spm_id if spm_id else self.unk_token_id





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):



		if index in self.fairseq_ids_to_tokens:
			return self.fairseq_ids_to_tokens[index]
		return self.sp_model.IdToPiece(a_       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		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:
				if not prev_is_special:
					out_string += " "
				out_string += self.sp_model.decode(a_       ) + token
				lowercase       =    True
				lowercase       =    []
			else:
				current_sub_tokens.append(a_       )
				lowercase       =    False
		out_string += self.sp_model.decode(a_       )
		return out_string.strip()





	def __getstate__(       self       ):
		lowercase       =    self.__dict__.copy()
		lowercase       =    None
		return state





	def __setstate__(       self		,    snake_case       ):
		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       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case = None       ):



		if not os.path.isdir(a_       ):
			logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
			return
		lowercase       =    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:
				lowercase       =    self.sp_model.serialized_model_proto()
				fi.write(a_       )

		return (out_vocab_file,)







"""simple docstring"""


import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
     if is_torch_available():
          import torch
     if is_tf_available():
          import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


A_						=				logging.get_logger(__name__)

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

A_						=				{
    '''vocab_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''Salesforce/codegen-350M-mono''': (
            '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'''
        ),
    },
}

A_						=				{
    '''Salesforce/codegen-350M-mono''': 20_48,
}






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					VOCAB_FILES_NAMES
 lowercase__ 					=					PRETRAINED_VOCAB_FILES_MAP
 lowercase__ 					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase__ 					=					["input_ids", "attention_mask"]
 lowercase__ 					=					CodeGenTokenizer


 def __init__(				self:		Union[str, Any],      a_:		List[Any]=None,      a_:		str=None,      a_:		str=None,      a_:		Dict="<|endoftext|>",      a_:		Tuple="<|endoftext|>",      a_:		str="<|endoftext|>",      a_:		List[Any]=False,      **a_:		List[str],      ):




   '''simple docstring'''



   super().__init__(
       a_,      a_,      tokenizer_file=a_,      unk_token=a_,      bos_token=a_,      eos_token=a_,      add_prefix_space=a_,      **a_,      )

   if kwargs.pop("""add_bos_token""",      a_		):
     _snake_case							:		str								=  kwargs.pop("""name_or_path""",      """"""		)
     raise ValueError(
         """Currenty GPT2's fast tokenizer does NOT support adding a BOS token."""
         """Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n"""
         f"`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"
         f"`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"
         """This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."""
         """ so that the fast tokenizer works correctly."""		)

   _snake_case							:		Tuple								=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
   if pre_tok_state.get("""add_prefix_space""",      a_		) != add_prefix_space:
     _snake_case							:		Dict								=  getattr(a_,      pre_tok_state.pop("""type"""		)		)
     _snake_case							:		Dict								=  add_prefix_space
     _snake_case							:		str								=  pre_tok_class(**a_		)

   _snake_case							:		List[Any]								=  add_prefix_space


 def  UpperCamelCase_   (				self:		Any,      *a_:		Any,      **a_:		int		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  kwargs.get("""is_split_into_words""",      a_		)
   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(*a_,      **a_		)


 def  UpperCamelCase_   (				self:		Optional[Any],      *a_:		Any,      **a_:		List[str]		):




   '''simple docstring'''



   _snake_case							:		Dict								=  kwargs.get("""is_split_into_words""",      a_		)

   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(*a_,      **a_		)


 def  UpperCamelCase_   (				self:		Optional[int],      a_:		str,      a_:		Optional[str] = None		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  self._tokenizer.model.save(a_,      name=a_		)
   return tuple(a_		)


 def  UpperCamelCase_   (				self:		str,      a_:		Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],      a_:		bool = False,      a_:		bool = None,      a_:		Optional[List[str]] = None,      **a_:		List[str],      ):




   '''simple docstring'''



   _snake_case							:		Any								=  super().decode(
       token_ids=a_,      skip_special_tokens=a_,      clean_up_tokenization_spaces=a_,      **a_,      )

   if truncate_before_pattern is not None and len(a_		) > 0:
     _snake_case							:		List[str]								=  self.truncate(a_,      a_		)

   return decoded_text


 def  UpperCamelCase_   (				self:		Dict,      a_:		Tuple,      a_:		Optional[Any]		):




   '''simple docstring'''



   def find_re(a_:		Dict,      a_:		str,      a_:		Union[str, Any]		):
     _snake_case							:		Any								=  pattern.search(a_,      a_		)
     return m.start() if m else -1

   _snake_case							:		Tuple								=  [re.compile(a_,      re.MULTILINE		) for pattern in truncate_before_pattern]

   _snake_case							:		List[Any]								=  list(re.finditer("""^print""",      a_,      re.MULTILINE		)		)

   if len(a_		) > 1:
     _snake_case							:		int								=  completion[: prints[1].start()]

   _snake_case							:		List[str]								=  list(re.finditer("""^def""",      a_,      re.MULTILINE		)		)

   if len(a_		) > 1:
     _snake_case							:		List[Any]								=  completion[: defs[1].start()]

   _snake_case							:		int								=  0

   _snake_case							:		List[Any]								=  [
       pos for pos in [find_re(a_,      a_,      a_		) for terminal in terminals] if pos != -1
   ]

   if len(a_		) > 0:
     return completion[: min(a_		)]
   else:
     return completion


