





'''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


__lowerCAmelCase							:	Tuple			=logging.get_logger(__name__)

__lowerCAmelCase							:	Any			={"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__lowerCAmelCase							:	Optional[int]			={
    "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"
        ),
    },
}

__lowerCAmelCase							:	List[str]			={
    "Salesforce/codegen-350M-mono": 2048,
}






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				VOCAB_FILES_NAMES
 __lowercase   		=				PRETRAINED_VOCAB_FILES_MAP
 __lowercase   		=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase   		=				["""input_ids""", """attention_mask"""]
 __lowercase   		=				CodeGenTokenizer
 def __init__(				self      :int						,       lowercase_      :List[Any]=None						,       lowercase_      :Optional[int]=None						,       lowercase_      :Optional[Any]=None						,       lowercase_      :Optional[int]="<|endoftext|>"						,       lowercase_      :Optional[Any]="<|endoftext|>"						,       lowercase_      :int="<|endoftext|>"						,       lowercase_      :Optional[Any]=False						,       **lowercase_      :List[Any]						,       )->							Optional[Any]:
     super().__init__(
         lowercase_						,       lowercase_						,       tokenizer_file=lowercase_						,       unk_token=lowercase_						,       bos_token=lowercase_						,       eos_token=lowercase_						,       add_prefix_space=lowercase_						,       **lowercase_						,       )

     if kwargs.pop("add_bos_token"						,       lowercase_							):
         A__				=			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."							)

     A__				=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
     if pre_tok_state.get("add_prefix_space"						,       lowercase_							) != add_prefix_space:
         A__				=			getattr(lowercase_						,       pre_tok_state.pop("type"							)							)
         A__				=			add_prefix_space
         A__				=			pre_tok_class(**lowercase_							)

     A__				=			add_prefix_space
 def      UpperCAmelCase_							(				self      :Union[str, Any]						,       *lowercase_      :List[Any]						,       **lowercase_      :Optional[Any]							)->							BatchEncoding:
     A__				=			kwargs.get("is_split_into_words"						,       lowercase_							)
     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(*lowercase_						,       **lowercase_							)
 def      UpperCAmelCase_							(				self      :List[str]						,       *lowercase_      :Dict						,       **lowercase_      :str							)->							BatchEncoding:
     A__				=			kwargs.get("is_split_into_words"						,       lowercase_							)

     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(*lowercase_						,       **lowercase_							)
 def      UpperCAmelCase_							(				self      :int						,       lowercase_      :str						,       lowercase_      :Optional[str] = None							)->							Tuple[str]:
     A__				=			self._tokenizer.model.save(lowercase_						,       name=lowercase_							)
     return tuple(lowercase_							)
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]						,       lowercase_      :bool = False						,       lowercase_      :bool = None						,       lowercase_      :Optional[List[str]] = None						,       **lowercase_      :List[Any]						,       )->							str:
     A__				=			super().decode(
         token_ids=lowercase_						,       skip_special_tokens=lowercase_						,       clean_up_tokenization_spaces=lowercase_						,       **lowercase_						,       )

     if truncate_before_pattern is not None and len(lowercase_							) > 0:
         A__				=			self.truncate(lowercase_						,       lowercase_							)

     return decoded_text



 def      UpperCAmelCase_							(				self      :Tuple						,       lowercase_      :str						,       lowercase_      :str							)->							Union[str, Any]:
     def find_re(lowercase_      :Optional[Any]						,       lowercase_      :List[str]						,       lowercase_      :Optional[int]							):
         A__				=			pattern.search(lowercase_						,       lowercase_							)
         return m.start() if m else -1

     A__				=			[re.compile(lowercase_						,       re.MULTILINE							) for pattern in truncate_before_pattern]

     A__				=			list(re.finditer("^print"						,       lowercase_						,       re.MULTILINE							)							)

     if len(lowercase_							) > 1:
         A__				=			completion[: prints[1].start()]

     A__				=			list(re.finditer("^def"						,       lowercase_						,       re.MULTILINE							)							)

     if len(lowercase_							) > 1:
         A__				=			completion[: defs[1].start()]

     A__				=			0

     A__				=			[
         pos for pos in [find_re(lowercase_						,       lowercase_						,       lowercase_							) for terminal in terminals] if pos != -1
     ]

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










'''simple docstring'''

import warnings


warnings.warn(
    "memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
    "`from accelerate import find_executable_batch_size` to avoid this warning.",
    FutureWarning,
)



