





'''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							:     Optional[int]		     =logging.get_logger(__name__)

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

__lowerCAmelCase							:     List[Any]		     ={
    "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							:     int		     ={
    "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_				:int=None						,	lowercase_				:List[Any]=None						,	lowercase_				:int=None						,	lowercase_				:Tuple="<|endoftext|>"						,	lowercase_				:Optional[int]="<|endoftext|>"						,	lowercase_				:Optional[int]="<|endoftext|>"						,	lowercase_				:Tuple=False						,	**lowercase_				:Union[str, Any]						,	)->							int:
     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				:Tuple						,	*lowercase_				:int						,	**lowercase_				:Tuple							)->							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				:Dict						,	*lowercase_				:Union[str, Any]						,	**lowercase_				:Union[str, 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()._encode_plus(*lowercase_						,	**lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]						,	lowercase_				:str						,	lowercase_				:Optional[str] = None							)->							Tuple[str]:
     A__    =      self._tokenizer.model.save(lowercase_						,	name=lowercase_							)
     return tuple(lowercase_							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]						,	lowercase_				:bool = False						,	lowercase_				:bool = None						,	lowercase_				:Optional[List[str]] = None						,	**lowercase_				:Optional[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				:List[Any]						,	lowercase_				:List[str]						,	lowercase_				:Union[str, Any]							)->							int:
     def find_re(lowercase_				:Dict						,	lowercase_				:Dict						,	lowercase_				:str							):
         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'''

__lowerCAmelCase							:     Dict		     ="\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

__lowerCAmelCase							:     Optional[Any]		     =[{"type": "code", "content": INSTALL_CONTENT}]
__lowerCAmelCase							:     Tuple		     ={
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}



