




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


UpperCamelCase								=	logging.get_logger(__name__)

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

UpperCamelCase								=	{
    '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'
        ),
    },
}

UpperCamelCase								=	{
    'Salesforce/codegen-350M-mono': 2048,
}





class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

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


    def __init__(			self	:	Optional[Any]  ,      SCREAMING_SNAKE_CASE__	:	Any=None  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]=None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=None  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]="<|endoftext|>"  ,      SCREAMING_SNAKE_CASE__	:	Dict="<|endoftext|>"  ,      SCREAMING_SNAKE_CASE__	:	Any="<|endoftext|>"  ,      SCREAMING_SNAKE_CASE__	:	Dict=False  ,      **SCREAMING_SNAKE_CASE__	:	Tuple  ,      ) ->			Optional[int]:
         super().__init__(
             SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      tokenizer_file=SCREAMING_SNAKE_CASE__  ,      unk_token=SCREAMING_SNAKE_CASE__  ,      bos_token=SCREAMING_SNAKE_CASE__  ,      eos_token=SCREAMING_SNAKE_CASE__  ,      add_prefix_space=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  ,      )

         if kwargs.pop("add_bos_token"  ,      SCREAMING_SNAKE_CASE__  ):
              lowerCAmelCase__            =					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."  )

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

         lowerCAmelCase__            =					add_prefix_space


    def   a (			self	:	str  ,      *SCREAMING_SNAKE_CASE__	:	str  ,      **SCREAMING_SNAKE_CASE__	:	Dict  ) ->			BatchEncoding:
         lowerCAmelCase__            =					kwargs.get("is_split_into_words"  ,      SCREAMING_SNAKE_CASE__  )
         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(*SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Tuple  ,      *SCREAMING_SNAKE_CASE__	:	List[Any]  ,      **SCREAMING_SNAKE_CASE__	:	Tuple  ) ->			BatchEncoding:
         lowerCAmelCase__            =					kwargs.get("is_split_into_words"  ,      SCREAMING_SNAKE_CASE__  )

         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(*SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	str  ,      SCREAMING_SNAKE_CASE__	:	Optional[str] = None  ) ->			Tuple[str]:
         lowerCAmelCase__            =					self._tokenizer.model.save(SCREAMING_SNAKE_CASE__  ,      name=SCREAMING_SNAKE_CASE__  )
         return tuple(SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Any  ,      SCREAMING_SNAKE_CASE__	:	Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]  ,      SCREAMING_SNAKE_CASE__	:	bool = False  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[List[str]] = None  ,      **SCREAMING_SNAKE_CASE__	:	Union[str, Any]  ,      ) ->			str:
         lowerCAmelCase__            =					super().decode(
             token_ids=SCREAMING_SNAKE_CASE__  ,      skip_special_tokens=SCREAMING_SNAKE_CASE__  ,      clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  ,      )

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

         return decoded_text


    def   a (			self	:	int  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	Dict  ) ->			Optional[Any]:
         def find_re(SCREAMING_SNAKE_CASE__	:	int  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	Tuple  ):
              lowerCAmelCase__            =					pattern.search(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )
              return m.start() if m else -1

         lowerCAmelCase__            =					[re.compile(SCREAMING_SNAKE_CASE__  ,      re.MULTILINE  ) for pattern in truncate_before_pattern]

         lowerCAmelCase__            =					list(re.finditer("^print"  ,      SCREAMING_SNAKE_CASE__  ,      re.MULTILINE  )  )

         if len(SCREAMING_SNAKE_CASE__  ) > 1:
              lowerCAmelCase__            =					completion[: prints[1].start()]

         lowerCAmelCase__            =					list(re.finditer("^def"  ,      SCREAMING_SNAKE_CASE__  ,      re.MULTILINE  )  )

         if len(SCREAMING_SNAKE_CASE__  ) > 1:
              lowerCAmelCase__            =					completion[: defs[1].start()]

         lowerCAmelCase__            =					0

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

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




'''simple docstring'''







from __future__ import annotations

from collections.abc import Iterator
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							value
            _lowerCAmelCase     :				Node | None       =							None
            _lowerCAmelCase     :				Node | None       =							None
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							tree







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            if node is None:
                   return 0
            return node.value + (
                self.depth_first_search(node.left ) + self.depth_first_search(node.right )
            )







     def __iter__(			self ):

            '''simple docstring'''

            yield self.depth_first_search(self.tree )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

