





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


snake_case_				=			logging.get_logger(__name__)

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

snake_case_				=			{
    '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'
        ),
    },
}

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

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				Dict							=    VOCAB_FILES_NAMES
      A_						:				int							=    PRETRAINED_VOCAB_FILES_MAP
      A_						:				Union[str, Any]							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A_						:				Union[str, Any]							=    ['input_ids', 'attention_mask']
      A_						:				Any							=    CodeGenTokenizer


      def __init__(self       :      Optional[int]				,			a__       :      Any=None				,			a__       :      List[Any]=None				,			a__       :      Optional[int]=None				,			a__       :      Tuple="<|endoftext|>"				,			a__       :      Tuple="<|endoftext|>"				,			a__       :      Any="<|endoftext|>"				,			a__       :      Any=False				,			**a__       :      Dict				,			):





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

         __snake_case							       =   add_prefix_space


      def    a (self       :      Tuple				,			*a__       :      Any				,			**a__       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   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    a (self       :      Optional[int]				,			*a__       :      List[str]				,			**a__       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   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    a (self       :      List[str]				,			a__       :      str				,			a__       :      Optional[str] = None      ):





         """simple docstring"""



         __snake_case							       =   self._tokenizer.model.save(a__				,			name=a__      )
         return tuple(a__      )


      def    a (self       :      Dict				,			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							       =   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							       =   self.truncate(a__				,			a__      )

         return decoded_text


      def    a (self       :      Any				,			a__       :      Any				,			a__       :      Optional[int]      ):





         """simple docstring"""



         def find_re(a__       :      str				,			a__       :      Any				,			a__       :      Any      ):
            __snake_case							       =   pattern.search(a__				,			a__      )
            return m.start() if m else -1

         __snake_case							       =   [re.compile(a__				,			re.MULTILINE      ) for pattern in truncate_before_pattern]

         __snake_case							       =   list(re.finditer('''^print'''				,			a__				,			re.MULTILINE      )      )

         if len(a__      ) > 1:
            __snake_case							       =   completion[: prints[1].start()]

         __snake_case							       =   list(re.finditer('''^def'''				,			a__				,			re.MULTILINE      )      )

         if len(a__      ) > 1:
            __snake_case							       =   completion[: defs[1].start()]

         __snake_case							       =   0

         __snake_case							       =   [
             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








from math import pi






def lowerCamelCase__    (							snake_case_	:  int       ,      snake_case_	:  int       )      ->				float:
   return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
    print(arc_length(90, 10))

