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

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

A__ :       Tuple				= {
    '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__ :       List[Any]				= {
    'Salesforce/codegen-350M-mono': 2_0_4_8,
}


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		VOCAB_FILES_NAMES
       UpperCamelCase_ =		PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase_ =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase_ =		['''input_ids''', '''attention_mask''']
       UpperCamelCase_ =		CodeGenTokenizer
       def __init__(  self			,					A_=None			,					A_=None			,					A_=None			,					A_="<|endoftext|>"			,					A_="<|endoftext|>"			,					A_="<|endoftext|>"			,					A_=False			,					**A_			,					)  ->						Union[str, Any]:





         """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_							):
           _lowercase:      List[Any]   			=  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.'''							)

         _lowercase:      List[str]   			=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
         if pre_tok_state.get('''add_prefix_space'''			,					A_							) != add_prefix_space:
           _lowercase:      Dict   			=  getattr(A_			,					pre_tok_state.pop('''type'''							)							)
           _lowercase:      Optional[int]   			=  add_prefix_space
           _lowercase:      Dict   			=  pre_tok_class(**A_							)

         _lowercase:      Tuple   			=  add_prefix_space
       def 				lowercase_				(  self			,					*A_			,					**A_							)  ->						BatchEncoding:





         """simple docstring"""




         _lowercase:      str   			=  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 				lowercase_				(  self			,					*A_			,					**A_							)  ->						BatchEncoding:





         """simple docstring"""




         _lowercase:      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()._encode_plus(*A_			,					**A_							)
       def 				lowercase_				(  self			,					A_			,					A_ = None							)  ->						Tuple[str]:





         """simple docstring"""




         _lowercase:      Any   			=  self._tokenizer.model.save(A_			,					name=A_							)
         return tuple(A_							)
       def 				lowercase_				(  self			,					A_			,					A_ = False			,					A_ = None			,					A_ = None			,					**A_			,					)  ->						str:





         """simple docstring"""




         _lowercase:      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:
           _lowercase:      List[str]   			=  self.truncate(A_			,					A_							)

         return decoded_text



       def 				lowercase_				(  self			,					A_			,					A_							)  ->						List[Any]:





         """simple docstring"""




         def find_re(A_			,					A_			,					A_							):
           _lowercase:      Any   			=  pattern.search(A_			,					A_							)
           return m.start() if m else -1

         _lowercase:      Union[str, Any]   			=  [re.compile(A_			,					re.MULTILINE							) for pattern in truncate_before_pattern]

         _lowercase:      Union[str, Any]   			=  list(re.finditer('''^print'''			,					A_			,					re.MULTILINE							)							)

         if len(A_							) > 1:
           _lowercase:      Dict   			=  completion[: prints[1].start()]

         _lowercase:      Optional[Any]   			=  list(re.finditer('''^def'''			,					A_			,					re.MULTILINE							)							)

         if len(A_							) > 1:
           _lowercase:      str   			=  completion[: defs[1].start()]

         _lowercase:      Optional[int]   			=  0

         _lowercase:      str   			=  [
             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


"""simple docstring"""




from collections.abc import Callable




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      float   			=  a
  _lowercase:      float   			=  b
  if function(_UpperCamelCase    ) == 0:  # one of the a or b is a root for the function
    return a
  elif function(_UpperCamelCase    ) == 0:
    return b
  elif (
      function(_UpperCamelCase    ) * function(_UpperCamelCase    ) > 0
  ):  # if none of these are root and they are both positive or negative,
    # then this algorithm can't find the root
    raise ValueError('''could not find root in given interval.'''    )
  else:
    _lowercase:      float   			=  start + (end - start) / 2.0
    while abs(start - mid    ) > 10**-7:  # until precisely equals to 10^-7
      if function(_UpperCamelCase    ) == 0:
        return mid
      elif function(_UpperCamelCase    ) * function(_UpperCamelCase    ) < 0:
        _lowercase:      Union[str, Any]   			=  mid
      else:
        _lowercase:      Any   			=  mid
      _lowercase:      List[Any]   			=  start + (end - start) / 2.0
    return mid




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  return x**3 - 2 * x - 5


if __name__ == "__main__":
  print(bisection(f, 1, 1_0_0_0))

  import doctest

  doctest.testmod()
