




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

snake_case_ : List[str]    		=       {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

snake_case_ : str    		=       {
    "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_ : Tuple    		=       {
    "Salesforce/codegen-350M-mono": 2048,
}







class __snake_case			(			a		):
   UpperCAmelCase__  :    Optional[int]          =  VOCAB_FILES_NAMES
   UpperCAmelCase__  :    Tuple          =  PRETRAINED_VOCAB_FILES_MAP
   UpperCAmelCase__  :    Dict          =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   UpperCAmelCase__  :    int          =  ['''input_ids''', '''attention_mask''']
   UpperCAmelCase__  :    List[Any]          =  CodeGenTokenizer







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

        """simple docstring"""


        super().__init__(
            _snake_case					,						_snake_case					,						tokenizer_file=_snake_case					,						unk_token=_snake_case					,						bos_token=_snake_case					,						eos_token=_snake_case					,						add_prefix_space=_snake_case					,						**_snake_case					,						)

        if kwargs.pop('''add_bos_token'''					,						_snake_case):
             UpperCAmelCase_          =							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.''')

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

        UpperCAmelCase_          =							add_prefix_space







   def lowerCamelCase						(      self		:						Any					,						*_snake_case		:						Optional[int]					,						**_snake_case		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							kwargs.get('''is_split_into_words'''					,						_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(*_snake_case					,						**_snake_case)







   def lowerCamelCase						(      self		:						int					,						*_snake_case		:						Any					,						**_snake_case		:						Dict):

        """simple docstring"""


        UpperCAmelCase_          =							kwargs.get('''is_split_into_words'''					,						_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(*_snake_case					,						**_snake_case)







   def lowerCamelCase						(      self		:						List[Any]					,						_snake_case		:						str					,						_snake_case		:						Optional[str] = None):

        """simple docstring"""


        UpperCAmelCase_          =							self._tokenizer.model.save(_snake_case					,						name=_snake_case)
        return tuple(_snake_case)







   def lowerCamelCase						(      self		:						Union[str, Any]					,						_snake_case		:						Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]					,						_snake_case		:						bool = False					,						_snake_case		:						bool = None					,						_snake_case		:						Optional[List[str]] = None					,						**_snake_case		:						List[str]					,						):

        """simple docstring"""


        UpperCAmelCase_          =							super().decode(
            token_ids=_snake_case					,						skip_special_tokens=_snake_case					,						clean_up_tokenization_spaces=_snake_case					,						**_snake_case					,						)

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

        return decoded_text







   def lowerCamelCase						(      self		:						Any					,						_snake_case		:						Optional[int]					,						_snake_case		:						List[str]):

        """simple docstring"""


        def find_re(_snake_case		:						Union[str, Any]					,						_snake_case		:						Dict					,						_snake_case		:						List[str]):
             UpperCAmelCase_          =							pattern.search(_snake_case					,						_snake_case)
             return m.start() if m else -1

        UpperCAmelCase_          =							[re.compile(_snake_case					,						re.MULTILINE) for pattern in truncate_before_pattern]

        UpperCAmelCase_          =							list(re.finditer('''^print'''					,						_snake_case					,						re.MULTILINE))

        if len(_snake_case) > 1:
             UpperCAmelCase_          =							completion[: prints[1].start()]

        UpperCAmelCase_          =							list(re.finditer('''^def'''					,						_snake_case					,						re.MULTILINE))

        if len(_snake_case) > 1:
             UpperCAmelCase_          =							completion[: defs[1].start()]

        UpperCAmelCase_          =							0

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

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








"""simple docstring"""





from math import isqrt, loga



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = [True] * max_number
		for i in range(2  ,				isqrt(max_number - 1				) + 1				):
				if is_prime[i]:
						for j in range(i**2  ,				UpperCamelCase_  ,				UpperCamelCase_				):
								__SCREAMING_SNAKE_CASE            = False

		return [i for i in range(2  ,				UpperCamelCase_				) if is_prime[i]]



def 						_lowerCAmelCase				(				UpperCamelCase_ = 80_0800  ,				UpperCamelCase_ = 80_0800				):
		__SCREAMING_SNAKE_CASE            = degree * loga(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = int(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = calculate_prime_numbers(UpperCamelCase_				)

		__SCREAMING_SNAKE_CASE            = 0
		__SCREAMING_SNAKE_CASE            = 0
		__SCREAMING_SNAKE_CASE            = len(UpperCamelCase_				) - 1
		while left < right:
				while (
				    prime_numbers[right] * loga(prime_numbers[left]				)
				    + prime_numbers[left] * loga(prime_numbers[right]				)
				    > upper_bound
				):
						right -= 1
				hybrid_integers_count += right - left
				left += 1

		return hybrid_integers_count


if __name__ == "__main__":
							print(F"""{solution() = }""")




