






"""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_      =							logging.get_logger(__name__)

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

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






class 							__SCREAMING_SNAKE_CASE  (			UpperCamelCase						):
							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 :  List[str]     ,      snake_case :  Optional[int]=None     ,      snake_case :  List[Any]=None     ,      snake_case :  Union[str, Any]=None     ,      snake_case :  Tuple="<|endoftext|>"     ,      snake_case :  Optional[int]="<|endoftext|>"     ,      snake_case :  int="<|endoftext|>"     ,      snake_case :  Dict=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							):
																			A__  :					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."""							)

													A__  :					Optional[Any]											=					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
													if pre_tok_state.get("""add_prefix_space"""     ,      snake_case							) != add_prefix_space:
																			A__  :					int											=					getattr(snake_case     ,      pre_tok_state.pop("""type"""							)							)
																			A__  :					Optional[Any]											=					add_prefix_space
																			A__  :					Any											=					pre_tok_class(**snake_case							)

													A__  :					Dict											=					add_prefix_space


							def   _UpperCamelCase    (     self :  int     ,      *snake_case :  Dict     ,      **snake_case :  Dict							):
													'''simple docstring'''





													A__  :					Dict											=					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   _UpperCamelCase    (     self :  Dict     ,      *snake_case :  Optional[int]     ,      **snake_case :  Any							):
													'''simple docstring'''





													A__  :					Any											=					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   _UpperCamelCase    (     self :  Dict     ,      snake_case :  str     ,      snake_case :  Optional[str] = None							):
													'''simple docstring'''





													A__  :					List[str]											=					self._tokenizer.model.save(snake_case     ,      name=snake_case							)
													return tuple(snake_case							)


							def   _UpperCamelCase    (     self :  Optional[int]     ,      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 :  int     ,      ):
													'''simple docstring'''





													A__  :					List[Any]											=					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:
																			A__  :					Optional[int]											=					self.truncate(snake_case     ,      snake_case							)

													return decoded_text


							def   _UpperCamelCase    (     self :  str     ,      snake_case :  Any     ,      snake_case :  Tuple							):
													'''simple docstring'''





													def find_re(snake_case :  Any     ,      snake_case :  Tuple     ,      snake_case :  Optional[int]							):
																			A__  :					Tuple											=					pattern.search(snake_case     ,      snake_case							)
																			return m.start() if m else -1

													A__  :					Any											=					[re.compile(snake_case     ,      re.MULTILINE							) for pattern in truncate_before_pattern]

													A__  :					Dict											=					list(re.finditer("""^print"""     ,      snake_case     ,      re.MULTILINE							)							)

													if len(snake_case							) > 1:
																			A__  :					str											=					completion[: prints[1].start()]

													A__  :					List[Any]											=					list(re.finditer("""^def"""     ,      snake_case     ,      re.MULTILINE							)							)

													if len(snake_case							) > 1:
																			A__  :					Union[str, Any]											=					completion[: defs[1].start()]

													A__  :					List[str]											=					0

													A__  :					Optional[int]											=					[
													    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 collections import defaultdict
from math import gcd




def        _lowerCAmelCase						(							UpperCAmelCase__    :   int = 1_5_0_0_0_0_0 )						->int:
						A__  :					defaultdict											=					defaultdict(UpperCAmelCase__ )
						A__  :					Any											=					2
						while 2 * euclid_m * (euclid_m + 1) <= limit:
												for euclid_n in range((euclid_m % 2) + 1,  UpperCAmelCase__,  2 ):
																		if gcd(UpperCAmelCase__,  UpperCAmelCase__ ) > 1:
																								continue
																		A__  :					str											=					2 * euclid_m * (euclid_m + euclid_n)
																		for perimeter in range(UpperCAmelCase__,  limit + 1,  UpperCAmelCase__ ):
																								frequencies[perimeter] += 1
												euclid_m += 1
						return sum(1 for frequency in frequencies.values() if frequency == 1 )


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






