


"""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__    :							List[Any]            =					logging.get_logger(__name__)

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

a__    :							int            =					{
    '''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__    :							Union[str, Any]            =					{
    '''Salesforce/codegen-350M-mono''': 2_0_4_8,
}




class 			UpperCamelCase_     (			_UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  Tuple    =      VOCAB_FILES_NAMES
	snake_case__    :  int    =      PRETRAINED_VOCAB_FILES_MAP
	snake_case__    :  Dict    =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__    :  Optional[Any]    =      ['input_ids', 'attention_mask']
	snake_case__    :  str    =      CodeGenTokenizer




	def __init__(			self      :    Dict      ,      UpperCAmelCase__      :    Any=None      ,      UpperCAmelCase__      :    List[Any]=None      ,      UpperCAmelCase__      :    Union[str, Any]=None      ,      UpperCAmelCase__      :    Dict="<|endoftext|>"      ,      UpperCAmelCase__      :    Dict="<|endoftext|>"      ,      UpperCAmelCase__      :    List[Any]="<|endoftext|>"      ,      UpperCAmelCase__      :    Tuple=False      ,      **UpperCAmelCase__      :    Union[str, Any]      ,      )	->     Tuple:
						super().__init__(
						    _UpperCAmelCase      ,      _UpperCAmelCase      ,      tokenizer_file=_UpperCAmelCase      ,      unk_token=_UpperCAmelCase      ,      bos_token=_UpperCAmelCase      ,      eos_token=_UpperCAmelCase      ,      add_prefix_space=_UpperCAmelCase      ,      **_UpperCAmelCase      ,      )

						if kwargs.pop("add_bos_token"      ,      _UpperCAmelCase       ):
											__SCREAMING_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."       )

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

						__SCREAMING_SNAKE_CASE						       =    add_prefix_space




	def   UpperCAmelCase_				(			self      :    List[str]      ,      *UpperCAmelCase__      :    Any      ,      **UpperCAmelCase__      :    str       )	->     Tuple:
						__SCREAMING_SNAKE_CASE						       =    kwargs.get("is_split_into_words"      ,      _UpperCAmelCase       )
						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(*_UpperCAmelCase      ,      **_UpperCAmelCase       )




	def   UpperCAmelCase_				(			self      :    int      ,      *UpperCAmelCase__      :    int      ,      **UpperCAmelCase__      :    Union[str, Any]       )	->     str:
						__SCREAMING_SNAKE_CASE						       =    kwargs.get("is_split_into_words"      ,      _UpperCAmelCase       )

						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(*_UpperCAmelCase      ,      **_UpperCAmelCase       )




	def   UpperCAmelCase_				(			self      :    Union[str, Any]      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    Optional[str] = None       )	->     Optional[Any]:
						__SCREAMING_SNAKE_CASE						       =    self._tokenizer.model.save(_UpperCAmelCase      ,      name=_UpperCAmelCase       )
						return tuple(_UpperCAmelCase       )




	def   UpperCAmelCase_				(			self      :    Tuple      ,      UpperCAmelCase__      :    Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]      ,      UpperCAmelCase__      :    bool = False      ,      UpperCAmelCase__      :    bool = None      ,      UpperCAmelCase__      :    Optional[List[str]] = None      ,      **UpperCAmelCase__      :    List[str]      ,      )	->     int:
						__SCREAMING_SNAKE_CASE						       =    super().decode(
						    token_ids=_UpperCAmelCase      ,      skip_special_tokens=_UpperCAmelCase      ,      clean_up_tokenization_spaces=_UpperCAmelCase      ,      **_UpperCAmelCase      ,      )

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

						return decoded_text




	def   UpperCAmelCase_				(			self      :    int      ,      UpperCAmelCase__      :    Tuple      ,      UpperCAmelCase__      :    List[Any]       )	->     Union[str, Any]:
						def find_re(UpperCAmelCase__      :    int      ,      UpperCAmelCase__      :    Union[str, Any]      ,      UpperCAmelCase__      :    int       ):
											__SCREAMING_SNAKE_CASE						       =    pattern.search(_UpperCAmelCase      ,      _UpperCAmelCase       )
											return m.start() if m else -1

						__SCREAMING_SNAKE_CASE						       =    [re.compile(_UpperCAmelCase      ,      re.MULTILINE       ) for pattern in truncate_before_pattern]

						__SCREAMING_SNAKE_CASE						       =    list(re.finditer("^print"      ,      _UpperCAmelCase      ,      re.MULTILINE       )       )

						if len(_UpperCAmelCase       ) > 1:
											__SCREAMING_SNAKE_CASE						       =    completion[: prints[1].start()]

						__SCREAMING_SNAKE_CASE						       =    list(re.finditer("^def"      ,      _UpperCAmelCase      ,      re.MULTILINE       )       )

						if len(_UpperCAmelCase       ) > 1:
											__SCREAMING_SNAKE_CASE						       =    completion[: defs[1].start()]

						__SCREAMING_SNAKE_CASE						       =    0

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

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




"""simple docstring"""







def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    [0 for i in range(r + 1       )]
					# nc0 = 1
					__SCREAMING_SNAKE_CASE						       =    1
					for i in range(1  ,						n + 1       ):
										# to compute current row from previous row.
										__SCREAMING_SNAKE_CASE						       =    min(lowerCAmelCase_  ,						lowerCAmelCase_       )
										while j > 0:
															c[j] += c[j - 1]
															j -= 1
					return c[r]



print(binomial_coefficient(n=1_0, r=5))
