

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_	:			Tuple  	=      logging.get_logger(__name__)

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

A_	:			Optional[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_	:			Tuple  	=      {
    "Salesforce/codegen-350M-mono": 2048,
}



class    _a						(UpperCamelCase_    ):


					'''simple docstring'''






					UpperCAmelCase__:       List[str]  							=	VOCAB_FILES_NAMES
					UpperCAmelCase__:       Optional[int]  							=	PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:       Tuple  							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:       Any  							=	["""input_ids""", """attention_mask"""]
					UpperCAmelCase__:       Any  							=	CodeGenTokenizer






					def __init__(			self   ,	A__=None   ,	A__=None   ,	A__=None   ,	A__="<|endoftext|>"   ,	A__="<|endoftext|>"   ,	A__="<|endoftext|>"   ,	A__=False   ,	**A__   ,	):
						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      ):
							A__							:   int					       =		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__							:   int					       =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
						if pre_tok_state.get("""add_prefix_space"""   ,	_a      ) != add_prefix_space:
							A__							:   List[Any]					       =		getattr(_a   ,	pre_tok_state.pop("""type"""      )      )
							A__							:   Optional[Any]					       =		add_prefix_space
							A__							:   int					       =		pre_tok_class(**_a      )

						A__							:   Dict					       =		add_prefix_space






					def   __A      (			self   ,	*A__   ,	**A__      ):
						A__							:   Dict					       =		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   ,	*A__   ,	**A__      ):
						A__							:   Optional[Any]					       =		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   ,	A__   ,	A__ = None      ):
						A__							:   Dict					       =		self._tokenizer.model.save(_a   ,	name=_a      )
						return tuple(_a      )






					def   __A      (			self   ,	A__   ,	A__ = False   ,	A__ = None   ,	A__ = None   ,	**A__   ,	):
						A__							:   str					       =		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:
							A__							:   Dict					       =		self.truncate(_a   ,	_a      )

						return decoded_text






					def   __A      (			self   ,	A__   ,	A__      ):


						def find_re(A__   ,	A__   ,	A__      ):
							A__							:   Tuple					       =		pattern.search(_a   ,	_a      )
							return m.start() if m else -1

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

						A__							:   Optional[int]					       =		list(re.finditer("""^print"""   ,	_a   ,	re.MULTILINE      )      )

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

						A__							:   Any					       =		list(re.finditer("""^def"""   ,	_a   ,	re.MULTILINE      )      )

						if len(_a      ) > 1:
							A__							:   int					       =		completion[: defs[1].start()]

						A__							:   List[str]					       =		0

						A__							:   int					       =		[
						    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 __future__ import annotations

def 						UpperCamelCase (lowercase_:			list[int]   ,				lowercase_:			list[int]   ,				lowercase_:			int )					->				tuple[float, list[float]]:
	A__							:   Tuple					       =		list(range(len(lowercase_ ) ) )
	A__							:   Union[str, Any]					       =		[v / w for v, w in zip(lowercase_   ,				lowercase_ )]
	index.sort(key=lambda lowercase_ : ratio[i]   ,				reverse=lowercase_ )

	A__							:   float					       =		0
	A__							:   list[float]					       =		[0] * len(lowercase_ )
	for i in index:
		if weight[i] <= capacity:
			A__							:   Optional[int]					       =		1
			max_value += value[i]
			capacity -= weight[i]
		else:
			A__							:   Union[str, Any]					       =		capacity / weight[i]
			max_value += value[i] * capacity / weight[i]
			break

	return max_value, fractions


if __name__ == "__main__":
	import doctest

	doctest.testmod()



