"""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        _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					:	str							,    UpperCAmelCase					:	Optional[int]=None							,    UpperCAmelCase					:	int=None							,    UpperCAmelCase					:	int=None							,    UpperCAmelCase					:	List[Any]="<|endoftext|>"							,    UpperCAmelCase					:	str="<|endoftext|>"							,    UpperCAmelCase					:	List[Any]="<|endoftext|>"							,    UpperCAmelCase					:	List[Any]=False							,    **UpperCAmelCase					:	Optional[int]							,    ):
													super().__init__(
													    __lowerCamelCase							,    __lowerCamelCase							,    tokenizer_file=__lowerCamelCase							,    unk_token=__lowerCamelCase							,    bos_token=__lowerCamelCase							,    eos_token=__lowerCamelCase							,    add_prefix_space=__lowerCamelCase							,    **__lowerCamelCase							,    )

													if kwargs.pop("add_bos_token"							,    __lowerCamelCase			):
																				__lowerCamelCase    :						Optional[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."			)

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

													__lowerCamelCase    :						Any						      =   add_prefix_space






						def 					lowerCamelCase__   (				self					:	Dict							,    *UpperCAmelCase					:	str							,    **UpperCAmelCase					:	Optional[int]			):
													__lowerCamelCase    :						Union[str, Any]						      =   kwargs.get("is_split_into_words"							,    __lowerCamelCase			)
													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(*__lowerCamelCase							,    **__lowerCamelCase			)






						def 					lowerCamelCase__   (				self					:	Union[str, Any]							,    *UpperCAmelCase					:	List[str]							,    **UpperCAmelCase					:	List[str]			):
													__lowerCamelCase    :						List[Any]						      =   kwargs.get("is_split_into_words"							,    __lowerCamelCase			)

													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(*__lowerCamelCase							,    **__lowerCamelCase			)






						def 					lowerCamelCase__   (				self					:	List[Any]							,    UpperCAmelCase					:	str							,    UpperCAmelCase					:	Optional[str] = None			):
													__lowerCamelCase    :						List[str]						      =   self._tokenizer.model.save(__lowerCamelCase							,    name=__lowerCamelCase			)
													return tuple(__lowerCamelCase			)






						def 					lowerCamelCase__   (				self					:	Optional[int]							,    UpperCAmelCase					:	Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]							,    UpperCAmelCase					:	bool = False							,    UpperCAmelCase					:	bool = None							,    UpperCAmelCase					:	Optional[List[str]] = None							,    **UpperCAmelCase					:	int							,    ):
													__lowerCamelCase    :						List[str]						      =   super().decode(
													    token_ids=__lowerCamelCase							,    skip_special_tokens=__lowerCamelCase							,    clean_up_tokenization_spaces=__lowerCamelCase							,    **__lowerCamelCase							,    )

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

													return decoded_text







						def 					lowerCamelCase__   (				self					:	List[Any]							,    UpperCAmelCase					:	Any							,    UpperCAmelCase					:	List[str]			):
													def find_re(UpperCAmelCase					:	Union[str, Any]							,    UpperCAmelCase					:	Union[str, Any]							,    UpperCAmelCase					:	Tuple			):
																				__lowerCamelCase    :						int						      =   pattern.search(__lowerCamelCase							,    __lowerCamelCase			)
																				return m.start() if m else -1

													__lowerCamelCase    :						Dict						      =   [re.compile(__lowerCamelCase							,    re.MULTILINE			) for pattern in truncate_before_pattern]

													__lowerCamelCase    :						List[str]						      =   list(re.finditer("^print"							,    __lowerCamelCase							,    re.MULTILINE			)			)

													if len(__lowerCamelCase			) > 1:
																				__lowerCamelCase    :						Optional[Any]						      =   completion[: prints[1].start()]

													__lowerCamelCase    :						Union[str, Any]						      =   list(re.finditer("^def"							,    __lowerCamelCase							,    re.MULTILINE			)			)

													if len(__lowerCamelCase			) > 1:
																				__lowerCamelCase    :						List[str]						      =   completion[: defs[1].start()]

													__lowerCamelCase    :						Optional[int]						      =   0

													__lowerCamelCase    :						Optional[int]						      =   [
													    pos for pos in [find_re(__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase			) for terminal in terminals] if pos != -1
													]

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





from __future__ import annotations




def 			__magic_name__				( A		:					list   ):






			'''simple docstring'''







			if len(A   ) == 0:
						return []
			a    ,		a    =			min(A   ), max(A   )
			a    =			int(max_value - min_value   ) + 1
			a    =			[[] for _ in range(A   )]

			for i in my_list:
						buckets[int(i - min_value   )].append(A   )

			return [v for bucket in buckets for v in sorted(A   )]


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
		assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]






