

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


_lowerCAmelCase   :     str    							=     logging.get_logger(__name__)

_lowerCAmelCase   :     Union[str, Any]    							=     {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase   :     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"
        ),
    },
}

_lowerCAmelCase   :     str    							=     {
    "Salesforce/codegen-350M-mono": 2_048,
}






class       __magic_name__       (     __UpperCamelCase ):
			SCREAMING_SNAKE_CASE				  =		VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE				  =		PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE				  =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE				  =		['input_ids', 'attention_mask']
			SCREAMING_SNAKE_CASE				  =		CodeGenTokenizer





			def __init__(						self   ,    __snake_case=None   ,    __snake_case=None   ,    __snake_case=None   ,    __snake_case="<|endoftext|>"   ,    __snake_case="<|endoftext|>"   ,    __snake_case="<|endoftext|>"   ,    __snake_case=False   ,    **__snake_case   ,    )  ->					Any:




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

						__a     =add_prefix_space





			def 						__magic_name__       (						self   ,    *__snake_case   ,    **__snake_case   )  ->					Dict:




						'''simple docstring'''
						__a     =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 						__magic_name__       (						self   ,    *__snake_case   ,    **__snake_case   )  ->					Union[str, Any]:




						'''simple docstring'''
						__a     =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 						__magic_name__       (						self   ,    __snake_case   ,    __snake_case = None   )  ->					Dict:




						'''simple docstring'''
						__a     =self._tokenizer.model.save(__snake_case   ,    name=__snake_case   )
						return tuple(__snake_case   )





			def 						__magic_name__       (						self   ,    __snake_case   ,    __snake_case = False   ,    __snake_case = None   ,    __snake_case = None   ,    **__snake_case   ,    )  ->					Dict:




						'''simple docstring'''
						__a     =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     =self.truncate(__snake_case   ,    __snake_case   )

						return decoded_text





			def 						__magic_name__       (						self   ,    __snake_case   ,    __snake_case   )  ->					Tuple:




						'''simple docstring'''
						def find_re(__snake_case   ,    __snake_case   ,    __snake_case   ):
									__a     =pattern.search(__snake_case   ,    __snake_case   )
									return m.start() if m else -1

						__a     =[re.compile(__snake_case   ,    re.MULTILINE   ) for pattern in truncate_before_pattern]

						__a     =list(re.finditer('^print'   ,    __snake_case   ,    re.MULTILINE   )   )

						if len(__snake_case   ) > 1:
									__a     =completion[: prints[1].start()]

						__a     =list(re.finditer('^def'   ,    __snake_case   ,    re.MULTILINE   )   )

						if len(__snake_case   ) > 1:
									__a     =completion[: defs[1].start()]

						__a     =0

						__a     =[
						    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








def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    if any(not isinstance(__lowerCAmelCase     ,		__lowerCAmelCase				) or x < 0 for x in sequence				):
        raise TypeError("Sequence must be list of non-negative integers"				)
    for _ in range(len(__lowerCAmelCase				)				):
        for i, (rod_upper, rod_lower) in enumerate(zip(__lowerCAmelCase     ,		sequence[1:]				)				):
            if rod_upper > rod_lower:
                sequence[i] -= rod_upper - rod_lower
                sequence[i + 1] += rod_upper - rod_lower
    return sequence


if __name__ == "__main__":
     assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
     assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]



