



'''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


snake_case_					:      Optional[int]    			=    logging.get_logger(__name__)

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

snake_case_					:      Dict    			=    {
    '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'
        ),
    },
}

snake_case_					:      Optional[Any]    			=    {
    'Salesforce/codegen-350M-mono': 2048,
}






class 				lowercase__      (				lowercase  ):
	lowercase__    							=     VOCAB_FILES_NAMES
	lowercase__    							=     PRETRAINED_VOCAB_FILES_MAP
	lowercase__    							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase__    							=     ["""input_ids""", """attention_mask"""]
	lowercase__    							=     CodeGenTokenizer





	def __init__(					self   :							int					,lowerCamelCase__   :							Any=None					,lowerCamelCase__   :							Tuple=None					,lowerCamelCase__   :							Optional[int]=None					,lowerCamelCase__   :							List[Any]="<|endoftext|>"					,lowerCamelCase__   :							Tuple="<|endoftext|>"					,lowerCamelCase__   :							Tuple="<|endoftext|>"					,lowerCamelCase__   :							Optional[int]=False					,**lowerCamelCase__   :							List[str]					,):







							'''simple docstring'''
							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__					):
													_UpperCamelCase   :						Dict												=  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.'					)

							_UpperCamelCase   :						List[str]												=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
							if pre_tok_state.get('add_prefix_space'					,lowerCamelCase__					) != add_prefix_space:
													_UpperCamelCase   :						Any												=  getattr(lowerCamelCase__					,pre_tok_state.pop('type'					)					)
													_UpperCamelCase   :						Any												=  add_prefix_space
													_UpperCamelCase   :						int												=  pre_tok_class(**lowerCamelCase__					)

							_UpperCamelCase   :						Any												=  add_prefix_space





	def       UpperCamelCase_    (					self   :							Tuple					,*lowerCamelCase__   :							str					,**lowerCamelCase__   :							Union[str, Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  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       UpperCamelCase_    (					self   :							int					,*lowerCamelCase__   :							Dict					,**lowerCamelCase__   :							Dict					):







							'''simple docstring'''
							_UpperCamelCase   :						List[str]												=  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       UpperCamelCase_    (					self   :							Dict					,lowerCamelCase__   :							str					,lowerCamelCase__   :							Optional[str] = None					):







							'''simple docstring'''
							_UpperCamelCase   :						Any												=  self._tokenizer.model.save(lowerCamelCase__					,name=lowerCamelCase__					)
							return tuple(lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Any					,lowerCamelCase__   :							Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]					,lowerCamelCase__   :							bool = False					,lowerCamelCase__   :							bool = None					,lowerCamelCase__   :							Optional[List[str]] = None					,**lowerCamelCase__   :							List[Any]					,):







							'''simple docstring'''
							_UpperCamelCase   :						Any												=  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:
													_UpperCamelCase   :						Any												=  self.truncate(lowerCamelCase__					,lowerCamelCase__					)

							return decoded_text





	def       UpperCamelCase_    (					self   :							Optional[int]					,lowerCamelCase__   :							Union[str, Any]					,lowerCamelCase__   :							Union[str, Any]					):







							'''simple docstring'''
							def find_re(lowerCamelCase__   :							int					,lowerCamelCase__   :							Optional[Any]					,lowerCamelCase__   :							str					):
													_UpperCamelCase   :						str												=  pattern.search(lowerCamelCase__					,lowerCamelCase__					)
													return m.start() if m else -1

							_UpperCamelCase   :						Tuple												=  [re.compile(lowerCamelCase__					,re.MULTILINE					) for pattern in truncate_before_pattern]

							_UpperCamelCase   :						Optional[int]												=  list(re.finditer('^print'					,lowerCamelCase__					,re.MULTILINE					)					)

							if len(lowerCamelCase__					) > 1:
													_UpperCamelCase   :						List[Any]												=  completion[: prints[1].start()]

							_UpperCamelCase   :						str												=  list(re.finditer('^def'					,lowerCamelCase__					,re.MULTILINE					)					)

							if len(lowerCamelCase__					) > 1:
													_UpperCamelCase   :						Tuple												=  completion[: defs[1].start()]

							_UpperCamelCase   :						str												=  0

							_UpperCamelCase   :						str												=  [
							    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







'''simple docstring'''
import os
from pathlib import Path






def A__       (	):
						from torch.utils.cpp_extension import load

						_UpperCamelCase   :						Optional[Any]												=  Path(UpperCAmelCase_				).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
						_UpperCamelCase   :						Tuple												=  [
						    root / filename
						    for filename in [
						        'vision.cpp',
						        os.path.join('cpu'    ,    'ms_deform_attn_cpu.cpp'				),
						        os.path.join('cuda'    ,    'ms_deform_attn_cuda.cu'				),
						    ]
						]

						load(
						    'MultiScaleDeformableAttention'    ,    UpperCAmelCase_    ,    with_cuda=UpperCAmelCase_    ,    extra_include_paths=[str(UpperCAmelCase_				)]    ,    extra_cflags=['-DWITH_CUDA=1']    ,    extra_cuda_cflags=[
						        '-DCUDA_HAS_FP16=1',
						        '-D__CUDA_NO_HALF_OPERATORS__',
						        '-D__CUDA_NO_HALF_CONVERSIONS__',
						        '-D__CUDA_NO_HALF2_OPERATORS__',
						    ]    ,    )

						import MultiScaleDeformableAttention as MSDA

						return MSDA


