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


UpperCamelCase  							=						logging.get_logger(__name__)

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

UpperCamelCase  							=						{
    '''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'''
        ),
    },
}

UpperCamelCase  							=						{
    '''Salesforce/codegen-350M-mono''': 2048,
}



class   snake_case_       (		__A       ):
   __A	:		Optional[Any]         =				VOCAB_FILES_NAMES
   __A	:		Any         =				PRETRAINED_VOCAB_FILES_MAP
   __A	:		List[str]         =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A	:		List[str]         =				["input_ids", "attention_mask"]
   __A	:		Dict         =				CodeGenTokenizer






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

       if kwargs.pop("add_bos_token"	,						lowercase_  ):
           lowercase__ :   Optional[Any]    					=				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."  )

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

       lowercase__ :   str    					=				add_prefix_space






   def       __UpperCamelCase     (      self				:     List[Any]	,						*lowercase_				:     Any	,						**lowercase_				:     Dict  )					->						BatchEncoding:
       lowercase__ :   str    					=				kwargs.get("is_split_into_words"	,						lowercase_  )
       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(*lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     List[Any]	,						*lowercase_				:     Dict	,						**lowercase_				:     Any  )					->						BatchEncoding:
       lowercase__ :   str    					=				kwargs.get("is_split_into_words"	,						lowercase_  )

       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(*lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     Union[str, Any]	,						lowercase_				:     str	,						lowercase_				:     Optional[str] = None  )					->						Tuple[str]:
       lowercase__ :   List[Any]    					=				self._tokenizer.model.save(lowercase_	,						name=lowercase_  )
       return tuple(lowercase_  )






   def       __UpperCamelCase     (      self				:     List[Any]	,						lowercase_				:     Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]	,						lowercase_				:     bool = False	,						lowercase_				:     bool = None	,						lowercase_				:     Optional[List[str]] = None	,						**lowercase_				:     Optional[int]	,						)					->						str:
       lowercase__ :   Dict    					=				super().decode(
           token_ids=lowercase_	,						skip_special_tokens=lowercase_	,						clean_up_tokenization_spaces=lowercase_	,						**lowercase_	,						)

       if truncate_before_pattern is not None and len(lowercase_  ) > 0:
           lowercase__ :   Dict    					=				self.truncate(lowercase_	,						lowercase_  )

       return decoded_text







   def       __UpperCamelCase     (      self				:     Dict	,						lowercase_				:     str	,						lowercase_				:     Any  )					->						Optional[int]:
       def find_re(lowercase_				:     Union[str, Any]	,						lowercase_				:     List[str]	,						lowercase_				:     Optional[Any]  ):
           lowercase__ :   Any    					=				pattern.search(lowercase_	,						lowercase_  )
           return m.start() if m else -1

       lowercase__ :   List[Any]    					=				[re.compile(lowercase_	,						re.MULTILINE  ) for pattern in truncate_before_pattern]

       lowercase__ :   Optional[Any]    					=				list(re.finditer("^print"	,						lowercase_	,						re.MULTILINE  )  )

       if len(lowercase_  ) > 1:
           lowercase__ :   int    					=				completion[: prints[1].start()]

       lowercase__ :   Optional[int]    					=				list(re.finditer("^def"	,						lowercase_	,						re.MULTILINE  )  )

       if len(lowercase_  ) > 1:
           lowercase__ :   Union[str, Any]    					=				completion[: defs[1].start()]

       lowercase__ :   Tuple    					=				0

       lowercase__ :   Union[str, Any]    					=				[
           pos for pos in [find_re(lowercase_	,						lowercase_	,						lowercase_  ) for terminal in terminals] if pos != -1
       ]

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






from abc import ABC, abstractmethod
from argparse import ArgumentParser







class  lowercase				(       lowercase_			):




				@staticmethod
				@abstractmethod
				def     a							(					snake_case       ):
											raise NotImplementedError()




				@abstractmethod
				def     a							(					self       ):
											raise NotImplementedError()




