





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""": 2_048,
}



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      int										=VOCAB_FILES_NAMES
  lowerCamelCase   :      Tuple										=PRETRAINED_VOCAB_FILES_MAP
  lowerCamelCase   :      Any										=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCamelCase   :      Tuple										=["input_ids", "attention_mask"]
  lowerCamelCase   :      Dict										=CodeGenTokenizer







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






     """simple docstring"""







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

     __lowerCAmelCase  :      Union[str, Any]    =						add_prefix_space







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							*lowerCAmelCase			:   Union[str, Any]	,							**lowerCAmelCase			:   int   )							->     BatchEncoding:






     """simple docstring"""







     __lowerCAmelCase  :      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 	SCREAMING_SNAKE_CASE   (      self			:   str	,							*lowerCAmelCase			:   Tuple	,							**lowerCAmelCase			:   Dict   )							->     BatchEncoding:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						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 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   Optional[str] = None   )							->     Tuple[str]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self._tokenizer.model.save(lowercase_	,							name=lowercase_   )
     return tuple(lowercase_   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]	,							lowerCAmelCase			:   bool = False	,							lowerCAmelCase			:   bool = None	,							lowerCAmelCase			:   Optional[List[str]] = None	,							**lowerCAmelCase			:   int	,							)							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						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:
        __lowerCAmelCase  :      Tuple    =						self.truncate(lowercase_	,							lowercase_   )

     return decoded_text







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Any   )							->     Any:






     """simple docstring"""







     def find_re(lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   int   ):
        __lowerCAmelCase  :      Any    =						pattern.search(lowercase_	,							lowercase_   )
        return m.start() if m else -1

     __lowerCAmelCase  :      str    =						[re.compile(lowercase_	,							re.MULTILINE   ) for pattern in truncate_before_pattern]

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

     if len(lowercase_   ) > 1:
        __lowerCAmelCase  :      Tuple    =						completion[: prints[1].start()]

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

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

     __lowerCAmelCase  :      Optional[Any]    =						0

     __lowerCAmelCase  :      Optional[int]    =						[
         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 pathlib import Path

import fire





def      snake_case_				(__A					:						str ,       __A					:						str ,       __A					:						int					)      ->     Any:
   __lowerCAmelCase  :      Tuple    =						Path(__A					)
   __lowerCAmelCase  :      Tuple    =						Path(__A					)
   dest_dir.mkdir(exist_ok=__A					)
   for path in src_dir.iterdir():
      __lowerCAmelCase  :      str    =						[x.rstrip() for x in list(path.open().readlines()					)][:n]
      __lowerCAmelCase  :      Dict    =						dest_dir.joinpath(path.name					)
      print(__A					)
      dest_path.open("""w"""					).write("""\n""".join(__A					)					)


if __name__ == "__main__":
   fire.Fire(minify)


