





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


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						   =       {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE_						   =       {
    """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"""
        ),
    },
}

SCREAMING_SNAKE_CASE_						   =       {
    """Salesforce/codegen-350M-mono""": 2_0_4_8,
}

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Optional[Any]			=			VOCAB_FILES_NAMES
     __snake_case    :	Optional[int]			=			PRETRAINED_VOCAB_FILES_MAP
     __snake_case    :	Optional[Any]			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __snake_case    :	Optional[int]			=			["input_ids", "attention_mask"]
     __snake_case    :	str			=			CodeGenTokenizer






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

           SCREAMING_SNAKE_CASE		=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
           if pre_tok_state.get("""add_prefix_space"""						,lowerCamelCase__       ) != add_prefix_space:
                 SCREAMING_SNAKE_CASE		=						getattr(lowerCamelCase__						,pre_tok_state.pop("""type"""       )       )
                 SCREAMING_SNAKE_CASE		=						add_prefix_space
                 SCREAMING_SNAKE_CASE		=						pre_tok_class(**lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						add_prefix_space






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[Any]						,*lowerCamelCase__						:    Dict						,**lowerCamelCase__						:    Optional[Any]       )     ->		BatchEncoding:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						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 				SCREAMING_SNAKE_CASE__  (							self						:    Any						,*lowerCamelCase__						:    int						,**lowerCamelCase__						:    Dict       )     ->		BatchEncoding:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						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 				SCREAMING_SNAKE_CASE__  (							self						:    str						,lowerCamelCase__						:    str						,lowerCamelCase__						:    Optional[str] = None       )     ->		Tuple[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self._tokenizer.model.save(lowerCamelCase__						,name=lowerCamelCase__       )
           return tuple(lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    int						,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]						,)     ->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						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:
                 SCREAMING_SNAKE_CASE		=						self.truncate(lowerCamelCase__						,lowerCamelCase__       )

           return decoded_text






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple						,lowerCamelCase__						:    Optional[int]						,lowerCamelCase__						:    List[str]       )     ->		str:




           '''simple docstring'''


           def find_re(lowerCamelCase__						:    Optional[Any]						,lowerCamelCase__						:    List[str]						,lowerCamelCase__						:    Optional[Any]       ):
                 SCREAMING_SNAKE_CASE		=						pattern.search(lowerCamelCase__						,lowerCamelCase__       )
                 return m.start() if m else -1

           SCREAMING_SNAKE_CASE		=						[re.compile(lowerCamelCase__						,re.MULTILINE       ) for pattern in truncate_before_pattern]

           SCREAMING_SNAKE_CASE		=						list(re.finditer("""^print"""						,lowerCamelCase__						,re.MULTILINE       )       )

           if len(lowerCamelCase__       ) > 1:
                 SCREAMING_SNAKE_CASE		=						completion[: prints[1].start()]

           SCREAMING_SNAKE_CASE		=						list(re.finditer("""^def"""						,lowerCamelCase__						,re.MULTILINE       )       )

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

           SCREAMING_SNAKE_CASE		=						0

           SCREAMING_SNAKE_CASE		=						[
               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 pathlib import Path

import fire



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						Path(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						Path(_SCREAMING_SNAKE_CASE	)
      dest_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE	)
      for path in src_dir.iterdir():
            SCREAMING_SNAKE_CASE		=						[x.rstrip() for x in list(path.open().readlines()	)][:n]
            SCREAMING_SNAKE_CASE		=						dest_dir.joinpath(path.name	)
            print(_SCREAMING_SNAKE_CASE	)
            dest_path.open("""w"""	).write("""\n""".join(_SCREAMING_SNAKE_CASE	)	)


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


