






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


a		:       Optional[Any]			     = logging.get_logger(__name__)

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

a		:       List[str]			     = {
    "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"
        ),
    },
}

a		:       str			     = {
    "Salesforce/codegen-350M-mono": 2_048,
}
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : List[str]							       =	VOCAB_FILES_NAMES
      a  : Any							       =	PRETRAINED_VOCAB_FILES_MAP
      a  : Tuple							       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a  : List[str]							       =	['input_ids', 'attention_mask']
      a  : List[str]							       =	CodeGenTokenizer
      def __init__(      self					:			Dict		,      __lowercase					:			Union[str, Any]=None		,      __lowercase					:			str=None		,      __lowercase					:			Dict=None		,      __lowercase					:			Tuple="<|endoftext|>"		,      __lowercase					:			Dict="<|endoftext|>"		,      __lowercase					:			List[str]="<|endoftext|>"		,      __lowercase					:			Optional[int]=False		,      **__lowercase					:			Any		,      )							->      Union[str, 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					):
          __UpperCAmelCase							:	List[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."""					)

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

        __UpperCAmelCase							:	str           =  add_prefix_space
      def 	UpperCAmelCase      (      self					:			str		,      *__lowercase					:			Optional[Any]		,      **__lowercase					:			str					)							->      BatchEncoding:
        __UpperCAmelCase							:	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					:			Dict		,      *__lowercase					:			Any		,      **__lowercase					:			Any					)							->      BatchEncoding:
        __UpperCAmelCase							:	List[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					:			Tuple		,      __lowercase					:			str		,      __lowercase					:			Optional[str] = None					)							->      Tuple[str]:
        __UpperCAmelCase							:	List[str]           =  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					:			Union[str, Any]		,      )							->      str:
        __UpperCAmelCase							:	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:
          __UpperCAmelCase							:	List[str]           =  self.truncate(__lowercase		,      __lowercase					)

        return decoded_text




      def 	UpperCAmelCase      (      self					:			int		,      __lowercase					:			List[Any]		,      __lowercase					:			Tuple					)							->      List[Any]:
        def find_re(__lowercase					:			Optional[int]		,      __lowercase					:			int		,      __lowercase					:			Optional[Any]					):
          __UpperCAmelCase							:	str           =  pattern.search(__lowercase		,      __lowercase					)
          return m.start() if m else -1

        __UpperCAmelCase							:	Dict           =  [re.compile(__lowercase		,      re.MULTILINE					) for pattern in truncate_before_pattern]

        __UpperCAmelCase							:	List[Any]           =  list(re.finditer("""^print"""		,      __lowercase		,      re.MULTILINE					)					)

        if len(__lowercase					) > 1:
          __UpperCAmelCase							:	Tuple           =  completion[: prints[1].start()]

        __UpperCAmelCase							:	Union[str, Any]           =  list(re.finditer("""^def"""		,      __lowercase		,      re.MULTILINE					)					)

        if len(__lowercase					) > 1:
          __UpperCAmelCase							:	Any           =  completion[: defs[1].start()]

        __UpperCAmelCase							:	Optional[int]           =  0

        __UpperCAmelCase							:	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










def 				lowerCamelCase__      (    __lowerCamelCase			:     Tuple  ,		__lowerCamelCase			:     Union[str, Any]		):
  __UpperCAmelCase							:	Tuple           =  [1]
  for i in range(2  ,		__lowerCamelCase		):
    factorials.append(factorials[-1] * i		)
  assert 0 <= k < factorials[-1] * n, "k out of bounds"

  __UpperCAmelCase							:	Optional[Any]           =  []
  __UpperCAmelCase							:	str           =  list(range(__lowerCamelCase		)		)

  # Find permutation
  while factorials:
    __UpperCAmelCase							:	Any           =  factorials.pop()
    __UpperCAmelCase      , __UpperCAmelCase							:	Optional[Any]           =  divmod(__lowerCamelCase  ,		__lowerCamelCase		)
    permutation.append(elements[number]		)
    elements.remove(elements[number]		)
  permutation.append(elements[0]		)

  return permutation


if __name__ == "__main__":
       import doctest

       doctest.testmod()


