
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


__magic_name__  							=	logging.get_logger(__name__)

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

__magic_name__  							=	{
    '''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'''
        ),
    },
}

__magic_name__  							=	{
    '''Salesforce/codegen-350M-mono''': 2_048,
}




class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		str     						=  VOCAB_FILES_NAMES
      _A    :		List[Any]     						=  PRETRAINED_VOCAB_FILES_MAP
      _A    :		Union[str, Any]     						=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _A    :		List[str]     						=  ['input_ids', 'attention_mask']
      _A    :		Tuple     						=  CodeGenTokenizer
      def __init__(      self		,						lowerCamelCase=None		,						lowerCamelCase=None		,						lowerCamelCase=None		,						lowerCamelCase="<|endoftext|>"		,						lowerCamelCase="<|endoftext|>"		,						lowerCamelCase="<|endoftext|>"		,						lowerCamelCase=False		,						**lowerCamelCase		,						):
          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			):
              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."			)

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

          snake_case__    =					add_prefix_space
      def A_      (      self		,						*lowerCamelCase		,						**lowerCamelCase			):
          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 A_      (      self		,						*lowerCamelCase		,						**lowerCamelCase			):
          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 A_      (      self		,						lowerCamelCase		,						lowerCamelCase = None			):
          snake_case__    =					self._tokenizer.model.save(lowerCamelCase		,						name=lowerCamelCase			)
          return tuple(lowerCamelCase			)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase = False		,						lowerCamelCase = None		,						lowerCamelCase = None		,						**lowerCamelCase		,						):
          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:
              snake_case__    =					self.truncate(lowerCamelCase		,						lowerCamelCase			)

          return decoded_text



      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase			):
          def find_re(lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
              snake_case__    =					pattern.search(lowerCamelCase		,						lowerCamelCase			)
              return m.start() if m else -1

          snake_case__    =					[re.compile(lowerCamelCase		,						re.MULTILINE			) for pattern in truncate_before_pattern]

          snake_case__    =					list(re.finditer("^print"		,						lowerCamelCase		,						re.MULTILINE			)			)

          if len(lowerCamelCase			) > 1:
              snake_case__    =					completion[: prints[1].start()]

          snake_case__    =					list(re.finditer("^def"		,						lowerCamelCase		,						re.MULTILINE			)			)

          if len(lowerCamelCase			) > 1:
              snake_case__    =					completion[: defs[1].start()]

          snake_case__    =					0

          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 __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

__magic_name__  							=	TypeVar('''T''')




class        _SCREAMING_SNAKE_CASE	(   Generic[T]			):
      def __init__(      self		,						lowerCamelCase			):
          snake_case__    =					data
          snake_case__    =					None



      def __str__(      self			):
          return F"""{self.data}"""





class        _SCREAMING_SNAKE_CASE	(   Generic[T]			):
      def __init__(      self			):
          snake_case__    =					None
      def __iter__(      self			):
          snake_case__    =					self.top
          while node:
              yield node.data
              snake_case__    =					node.next
      def __str__(      self			):
          return "->".join([str(lowerCamelCase			) for item in self]			)
      def __len__(      self			):
          return len(tuple(iter(self			)			)			)
      def A_      (      self			):
          return self.top is None
      def A_      (      self		,						lowerCamelCase			):
          snake_case__    =					Node(lowerCamelCase			)
          if not self.is_empty():
              snake_case__    =					self.top
          snake_case__    =					node
      def A_      (      self			):
          if self.is_empty():
              raise IndexError("pop from empty stack"			)
          assert isinstance(self.top		,						lowerCamelCase			)
          snake_case__    =					self.top
          snake_case__    =					self.top.next
          return pop_node.data
      def A_      (      self			):
          if self.is_empty():
              raise IndexError("peek from empty stack"			)

          assert self.top is not None
          return self.top.data



      def A_      (      self			):
          snake_case__    =					None


if __name__ == "__main__":
     from doctest import testmod

     testmod()



