




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


_lowerCamelCase	:		Dict							       =   logging.get_logger(__name__)

_lowerCamelCase	:		List[str]							       =   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowerCamelCase	:		Optional[int]							       =   {
    """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"""
        ),
    },
}

_lowerCamelCase	:		List[Any]							       =   {
    """Salesforce/codegen-350M-mono""": 2048,
}


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      VOCAB_FILES_NAMES
    UpperCAmelCase__							  =      PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__							  =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__							  =      ['''input_ids''', '''attention_mask''']
    UpperCAmelCase__							  =      CodeGenTokenizer


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





           '''simple docstring'''

           super().__init__(
               UpperCAmelCase__					, UpperCAmelCase__					, tokenizer_file=UpperCAmelCase__					, unk_token=UpperCAmelCase__					, bos_token=UpperCAmelCase__					, eos_token=UpperCAmelCase__					, add_prefix_space=UpperCAmelCase__					, **UpperCAmelCase__					, )

           if kwargs.pop('''add_bos_token'''					, UpperCAmelCase__):
                  A__       =    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.''')

           A__       =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
           if pre_tok_state.get('''add_prefix_space'''					, UpperCAmelCase__) != add_prefix_space:
                  A__       =    getattr(UpperCAmelCase__					, pre_tok_state.pop('''type'''))
                  A__       =    add_prefix_space
                  A__       =    pre_tok_class(**UpperCAmelCase__)

           A__       =    add_prefix_space


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple					, *UpperCAmelCase__      :	int					, **UpperCAmelCase__      :	Optional[int])						->BatchEncoding:





           '''simple docstring'''

           A__       =    kwargs.get('''is_split_into_words'''					, UpperCAmelCase__)
           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(*UpperCAmelCase__					, **UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Any					, *UpperCAmelCase__      :	List[str]					, **UpperCAmelCase__      :	Dict)						->BatchEncoding:





           '''simple docstring'''

           A__       =    kwargs.get('''is_split_into_words'''					, UpperCAmelCase__)

           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(*UpperCAmelCase__					, **UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Any					, UpperCAmelCase__      :	str					, UpperCAmelCase__      :	Optional[str] = None)						->Tuple[str]:





           '''simple docstring'''

           A__       =    self._tokenizer.model.save(UpperCAmelCase__					, name=UpperCAmelCase__)
           return tuple(UpperCAmelCase__)


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





           '''simple docstring'''

           A__       =    super().decode(
               token_ids=UpperCAmelCase__					, skip_special_tokens=UpperCAmelCase__					, clean_up_tokenization_spaces=UpperCAmelCase__					, **UpperCAmelCase__					, )

           if truncate_before_pattern is not None and len(UpperCAmelCase__) > 0:
                  A__       =    self.truncate(UpperCAmelCase__					, UpperCAmelCase__)

           return decoded_text





    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	List[str]					, UpperCAmelCase__      :	Optional[int])						->Tuple:





           '''simple docstring'''

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

           A__       =    [re.compile(UpperCAmelCase__					, re.MULTILINE) for pattern in truncate_before_pattern]

           A__       =    list(re.finditer('''^print'''					, UpperCAmelCase__					, re.MULTILINE))

           if len(UpperCAmelCase__) > 1:
                  A__       =    completion[: prints[1].start()]

           A__       =    list(re.finditer('''^def'''					, UpperCAmelCase__					, re.MULTILINE))

           if len(UpperCAmelCase__) > 1:
                  A__       =    completion[: defs[1].start()]

           A__       =    0

           A__       =    [
               pos for pos in [find_re(UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__) for terminal in terminals] if pos != -1
           ]

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








def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Any:



       """simple docstring"""

       A__       =    [0] * len(lowercase_						)
       A__       =    []
       A__       =    [1] * len(lowercase_						)

       for values in graph.values():
              for i in values:
                     indegree[i] += 1

       for i in range(len(lowercase_						)						):
              if indegree[i] == 0:
                     queue.append(lowercase_						)

       while queue:
              A__       =    queue.pop(0						)
              for x in graph[vertex]:
                     indegree[x] -= 1

                     if long_dist[vertex] + 1 > long_dist[x]:
                            A__       =    long_dist[vertex] + 1

                     if indegree[x] == 0:
                            queue.append(lowercase_						)

       print(max(lowercase_						)						)


# Adjacency list of Graph
_lowerCamelCase	:		Optional[int]							       =   {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)


