



"""simple docstring"""






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


_lowercase				:				Dict     =		logging.get_logger(__name__)

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

_lowercase				:				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"
        ),
    },
}

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

class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





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



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

								lowerCamelCase__	:	List[Any]				=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
								if pre_tok_state.get('''add_prefix_space''',						lowerCamelCase  ) != add_prefix_space:
											lowerCamelCase__	:	Any				=getattr(lowerCamelCase,						pre_tok_state.pop('''type'''  )  )
											lowerCamelCase__	:	Union[str, Any]				=add_prefix_space
											lowerCamelCase__	:	Optional[Any]				=pre_tok_class(**lowerCamelCase  )

								lowerCamelCase__	:	Tuple				=add_prefix_space



					def 	snake_case ( self							:						Optional[Any],						*lowerCamelCase							:						Optional[Any],						**lowerCamelCase							:						List[Any]  )->      BatchEncoding:
								lowerCamelCase__	:	List[Any]				=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 	snake_case ( self							:						int,						*lowerCamelCase							:						Dict,						**lowerCamelCase							:						int  )->      BatchEncoding:
								lowerCamelCase__	:	Any				=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 	snake_case ( self							:						Dict,						lowerCamelCase							:						str,						lowerCamelCase							:						Optional[str] = None  )->      Tuple[str]:
								lowerCamelCase__	:	Dict				=self._tokenizer.model.save(lowerCamelCase,						name=lowerCamelCase  )
								return tuple(lowerCamelCase  )



					def 	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							:						Tuple,						)->      str:
								lowerCamelCase__	:	Tuple				=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:
											lowerCamelCase__	:	List[str]				=self.truncate(lowerCamelCase,						lowerCamelCase  )

								return decoded_text




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

								lowerCamelCase__	:	int				=[re.compile(lowerCamelCase,						re.MULTILINE  ) for pattern in truncate_before_pattern]

								lowerCamelCase__	:	Union[str, Any]				=list(re.finditer('''^print''',						lowerCamelCase,						re.MULTILINE  )  )

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

								lowerCamelCase__	:	Dict				=list(re.finditer('''^def''',						lowerCamelCase,						re.MULTILINE  )  )

								if len(lowerCamelCase  ) > 1:
											lowerCamelCase__	:	Union[str, Any]				=completion[: defs[1].start()]

								lowerCamelCase__	:	str				=0

								lowerCamelCase__	:	Union[str, Any]				=[
								    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









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					list[list[int]]							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					list[int]			):
			"""simple docstring"""

			# 1. Validate that path exists between current and next vertices
			if graph[path[curr_ind - 1]][next_ver] == 0:
						return False

			# 2. Validate that next vertex is not already in path
			return not any(vertex == next_ver for vertex in path			)






def   snake_case__     (	__lowerCamelCase       :					list[list[int]]							,	__lowerCamelCase       :					list[int]							,	__lowerCamelCase       :					int			):
			"""simple docstring"""

			# Base Case
			if curr_ind == len(__lowerCamelCase			):
						# return whether path exists between current and starting vertices
						return graph[path[curr_ind - 1]][path[0]] == 1

			# Recursive Step
			for next_ver in range(0							,	len(__lowerCamelCase			)			):
						if valid_connection(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			):
									# Insert current vertex  into path as next transition
									lowerCamelCase__	:	Tuple				=next_ver
									# Validate created path
									if util_hamilton_cycle(__lowerCamelCase							,	__lowerCamelCase							,	curr_ind + 1			):
												return True
									# Backtrack
									lowerCamelCase__	:	int				=-1
			return False






def   snake_case__     (	__lowerCamelCase       :					list[list[int]]							,	__lowerCamelCase       :					int = 0			):
			"""simple docstring"""
			lowerCamelCase__	:	Tuple				=[-1] * (len(__lowerCamelCase			) + 1)
			# initialize start and end of path with starting index
			lowerCamelCase__	:	Union[str, Any]				=start_index
			# evaluate and if we find answer return path either return empty array
			return path if util_hamilton_cycle(__lowerCamelCase							,	__lowerCamelCase							,	1			) else []




