




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		:				Optional[int]       			=		{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase		:				Tuple       			=		{
    '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		:				Tuple       			=		{
    'Salesforce/codegen-350M-mono': 2048,
}




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Any							 =					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_       :							str							 =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_       :							Any							 =					["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE_       :							Dict							 =					CodeGenTokenizer





				def __init__(      self				:     str					,      __lowerCamelCase				:     Optional[int]=None					,      __lowerCamelCase				:     int=None					,      __lowerCamelCase				:     int=None					,      __lowerCamelCase				:     List[Any]="<|endoftext|>"					,      __lowerCamelCase				:     str="<|endoftext|>"					,      __lowerCamelCase				:     List[Any]="<|endoftext|>"					,      __lowerCamelCase				:     List[Any]=False					,      **__lowerCamelCase				:     Optional[int]					,      )					->     Optional[int]:
							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							):
										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"					,      __lowerCamelCase							) != add_prefix_space:
										a    =			getattr(__lowerCamelCase					,      pre_tok_state.pop("type"							)							)
										a    =			add_prefix_space
										a    =			pre_tok_class(**__lowerCamelCase							)

							a    =			add_prefix_space





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





				def 							__UpperCAmelCase							(      self				:     Optional[int]					,      __lowerCamelCase				:     Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]					,      __lowerCamelCase				:     bool = False					,      __lowerCamelCase				:     bool = None					,      __lowerCamelCase				:     Optional[List[str]] = None					,      **__lowerCamelCase				:     int					,      )					->     str:
							a    =			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:
										a    =			self.truncate(__lowerCamelCase					,      __lowerCamelCase							)

							return decoded_text





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

							a    =			[re.compile(__lowerCamelCase					,      re.MULTILINE							) for pattern in truncate_before_pattern]

							a    =			list(re.finditer("^print"					,      __lowerCamelCase					,      re.MULTILINE							)							)

							if len(__lowerCamelCase							) > 1:
										a    =			completion[: prints[1].start()]

							a    =			list(re.finditer("^def"					,      __lowerCamelCase					,      re.MULTILINE							)							)

							if len(__lowerCamelCase							) > 1:
										a    =			completion[: defs[1].start()]

							a    =			0

							a    =			[
							    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












def 			__magic_name__				( A		:					str   ):






			'''simple docstring'''







			a    =			""
			for ch in key:
						if ch == " " or ch not in key_no_dups and ch.isalpha():
									key_no_dups += ch
			return key_no_dups




def 			__magic_name__				( A		:					str   ):






			'''simple docstring'''







			a    =			[chr(i + 65   ) for i in range(26   )]
			# Remove duplicate characters from key
			a    =			remove_duplicates(key.upper()   )
			a    =			len(A   )
			# First fill cipher with key characters
			a    =			{alphabet[i]: char for i, char in enumerate(A   )}
			# Then map remaining characters in alphabet to
			# the alphabet from the beginning
			for i in range(len(A   ),  26   ):
						a    =			alphabet[i - offset]
						# Ensure we are not mapping letters to letters previously mapped
						while char in key:
									offset -= 1
									a    =			alphabet[i - offset]
						a    =			char
			return cipher_alphabet




def 			__magic_name__				( A		:					str,  A		:					dict[str, str]   ):






			'''simple docstring'''







			return "".join(cipher_map.get(A,  A   ) for ch in message.upper()   )




def 			__magic_name__				( A		:					str,  A		:					dict[str, str]   ):






			'''simple docstring'''







			a    =			{v: k for k, v in cipher_map.items()}
			return "".join(rev_cipher_map.get(A,  A   ) for ch in message.upper()   )




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			input("Enter message to encode or decode: "   ).strip()
			a    =			input("Enter keyword: "   ).strip()
			a    =			input("Encipher or decipher? E/D:"   ).strip()[0].lower()
			try:
						a    =			{"e": encipher, "d": decipher}[option]
			except KeyError:
						raise KeyError("invalid input option"   )
			a    =			create_cipher_map(A   )
			print(func(A,  A   )   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()






