"""simple docstring"""






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_xglm import XGLMTokenizer
else:
				UpperCamelCase     					=     None


UpperCamelCase     					=     logging.get_logger(__name__)

UpperCamelCase     					=     {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase     					=     {
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json',
    },
}

UpperCamelCase     					=     {
    'facebook/xglm-564M': 20_48,
}
class   UpperCAmelCase__				(    __lowerCamelCase			):


	"""simple docstring"""
	lowerCAmelCase__    :					str						      =							VOCAB_FILES_NAMES
	lowerCAmelCase__    :					str						      =							PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__    :					Tuple						      =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__    :					Any						      =							["""input_ids""", """attention_mask"""]
	lowerCAmelCase__    :					str						      =							XGLMTokenizer







	def __init__(			self       ,     _SCREAMING_SNAKE_CASE=None       ,     _SCREAMING_SNAKE_CASE=None       ,     _SCREAMING_SNAKE_CASE="<s>"       ,     _SCREAMING_SNAKE_CASE="</s>"       ,     _SCREAMING_SNAKE_CASE="</s>"       ,     _SCREAMING_SNAKE_CASE="<s>"       ,     _SCREAMING_SNAKE_CASE="<unk>"       ,     _SCREAMING_SNAKE_CASE="<pad>"       ,     **_SCREAMING_SNAKE_CASE       ,     )    ->	Dict:
							# Compatibility with the original tokenizer
							a_							:			Any     		=					7
							a_							:			Optional[int]     		=					[f'''<madeupword{i}>''' for i in range(self.num_madeup_words )]

							a_							:			Dict     		=					kwargs.get("additional_special_tokens"       ,     [] )
							kwargs["additional_special_tokens"] += [
							    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    _SCREAMING_SNAKE_CASE       ,     tokenizer_file=_SCREAMING_SNAKE_CASE       ,     bos_token=_SCREAMING_SNAKE_CASE       ,     eos_token=_SCREAMING_SNAKE_CASE       ,     sep_token=_SCREAMING_SNAKE_CASE       ,     cls_token=_SCREAMING_SNAKE_CASE       ,     unk_token=_SCREAMING_SNAKE_CASE       ,     pad_token=_SCREAMING_SNAKE_CASE       ,     **_SCREAMING_SNAKE_CASE       ,     )

							a_							:			int     		=					vocab_file
							a_							:			Any     		=					False if not self.vocab_file else True







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE = None )    ->	List[int]:

							if token_ids_a is None:
													return [self.sep_token_id] + token_ids_a
							a_							:			Union[str, Any]     		=					[self.sep_token_id]
							return sep + token_ids_a + sep + sep + token_ids_a







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE = None )    ->	List[int]:
							a_							:			Dict     		=					[self.sep_token_id]

							if token_ids_a is None:
													return len(sep + token_ids_a ) * [0]
							return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE = None )    ->	Tuple[str]:
							if not self.can_save_slow_tokenizer:
													raise ValueError(
													    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
													    "tokenizer." )

							if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
													logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' )
													return
							a_							:			int     		=					os.path.join(
							    _SCREAMING_SNAKE_CASE       ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

							if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ):
													copyfile(self.vocab_file       ,     _SCREAMING_SNAKE_CASE )

							return (out_vocab_file,)





"""simple docstring"""






import numpy as np

UpperCamelCase     					=     [
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]
class   UpperCAmelCase__				:


	"""simple docstring"""







	def __init__(			self )    ->	None:
							a_							:			List[Any]     		=					np.array(_SCREAMING_SNAKE_CASE )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	np.ndarray:
							a_		,					a_							:			List[str]     		=					np.where(letter == self.SQUARE )
							a_							:			Union[str, Any]     		=					np.concatenate([indexa + 1, indexa + 1] )
							return indexes







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	str:
							return self.SQUARE[indexa - 1, indexa - 1]







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	str:
							a_							:			List[str]     		=					message.lower()
							a_							:			Union[str, Any]     		=					message.replace("j"       ,     "i" )

							a_							:			Tuple     		=					""
							for letter_index in range(len(_SCREAMING_SNAKE_CASE ) ):
													if message[letter_index] != " ":
																			a_							:			Dict     		=					self.letter_to_numbers(message[letter_index] )
																			a_							:			str     		=					encoded_message + str(numbers[0] ) + str(numbers[1] )
													elif message[letter_index] == " ":
																			a_							:			Any     		=					encoded_message + " "

							return encoded_message







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	str:
							a_							:			Dict     		=					message.replace(" "       ,     "  " )
							a_							:			str     		=					""
							for numbers_index in range(int(len(_SCREAMING_SNAKE_CASE ) / 2 ) ):
													if message[numbers_index * 2] != " ":
																			a_							:			str     		=					message[numbers_index * 2]
																			a_							:			str     		=					message[numbers_index * 2 + 1]

																			a_							:			Optional[Any]     		=					self.numbers_to_letter(int(_SCREAMING_SNAKE_CASE )       ,     int(_SCREAMING_SNAKE_CASE ) )
																			a_							:			Optional[Any]     		=					decoded_message + letter
													elif message[numbers_index * 2] == " ":
																			a_							:			str     		=					decoded_message + " "

							return decoded_message




