


"""simple docstring"""


import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
	import torch


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation


UpperCamelCase						:				Tuple             =      logging.get_logger(__name__)
UpperCamelCase						:				Optional[int]             =      {"vocab_file": "spiece.model"}

UpperCamelCase						:				Tuple             =      {
    "vocab_file": {
        "AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
    }
}

UpperCamelCase						:				str             =      {
    "AI-Sweden/gpt-sw3-126m": 2_048,
    "AI-Sweden/gpt-sw3-350m": 2_048,
    "AI-Sweden/gpt-sw3-1.6b": 2_048,
    "AI-Sweden/gpt-sw3-6.7b": 2_048,
    "AI-Sweden/gpt-sw3-20b": 2_048,
}



class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     VOCAB_FILES_NAMES
						lowerCAmelCase       =     PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase       =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase       =     ["""input_ids""", """attention_mask"""]







						def __init__(							self   :    Optional[Any]			,			_lowercase   :    Union[str, Any]			,			_lowercase   :    Optional[int]=False			,			_lowercase   :    Union[str, Any]=False			,			_lowercase   :    Union[str, Any]=False			,			_lowercase   :    Dict=None			,			_lowercase   :    Optional[Any]=None			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Any=None			,			_lowercase   :    Optional[Dict[str, Any]] = None			,			**_lowercase   :    Any			,			):
								A					       = {} if sp_model_kwargs is None else sp_model_kwargs

								A					       = kwargs.get('name_or_path'		)
								if name_or_path is None:
										logger.warning(
										    'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
										    ' you are testing the model, this can safely be ignored'		)
										A					       = 'None'

								# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
								A					       = '<|endoftext|>' if eos_token is None else eos_token
								A					       = '<unk>' if unk_token is None else unk_token
								if "gpt-sw3-7b" in name_or_path:
										A					       = unk_token if pad_token is None else pad_token
										A					       = eos_token if bos_token is None else bos_token
								else:
										A					       = '<pad>' if pad_token is None else pad_token
										A					       = '<s>' if bos_token is None else bos_token

								super().__init__(
								    do_lower_case=_lowercase			,			remove_space=_lowercase			,			keep_accents=_lowercase			,			bos_token=_lowercase			,			eos_token=_lowercase			,			unk_token=_lowercase			,			pad_token=_lowercase			,			sp_model_kwargs=self.sp_model_kwargs			,			**_lowercase			,			)

								A					       = do_lower_case
								A					       = remove_space
								A					       = keep_accents
								A					       = vocab_file

								A					       = spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(_lowercase		)

								# Used for whitespace normalization in input texts
								# fmt : off
								A					       = {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
								# fmt : on

								# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
								A					       = re.compile(
								    f'[{"".join(map(_lowercase			,			list(range(0			,			9		)		) + list(range(11			,			32		)		) + list(range(127			,			160		)		) + [160, 173, 8_203]		)		)}]'		)







						def __getstate__(							self   :    Any		):
								A					       = self.__dict__.copy()
								A					       = None
								return state







						def __setstate__(							self   :    Tuple			,			_lowercase   :    Tuple		):
								A					       = d

								# for backward compatibility
								if not hasattr(self			,			'sp_model_kwargs'		):
										A					       = {}

								A					       = spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(self.vocab_file		)







						@property
						# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
						def __a						(							self   :    Union[str, Any]		):
								return len(self.sp_model		)







						def __a						(							self   :    Any			,			_lowercase   :    str		):
								A					       = self.non_printing_characters_re.sub(''			,			_lowercase		)

								# Normalize whitespaces
								A					       = ''.join([char if char not in self.whitespaces else ' ' for char in text]		)

								# NFC Unicode normalization
								A					       = unicodedata.normalize('NFC'			,			_lowercase		)
								return text







						def __a						(							self   :    Dict			,			_lowercase   :    str			,			**_lowercase   :    Union[str, Any]		):
								A					       = self.preprocess_text(_lowercase		)
								return self.sp_model.encode(_lowercase			,			out_type=_lowercase		)







						def __a						(							self   :    str			,			_lowercase   :    str		):
								return self.sp_model.PieceToId(_lowercase		)







						def __a						(							self   :    int			,			_lowercase   :    int		):
								return self.sp_model.IdToPiece(_lowercase		)







						@staticmethod
						def __a						(							_lowercase   :    str		):
								return out_string







						def __a						(							self   :    Optional[Any]			,			_lowercase   :    List[str]		):
								A					       = []
								A					       = ''
								A					       = False
								for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
												# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
												if not prev_is_special:
														out_string += " "

												out_string += self.sp_model.decode(_lowercase		) + token
												A					       = True
												A					       = []
										else:
												current_sub_tokens.append(_lowercase		)
												A					       = False
								out_string += self.sp_model.decode(_lowercase		)

								return out_string







						def __a						(							self   :    List[Any]		):
								A					       = {self.convert_ids_to_tokens(_lowercase		): i for i in range(self.vocab_size		)}
								vocab.update(self.added_tokens_encoder		)
								return vocab







						def __a						(							self   :    Dict			,			_lowercase   :    str			,			_lowercase   :    Optional[str] = None		):
								if not os.path.isdir(_lowercase		):
										logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
										return
								A					       = os.path.join(
								    _lowercase			,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

								if os.path.abspath(self.vocab_file		) != os.path.abspath(_lowercase		) and os.path.isfile(self.vocab_file		):
										copyfile(self.vocab_file			,			_lowercase		)
								elif not os.path.isfile(self.vocab_file		):
										with open(_lowercase			,			'wb'		) as fi:
												A					       = self.sp_model.serialized_model_proto()
												fi.write(_lowercase		)

								return (out_vocab_file,)







						def __a						(							self   :    List[Any]			,			_lowercase   :    Union[str, List[str]]			,			_lowercase   :    Union[str, bool] = False		):

								if isinstance(_lowercase			,			_lowercase		):
										A					       = self.preprocess_text(_lowercase		)
										A					       = self.sp_model.encode(_lowercase		)
								else:
										A					       = [self.preprocess_text(_lowercase		) for t in text]
										A					       = self.sp_model.encode(_lowercase		)

								if return_tensors is True or return_tensors == "pt":
										A					       = torch.tensor(_lowercase		)

								return token_ids







						def __a						(							self   :    str			,			_lowercase   :    Union[int, List[int]]		):
								return self.sp_model.decode(_lowercase		)







						def __a						(							self   :    str			,			_lowercase   :    "Conversation"		):
								A					       = [f'User: {text}' if is_user else f'Bot: {text}' for is_user, text in conversation.iter_texts()]
								A					       = (
								    f'{self.eos_token}{self.bos_token}' + f'{self.bos_token}'.join(_lowercase		) + f'{self.bos_token}Bot:'
								)
								return self.encode(text=_lowercase		)









"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCamelCase						:				int             =      logging.get_logger(__name__)

UpperCamelCase						:				Optional[int]             =      {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}



class 			lowerCamelCase__						(   UpperCAmelCase_							,    UpperCAmelCase_			):
						lowerCAmelCase       =     """bit"""
						lowerCAmelCase       =     ["""preactivation""", """bottleneck"""]
						lowerCAmelCase       =     ["""SAME""", """VALID"""]







						def __init__(							self   :    List[str]			,			_lowercase   :    int=3			,			_lowercase   :    List[str]=64			,			_lowercase   :    Optional[int]=[256, 512, 1_024, 2_048]			,			_lowercase   :    Optional[int]=[3, 4, 6, 3]			,			_lowercase   :    int="preactivation"			,			_lowercase   :    Union[str, Any]="relu"			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Tuple=32			,			_lowercase   :    Optional[int]=0.0			,			_lowercase   :    Union[str, Any]=False			,			_lowercase   :    Dict=32			,			_lowercase   :    Union[str, Any]=1			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Any=None			,			**_lowercase   :    str			,			):
								super().__init__(**_lowercase		)
								if layer_type not in self.layer_types:
										raise ValueError(f'layer_type={layer_type} is not one of {",".join(self.layer_types		)}'		)
								if global_padding is not None:
										if global_padding.upper() in self.supported_padding:
												A					       = global_padding.upper()
										else:
												raise ValueError(f'Padding strategy {global_padding} not supported'		)
								A					       = num_channels
								A					       = embedding_size
								A					       = hidden_sizes
								A					       = depths
								A					       = layer_type
								A					       = hidden_act
								A					       = global_padding
								A					       = num_groups
								A					       = drop_path_rate
								A					       = embedding_dynamic_padding
								A					       = output_stride
								A					       = width_factor

								A					       = ['stem'] + [f'stage{idx}' for idx in range(1			,			len(_lowercase		) + 1		)]
								A       , A					       = get_aligned_output_features_output_indices(
								    out_features=_lowercase			,			out_indices=_lowercase			,			stage_names=self.stage_names		)





