

"""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


a		=logging.get_logger(__name__)
a		={"vocab_file": "spiece.model"}

a		={
    "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",
    }
}

a		={
    "AI-Sweden/gpt-sw3-126m": 2048,
    "AI-Sweden/gpt-sw3-350m": 2048,
    "AI-Sweden/gpt-sw3-1.6b": 2048,
    "AI-Sweden/gpt-sw3-6.7b": 2048,
    "AI-Sweden/gpt-sw3-20b": 2048,
}






class 	A_		(	UpperCAmelCase__    ):
							_UpperCAmelCase   :  Union[str, Any]										=				VOCAB_FILES_NAMES
							_UpperCAmelCase   :  Any										=				PRETRAINED_VOCAB_FILES_MAP
							_UpperCAmelCase   :  Any										=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_UpperCAmelCase   :  Union[str, Any]										=				['input_ids', 'attention_mask']
							def __init__(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      Tuple=False       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=False       ,SCREAMING_SNAKE_CASE__		:      List[Any]=False       ,SCREAMING_SNAKE_CASE__		:      Tuple=None       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=None       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=None       ,SCREAMING_SNAKE_CASE__		:      int=None       ,SCREAMING_SNAKE_CASE__		:      int = None       ,**SCREAMING_SNAKE_CASE__		:      Tuple       ,):
								__lowerCamelCase    :    List[Any] 	=			{} if sp_model_kwargs is None else sp_model_kwargs

								__lowerCamelCase    :    List[Any] 	=			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')
									__lowerCamelCase    :    str 	=			"""None"""

								# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
								__lowerCamelCase    :    List[str] 	=			"""<|endoftext|>""" if eos_token is None else eos_token
								__lowerCamelCase    :    Optional[Any] 	=			"""<unk>""" if unk_token is None else unk_token
								if "gpt-sw3-7b" in name_or_path:
									__lowerCamelCase    :    Tuple 	=			unk_token if pad_token is None else pad_token
									__lowerCamelCase    :    Dict 	=			eos_token if bos_token is None else bos_token
								else:
									__lowerCamelCase    :    Optional[Any] 	=			"""<pad>""" if pad_token is None else pad_token
									__lowerCamelCase    :    int 	=			"""<s>""" if bos_token is None else bos_token

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

								__lowerCamelCase    :    Optional[int] 	=			do_lower_case
								__lowerCamelCase    :    int 	=			remove_space
								__lowerCamelCase    :    Optional[Any] 	=			keep_accents
								__lowerCamelCase    :    Optional[Any] 	=			vocab_file

								__lowerCamelCase    :    List[str] 	=			spm.SentencePieceProcessor(**self.sp_model_kwargs)
								self.sp_model.Load(_SCREAMING_SNAKE_CASE)

								# Used for whitespace normalization in input texts
								# fmt : off
								__lowerCamelCase    :    str 	=			{""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
								# fmt : on

								# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
								__lowerCamelCase    :    List[Any] 	=			re.compile(
								    F"[{''.join(map(_SCREAMING_SNAKE_CASE       ,list(range(0       ,9)) + list(range(1_1       ,3_2)) + list(range(1_2_7       ,1_6_0)) + [1_6_0, 1_7_3, 8_2_0_3]))}]")
							def __getstate__(		self		:      Any):
								__lowerCamelCase    :    Optional[Any] 	=			self.__dict__.copy()
								__lowerCamelCase    :    List[str] 	=			None
								return state
							def __setstate__(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Any):
								__lowerCamelCase    :    List[Any] 	=			d

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

								__lowerCamelCase    :    Optional[Any] 	=			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    lowerCAmelCase					(		self		:      int):
								return len(self.sp_model)
							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      str):
								__lowerCamelCase    :    Tuple 	=			self.non_printing_characters_re.sub(''       ,_SCREAMING_SNAKE_CASE)

								# Normalize whitespaces
								__lowerCamelCase    :    str 	=			"""""".join([char if char not in self.whitespaces else ' ' for char in text])

								# NFC Unicode normalization
								__lowerCamelCase    :    Any 	=			unicodedata.normalize('NFC'       ,_SCREAMING_SNAKE_CASE)
								return text
							def    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      Dict       ,**SCREAMING_SNAKE_CASE__		:      int):
								__lowerCamelCase    :    Union[str, Any] 	=			self.preprocess_text(_SCREAMING_SNAKE_CASE)
								return self.sp_model.encode(_SCREAMING_SNAKE_CASE       ,out_type=_SCREAMING_SNAKE_CASE)
							def    lowerCAmelCase					(		self		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
								return self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE)
							def    lowerCAmelCase					(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Any):
								return self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE)
							@staticmethod
							def    lowerCAmelCase					(		SCREAMING_SNAKE_CASE__		:      Tuple):
								return out_string
							def    lowerCAmelCase					(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      List[Any]):
								__lowerCamelCase    :    Any 	=			[]
								__lowerCamelCase    :    Dict 	=			""""""
								__lowerCamelCase    :    Optional[int] 	=			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(_SCREAMING_SNAKE_CASE) + token
										__lowerCamelCase    :    Tuple 	=			True
										__lowerCamelCase    :    Optional[Any] 	=			[]
									else:
										current_sub_tokens.append(_SCREAMING_SNAKE_CASE)
										__lowerCamelCase    :    Optional[Any] 	=			False
								out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE)

								return out_string
							def    lowerCAmelCase					(		self		:      str):
								__lowerCamelCase    :    Optional[Any] 	=			{self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE): i for i in range(self.vocab_size)}
								vocab.update(self.added_tokens_encoder)
								return vocab
							def    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Any       ,SCREAMING_SNAKE_CASE__		:      Tuple = None):




								if not os.path.isdir(_SCREAMING_SNAKE_CASE):
									logger.error(F"Vocabulary path ({save_directory}) should be a directory")
									return
								__lowerCamelCase    :    List[str] 	=			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) and os.path.isfile(self.vocab_file):
									copyfile(self.vocab_file       ,_SCREAMING_SNAKE_CASE)
								elif not os.path.isfile(self.vocab_file):
									with open(_SCREAMING_SNAKE_CASE       ,'wb') as fi:
										__lowerCamelCase    :    Optional[Any] 	=			self.sp_model.serialized_model_proto()
										fi.write(_SCREAMING_SNAKE_CASE)

								return (out_vocab_file,)
							def    lowerCAmelCase					(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      int = False):




								if isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE):
									__lowerCamelCase    :    Optional[Any] 	=			self.preprocess_text(_SCREAMING_SNAKE_CASE)
									__lowerCamelCase    :    Optional[Any] 	=			self.sp_model.encode(_SCREAMING_SNAKE_CASE)
								else:
									__lowerCamelCase    :    Union[str, Any] 	=			[self.preprocess_text(_SCREAMING_SNAKE_CASE) for t in text]
									__lowerCamelCase    :    str 	=			self.sp_model.encode(_SCREAMING_SNAKE_CASE)

								if return_tensors is True or return_tensors == "pt":
									__lowerCamelCase    :    List[str] 	=			torch.tensor(_SCREAMING_SNAKE_CASE)

								return token_ids
							def    lowerCAmelCase					(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      str):
								return self.sp_model.decode(_SCREAMING_SNAKE_CASE)


							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      Dict):
								__lowerCamelCase    :    Optional[Any] 	=			[F"User: {text}" if is_user else F"Bot: {text}" for is_user, text in conversation.iter_texts()]
								__lowerCamelCase    :    Any 	=			(
								    F"{self.eos_token}{self.bos_token}" + F"{self.bos_token}".join(_SCREAMING_SNAKE_CASE) + F"{self.bos_token}Bot:"
								)
								return self.encode(text=_SCREAMING_SNAKE_CASE)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


a		=logging.get_logger(__name__)

a		={
    """sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  str										=				'''vit_msn'''
							def __init__(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      int=7_6_8       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=1_2       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=1_2       ,SCREAMING_SNAKE_CASE__		:      Tuple=3_0_7_2       ,SCREAMING_SNAKE_CASE__		:      str="gelu"       ,SCREAMING_SNAKE_CASE__		:      Any=0.0       ,SCREAMING_SNAKE_CASE__		:      str=0.0       ,SCREAMING_SNAKE_CASE__		:      Any=0.02       ,SCREAMING_SNAKE_CASE__		:      int=1E-06       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=2_2_4       ,SCREAMING_SNAKE_CASE__		:      Dict=1_6       ,SCREAMING_SNAKE_CASE__		:      int=3       ,SCREAMING_SNAKE_CASE__		:      List[str]=True       ,**SCREAMING_SNAKE_CASE__		:      str       ,):
								super().__init__(**SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    List[Any] 	=			hidden_size
								__lowerCamelCase    :    Tuple 	=			num_hidden_layers
								__lowerCamelCase    :    Union[str, Any] 	=			num_attention_heads
								__lowerCamelCase    :    Dict 	=			intermediate_size
								__lowerCamelCase    :    List[Any] 	=			hidden_act
								__lowerCamelCase    :    Optional[Any] 	=			hidden_dropout_prob
								__lowerCamelCase    :    str 	=			attention_probs_dropout_prob
								__lowerCamelCase    :    List[str] 	=			initializer_range
								__lowerCamelCase    :    Optional[int] 	=			layer_norm_eps
								__lowerCamelCase    :    Tuple 	=			image_size
								__lowerCamelCase    :    Union[str, Any] 	=			patch_size
								__lowerCamelCase    :    Optional[Any] 	=			num_channels
								__lowerCamelCase    :    List[str] 	=			qkv_bias




