
"""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


_lowercase				 =					logging.get_logger(__name__)
_lowercase				 =					{'''vocab_file''': '''spiece.model'''}

_lowercase				 =					{
    '''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''',
    }
}

_lowercase				 =					{
    '''AI-Sweden/gpt-sw3-126m''': 20_48,
    '''AI-Sweden/gpt-sw3-350m''': 20_48,
    '''AI-Sweden/gpt-sw3-1.6b''': 20_48,
    '''AI-Sweden/gpt-sw3-6.7b''': 20_48,
    '''AI-Sweden/gpt-sw3-20b''': 20_48,
}







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     List[str]   =     VOCAB_FILES_NAMES
						_lowerCamelCase:     List[Any]   =     PRETRAINED_VOCAB_FILES_MAP
						_lowerCamelCase:     int   =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCamelCase:     List[str]   =     ['''input_ids''', '''attention_mask''']


						def __init__(			self  :   Tuple	,A_  :   Any	,A_  :   int=False	,A_  :   List[Any]=False	,A_  :   Optional[int]=False	,A_  :   Union[str, Any]=None	,A_  :   Tuple=None	,A_  :   Any=None	,A_  :   int=None	,A_  :   Optional[Dict[str, Any]] = None	,**A_  :   Tuple	,)  -> None:
							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=A_	,remove_space=A_	,keep_accents=A_	,bos_token=A_	,eos_token=A_	,unk_token=A_	,pad_token=A_	,sp_model_kwargs=self.sp_model_kwargs	,**A_	,)

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

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

							# 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(A_	,list(range(0	,9   )   ) + list(range(11	,32   )   ) + list(range(127	,160   )   ) + [160, 173, 8203]   )   )}]'   )


						def __getstate__(			self  :   Optional[Any]   )  -> Tuple:
							A     = self.__dict__.copy()
							A     = None
							return state


						def __setstate__(			self  :   Dict	,A_  :   List[str]   )  -> str:
							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     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> int:
							return len(self.sp_model   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   str   )  -> str:
							A     = self.non_printing_characters_re.sub(''	,A_   )

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

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   str	,**A_  :   Any   )  -> List[str]:
							A     = self.preprocess_text(A_   )
							return self.sp_model.encode(A_	,out_type=A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,A_  :   str   )  -> int:
							return self.sp_model.PieceToId(A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   int   )  -> str:
							return self.sp_model.IdToPiece(A_   )


						@staticmethod
						def     _SCREAMING_SNAKE_CASE   (			A_  :   str   )  -> str:
							return out_string


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]	,A_  :   List[str]   )  -> 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(A_   ) + token
									A     = True
									A     = []
								else:
									current_sub_tokens.append(A_   )
									A     = False
							out_string += self.sp_model.decode(A_   )

							return out_string


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> Dict[str, int]:
							A     = {self.convert_ids_to_tokens(A_   ): i for i in range(self.vocab_size   )}
							vocab.update(self.added_tokens_encoder   )
							return vocab


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

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

							return (out_vocab_file,)


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,A_  :   Union[str, List[str]]	,A_  :   Union[str, bool] = False   )  -> Union[List[int], List[List[int]], "torch.Tensor"]:

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

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

							return token_ids


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,A_  :   Union[int, List[int]]   )  -> str:
							return self.sp_model.decode(A_   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]	,A_  :   "Conversation"   )  -> List[int]:
							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(A_   ) + F'{self.bos_token}Bot:'
							)
							return self.encode(text=A_   )






'''simple docstring'''


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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Union[str, Any]								=       "swinv2"

 __lowerCamelCase    :				int								=       {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }




 def __init__(						self      ,  _lowerCAmelCase=224      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=96      ,  _lowerCAmelCase=[2, 2, 6, 2]      ,  _lowerCAmelCase=[3, 6, 12, 24]      ,  _lowerCAmelCase=7      ,  _lowerCAmelCase=4.0      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-5      ,  _lowerCAmelCase=32      ,  **_lowerCAmelCase      ,  )     ->				Tuple:
     super().__init__(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   embed_dim
     _lowerCAmelCase						      =   depths
     _lowerCAmelCase						      =   len(_lowerCAmelCase		)
     _lowerCAmelCase						      =   num_heads
     _lowerCAmelCase						      =   window_size
     _lowerCAmelCase						      =   mlp_ratio
     _lowerCAmelCase						      =   qkv_bias
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   drop_path_rate
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   use_absolute_embeddings
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   encoder_stride
     # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     _lowerCAmelCase						      =   int(embed_dim * 2 ** (len(_lowerCAmelCase		) - 1)		)
     _lowerCAmelCase						      =   (0, 0, 0, 0)


