


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

__UpperCamelCase   		=       {
    '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   		=       {
    'AI-Sweden/gpt-sw3-126m': 2_0_4_8,
    'AI-Sweden/gpt-sw3-350m': 2_0_4_8,
    'AI-Sweden/gpt-sw3-1.6b': 2_0_4_8,
    'AI-Sweden/gpt-sw3-6.7b': 2_0_4_8,
    'AI-Sweden/gpt-sw3-20b': 2_0_4_8,
}







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Tuple    =		VOCAB_FILES_NAMES
			_UpperCamelCase		:					str    =		PRETRAINED_VOCAB_FILES_MAP
			_UpperCamelCase		:					str    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCamelCase		:					int    =		['input_ids', 'attention_mask']




			def __init__(							self      ,						snake_case      ,						snake_case=False      ,						snake_case=False      ,						snake_case=False      ,						snake_case=None      ,						snake_case=None      ,						snake_case=None      ,						snake_case=None      ,						snake_case = None      ,						**snake_case      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       {} if sp_model_kwargs is None else sp_model_kwargs

						UpperCamelCase__					=       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"     )
									UpperCamelCase__					=       "None"

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

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

						UpperCamelCase__					=       do_lower_case
						UpperCamelCase__					=       remove_space
						UpperCamelCase__					=       keep_accents
						UpperCamelCase__					=       vocab_file

						UpperCamelCase__					=       spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.Load(snake_case     )

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

						# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
						UpperCamelCase__					=       re.compile(
						    F'''[{''.join(map(snake_case      ,						list(range(0      ,						9     )     ) + list(range(11      ,						32     )     ) + list(range(127      ,						160     )     ) + [160, 173, 8203]     )     )}]'''     )




			def __getstate__(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.__dict__.copy()
						UpperCamelCase__					=       None
						return state




			def __setstate__(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       d

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

						UpperCamelCase__					=       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 				snake_case__				(							self     ):

						'''simple docstring'''


						return len(self.sp_model     )




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.non_printing_characters_re.sub(""      ,						snake_case     )

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

						# NFC Unicode normalization
						UpperCamelCase__					=       unicodedata.normalize("NFC"      ,						snake_case     )
						return text




			def 				snake_case__				(							self      ,						snake_case      ,						**snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.preprocess_text(snake_case     )
						return self.sp_model.encode(snake_case      ,						out_type=snake_case     )




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						return self.sp_model.PieceToId(snake_case     )




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						return self.sp_model.IdToPiece(snake_case     )




			@staticmethod
			def 				snake_case__				(							snake_case     ):

						'''simple docstring'''


						return out_string




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       []
						UpperCamelCase__					=       ""
						UpperCamelCase__					=       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(snake_case     ) + token
												UpperCamelCase__					=       True
												UpperCamelCase__					=       []
									else:
												current_sub_tokens.append(snake_case     )
												UpperCamelCase__					=       False
						out_string += self.sp_model.decode(snake_case     )

						return out_string




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       {self.convert_ids_to_tokens(snake_case     ): i for i in range(self.vocab_size     )}
						vocab.update(self.added_tokens_encoder     )
						return vocab




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case = None     ):

						'''simple docstring'''


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

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

						return (out_vocab_file,)




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case = False     ):

						'''simple docstring'''


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

						if return_tensors is True or return_tensors == "pt":
									UpperCamelCase__					=       torch.tensor(snake_case     )

						return token_ids




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						return self.sp_model.decode(snake_case     )







			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       [F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
						UpperCamelCase__					=       (
						    F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(snake_case     ) + F'''{self.bos_token}Bot:'''
						)
						return self.encode(text=snake_case     )






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase    		=       {
    'configuration_mobilenet_v2': [
        'MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileNetV2Config',
        'MobileNetV2OnnxConfig',
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase    		=       ['MobileNetV2FeatureExtractor']
						__UpperCamelCase    		=       ['MobileNetV2ImageProcessor']


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase    		=       [
						    'MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'MobileNetV2ForImageClassification',
						    'MobileNetV2ForSemanticSegmentation',
						    'MobileNetV2Model',
						    'MobileNetV2PreTrainedModel',
						    'load_tf_weights_in_mobilenet_v2',
						]


if TYPE_CHECKING:
						from .configuration_mobilenet_va import (
						    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    MobileNetVaConfig,
						    MobileNetVaOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
												from .image_processing_mobilenet_va import MobileNetVaImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilenet_va import (
												    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileNetVaForImageClassification,
												    MobileNetVaForSemanticSegmentation,
												    MobileNetVaModel,
												    MobileNetVaPreTrainedModel,
												    load_tf_weights_in_mobilenet_va,
												)


else:
						import sys

						__UpperCamelCase    		=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


