"""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


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

__lowerCamelCase											=  {
    "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",
    }
}

__lowerCamelCase											=  {
    "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 			UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Tuple													=		VOCAB_FILES_NAMES
						lowerCAmelCase__  :						Union[str, Any]													=		PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase__  :						Any													=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase__  :						Optional[Any]													=		["""input_ids""", """attention_mask"""]





						def __init__(       self		,__UpperCAmelCase		,__UpperCAmelCase=False		,__UpperCAmelCase=False		,__UpperCAmelCase=False		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase = None		,**__UpperCAmelCase		,)					->				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=__UpperCAmelCase		,remove_space=__UpperCAmelCase		,keep_accents=__UpperCAmelCase		,bos_token=__UpperCAmelCase		,eos_token=__UpperCAmelCase		,unk_token=__UpperCAmelCase		,pad_token=__UpperCAmelCase		,sp_model_kwargs=self.sp_model_kwargs		,**__UpperCAmelCase		,)

													A__	     =							do_lower_case
													A__	     =							remove_space
													A__	     =							keep_accents
													A__	     =							vocab_file

													A__	     =							spm.SentencePieceProcessor(**self.sp_model_kwargs     )
													self.sp_model.Load(__UpperCAmelCase     )

													# 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(__UpperCAmelCase		,list(range(0		,9     )     ) + list(range(11		,32     )     ) + list(range(1_27		,1_60     )     ) + [1_60, 1_73, 82_03]     )     )}]'''     )





						def __getstate__(       self     )					->				str:
													A__	     =							self.__dict__.copy()
													A__	     =							None
													return state





						def __setstate__(       self		,__UpperCAmelCase     )					->				Union[str, Any]:
													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 				snake_case__	(       self     )					->				int:
													return len(self.sp_model     )





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				str:
													A__	     =							self.non_printing_characters_re.sub(''		,__UpperCAmelCase     )

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

													# NFC Unicode normalization
													A__	     =							unicodedata.normalize('NFC'		,__UpperCAmelCase     )
													return text





						def 				snake_case__	(       self		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				List[str]:
													A__	     =							self.preprocess_text(__UpperCAmelCase     )
													return self.sp_model.encode(__UpperCAmelCase		,out_type=__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				int:
													return self.sp_model.PieceToId(__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				str:
													return self.sp_model.IdToPiece(__UpperCAmelCase     )





						@staticmethod
						def 				snake_case__	(       __UpperCAmelCase     )					->				str:
													return out_string





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				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(__UpperCAmelCase     ) + token
																											A__	     =							True
																											A__	     =							[]
																				else:
																											current_sub_tokens.append(__UpperCAmelCase     )
																											A__	     =							False
													out_string += self.sp_model.decode(__UpperCAmelCase     )

													return out_string





						def 				snake_case__	(       self     )					->				Dict[str, int]:
													A__	     =							{self.convert_ids_to_tokens(__UpperCAmelCase     ): i for i in range(self.vocab_size     )}
													vocab.update(self.added_tokens_encoder     )
													return vocab





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = None     )					->				Tuple[str]:

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

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

													return (out_vocab_file,)





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = False     )					->				Union[List[int], List[List[int]], "torch.Tensor"]:

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

													if return_tensors is True or return_tensors == "pt":
																				A__	     =							torch.tensor(__UpperCAmelCase     )

													return token_ids





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				str:
													return self.sp_model.decode(__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				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(__UpperCAmelCase     ) + f'''{self.bos_token}Bot:'''
													)
													return self.encode(text=__UpperCAmelCase     )






"""simple docstring"""




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
				import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class     UpperCamelCase__(			unittest.TestCase ):





						@property
						def 				snake_case__	(       self     )					->				int:
													return (
													    "CUDAExecutionProvider",
													    {
													        "gpu_mem_limit": "15000000000",  # 15GB
													        "arena_extend_strategy": "kSameAsRequested",
													    },
													)





						@property
						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     =							ort.SessionOptions()
													A__	     =							False
													return options





						def 				snake_case__	(       self     )					->				str:
													A__	     =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/overture-creations-5sI6fQgYIuo.png'     )
													A__	     =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'     )
													A__	     =							load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'     )

													# using the PNDM scheduler by default
													A__	     =							OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
													    'CompVis/stable-diffusion-v1-4'		,revision='onnx'		,safety_checker=__UpperCAmelCase		,feature_extractor=__UpperCAmelCase		,provider=self.gpu_provider		,sess_options=self.gpu_options		,)
													pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

													A__	     =							'A red cat sitting on a park bench'

													A__	     =							np.random.RandomState(0     )
													A__	     =							pipe(
													    prompt=__UpperCAmelCase		,image=__UpperCAmelCase		,mask_image=__UpperCAmelCase		,strength=0.7_5		,guidance_scale=7.5		,num_inference_steps=15		,generator=__UpperCAmelCase		,output_type='np'		,)

													A__	     =							output.images[0]

													assert image.shape == (5_12, 5_12, 3)
													assert np.abs(expected_image - image     ).max() < 1e-2





