
"""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


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

_SCREAMING_SNAKE_CASE	       =      {
    """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""",
    }
}

_SCREAMING_SNAKE_CASE	       =      {
    """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 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					List[str]     							=   VOCAB_FILES_NAMES
					_SCREAMING_SNAKE_CASE       :					int     							=   PRETRAINED_VOCAB_FILES_MAP
					_SCREAMING_SNAKE_CASE       :					List[str]     							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_SCREAMING_SNAKE_CASE       :					Union[str, Any]     							=   ['input_ids', 'attention_mask']





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

												__snake_case     			=       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"    )
																			__snake_case     			=       "None"

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

												__snake_case     			=       do_lower_case
												__snake_case     			=       remove_space
												__snake_case     			=       keep_accents
												__snake_case     			=       vocab_file

												__snake_case     			=       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
												self.sp_model.Load(snake_case_    )

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

												# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
												__snake_case     			=       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   :							Any    ):
												__snake_case     			=       self.__dict__.copy()
												__snake_case     			=       None
												return state





					def __setstate__(				self   :							List[str]							,						snake_case_   :							Any    ):
												__snake_case     			=       d

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

												__snake_case     			=       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   :							Union[str, Any]    ):
												return len(self.sp_model    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							str    ):
												__snake_case     			=       self.non_printing_characters_re.sub(""							,						snake_case_    )

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

												# NFC Unicode normalization
												__snake_case     			=       unicodedata.normalize("NFC"							,						snake_case_    )
												return text





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							str							,						**snake_case_   :							Optional[Any]    ):
												__snake_case     			=       self.preprocess_text(snake_case_    )
												return self.sp_model.encode(snake_case_							,						out_type=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							str    ):
												return self.sp_model.PieceToId(snake_case_    )





					def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							int    ):
												return self.sp_model.IdToPiece(snake_case_    )





					@staticmethod
					def 							lowerCAmelCase      (				snake_case_   :							str    ):
												return out_string





					def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							List[str]    ):
												__snake_case     			=       []
												__snake_case     			=       ""
												__snake_case     			=       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
																										__snake_case     			=       True
																										__snake_case     			=       []
																			else:
																										current_sub_tokens.append(snake_case_    )
																										__snake_case     			=       False
												out_string += self.sp_model.decode(snake_case_    )

												return out_string





					def 							lowerCAmelCase      (				self   :							Optional[Any]    ):
												__snake_case     			=       {self.convert_ids_to_tokens(snake_case_    ): i for i in range(self.vocab_size    )}
												vocab.update(self.added_tokens_encoder    )
												return vocab





					def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							str							,						snake_case_   :							Optional[str] = None    ):
												if not os.path.isdir(snake_case_    ):
																			logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
																			return
												__snake_case     			=       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:
																										__snake_case     			=       self.sp_model.serialized_model_proto()
																										fi.write(snake_case_    )

												return (out_vocab_file,)





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Union[str, List[str]]							,						snake_case_   :							Union[str, bool] = False    ):

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

												if return_tensors is True or return_tensors == "pt":
																			__snake_case     			=       torch.tensor(snake_case_    )

												return token_ids





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							Union[int, List[int]]    ):
												return self.sp_model.decode(snake_case_    )





					def 							lowerCAmelCase      (				self   :							List[Any]							,						snake_case_   :							"Conversation"    ):
												__snake_case     			=       [F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
												__snake_case     			=       (
												    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_    )


"""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 							__magic_name__     (		unittest.TestCase   ):





					@property
					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												return (
												    "CUDAExecutionProvider",
												    {
												        "gpu_mem_limit": "15000000000",  # 15GB
												        "arena_extend_strategy": "kSameAsRequested",
												    },
												)





					@property
					def 							lowerCAmelCase      (				self   :							str    ):
												__snake_case     			=       ort.SessionOptions()
												__snake_case     			=       False
												return options





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/in_paint/overture-creations-5sI6fQgYIuo.png"    )
												__snake_case     			=       load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"    )
												__snake_case     			=       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
												__snake_case     			=       OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
												    "CompVis/stable-diffusion-v1-4"							,						revision="onnx"							,						safety_checker=snake_case_							,						feature_extractor=snake_case_							,						provider=self.gpu_provider							,						sess_options=self.gpu_options							,						)
												pipe.set_progress_bar_config(disable=snake_case_    )

												__snake_case     			=       "A red cat sitting on a park bench"

												__snake_case     			=       np.random.RandomState(0    )
												__snake_case     			=       pipe(
												    prompt=snake_case_							,						image=snake_case_							,						mask_image=snake_case_							,						strength=0.75							,						guidance_scale=7.5							,						num_inference_steps=15							,						generator=snake_case_							,						output_type="np"							,						)

												__snake_case     			=       output.images[0]

												assert image.shape == (512, 512, 3)
												assert np.abs(expected_image - image    ).max() < 1e-2
