


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


snake_case__			 =						logging.get_logger(__name__)


snake_case__			 =						'''▁'''

snake_case__			 =						{'''vocab_file''': '''spiece.model'''}

snake_case__			 =						{
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

snake_case__			 =						{
    '''google/reformer-crime-and-punishment''': 52_4288,
}


class 					lowerCAmelCase_ (       __UpperCAmelCase):
						lowerCamelCase_         =     VOCAB_FILES_NAMES
						lowerCamelCase_         =     PRETRAINED_VOCAB_FILES_MAP
						lowerCamelCase_         =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCamelCase_         =     ["input_ids", "attention_mask"]




						def __init__(	self						:   List[Any]   ,     __A						:   List[Any]   ,     __A						:   List[Any]="</s>"   ,     __A						:   str="<unk>"   ,     __A						:   Union[str, Any]=[]   ,     __A						:   Optional[Dict[str, Any]] = None   ,     **__A						:   List[str]   ,     )      ->Any:






									"""simple docstring"""






									a__						  =    {} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    eos_token=_lowerCamelCase   ,     unk_token=_lowerCamelCase   ,     additional_special_tokens=_lowerCamelCase   ,     sp_model_kwargs=self.sp_model_kwargs   ,     **_lowerCamelCase   ,     )

									a__						  =    vocab_file
									a__						  =    spm.SentencePieceProcessor(**self.sp_model_kwargs						)
									self.sp_model.Load(_lowerCamelCase						)




						@property
						def  _snake_case					(	self						:   Optional[int]						)      ->Optional[int]:






									"""simple docstring"""






									return self.sp_model.get_piece_size()




						def  _snake_case					(	self						:   int						)      ->Optional[Any]:






									"""simple docstring"""






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




						def __getstate__(	self						:   Tuple						)      ->Optional[int]:






									"""simple docstring"""






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




						def __setstate__(	self						:   Dict   ,     __A						:   Union[str, Any]						)      ->Union[str, Any]:






									"""simple docstring"""






									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						)




						def  _snake_case					(	self						:   Optional[Any]   ,     __A						:   str						)      ->List[Any]:






									"""simple docstring"""






									return self.sp_model.encode(_lowerCamelCase   ,     out_type=_lowerCamelCase						)




						def  _snake_case					(	self						:   int   ,     __A						:   Optional[int]						)      ->Tuple:






									"""simple docstring"""






									return self.sp_model.piece_to_id(_lowerCamelCase						)




						def  _snake_case					(	self						:   Union[str, Any]   ,     __A						:   Tuple						)      ->Union[str, Any]:






									"""simple docstring"""






									if index < self.sp_model.get_piece_size():
												a__						  =    self.sp_model.IdToPiece(_lowerCamelCase						)
									return token




						def  _snake_case					(	self						:   List[str]   ,     __A						:   int						)      ->Optional[int]:






									"""simple docstring"""






									a__						  =    []
									a__						  =    ""
									for token in tokens:
												# make sure that special tokens are not decoded using sentencepiece model
												if token in self.all_special_tokens:
															out_string += self.sp_model.decode(_lowerCamelCase						) + token
															a__						  =    []
												else:
															current_sub_tokens.append(_lowerCamelCase						)
									out_string += self.sp_model.decode(_lowerCamelCase						)
									return out_string.strip()






						def  _snake_case					(	self						:   Optional[int]   ,     __A						:   str   ,     __A						:   Optional[str] = None						)      ->Any:






									"""simple docstring"""






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

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

									return (out_vocab_file,)






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


snake_case__			 =						False


class 					lowerCAmelCase_ (       unittest.TestCase):
						pass






@nightly
@require_torch_gpu
class 					lowerCAmelCase_ (       unittest.TestCase):




						def  _snake_case					(	self						:   Tuple						)      ->Optional[int]:






									"""simple docstring"""






									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()




						def  _snake_case					(	self						:   Dict						)      ->Any:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"						)
									# remove text_unet
									pipe.remove_unused_weights()
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :List[Any]						  =    "A painting of a squirrel eating a burger "
									a__ :Optional[Any]						  =    torch.manual_seed(0						)
									a__ :List[Any]						  =    pipe(
									    prompt=__A   ,     generator=__A   ,     guidance_scale=7.5   ,     num_inference_steps=2   ,     output_type="numpy"						).images

									with tempfile.TemporaryDirectory() as tmpdirname:
												pipe.save_pretrained(__A						)
												a__ :List[Any]						  =    VersatileDiffusionTextToImagePipeline.from_pretrained(__A						)
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :Optional[int]						  =    generator.manual_seed(0						)
									a__ :List[Any]						  =    pipe(
									    prompt=__A   ,     generator=__A   ,     guidance_scale=7.5   ,     num_inference_steps=2   ,     output_type="numpy"						).images

									assert np.abs(image - new_image						).sum() < 1E-5, "Models don't have the same forward pass"






						def  _snake_case					(	self						:   Optional[Any]						)      ->List[Any]:






									"""simple docstring"""






									a__ :Tuple						  =    VersatileDiffusionTextToImagePipeline.from_pretrained(
									    "shi-labs/versatile-diffusion"   ,     torch_dtype=torch.floataa						)
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :Tuple						  =    "A painting of a squirrel eating a burger "
									a__ :Tuple						  =    torch.manual_seed(0						)
									a__ :Optional[Any]						  =    pipe(
									    prompt=__A   ,     generator=__A   ,     guidance_scale=7.5   ,     num_inference_steps=50   ,     output_type="numpy"						).images

									a__ :Tuple						  =    image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									a__ :Tuple						  =    np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]						)

									assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2


