





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


UpperCAmelCase_  							=				logging.get_logger(__name__)


UpperCAmelCase_  							=				"▁"

UpperCAmelCase_  							=				{"vocab_file": "spiece.model"}

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

UpperCAmelCase_  							=				{
    "google/reformer-crime-and-punishment": 52_4288,
}






class 					lowercase__ (			__UpperCAmelCase       ):

			'''simple docstring'''


			a		:							List[Any]     	=	VOCAB_FILES_NAMES
			a		:							Tuple     	=	PRETRAINED_VOCAB_FILES_MAP
			a		:							int     	=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a		:							Optional[Any]     	=	["input_ids", "attention_mask"]




			def __init__(					self,	__magic_name__,	__magic_name__="</s>",	__magic_name__="<unk>",	__magic_name__=[],	__magic_name__ = None,	**__magic_name__,	)				->   Any:






							"""simple docstring"""
							UpperCamelCase__							:       Optional[Any]      =   {} 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_,	)

							UpperCamelCase__							:       Optional[int]      =   vocab_file
							UpperCamelCase__							:       Dict      =   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
							self.sp_model.Load(lowerCAmelCase_							)




			@property
			def    UpperCamelCase__						(					self							)				->   List[Any]:






							"""simple docstring"""
							return self.sp_model.get_piece_size()




			def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






							"""simple docstring"""
							UpperCamelCase__							:       Tuple      =   {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:






							"""simple docstring"""
							UpperCamelCase__							:       int      =   self.__dict__.copy()
							UpperCamelCase__							:       Any      =   None
							return state




			def __setstate__(					self,	__magic_name__							)				->   Tuple:






							"""simple docstring"""
							UpperCamelCase__							:       Optional[int]      =   d

							# for backward compatibility
							if not hasattr(self,	'''sp_model_kwargs'''							):
											UpperCamelCase__							:       Optional[Any]      =   {}

							UpperCamelCase__							:       List[str]      =   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
							self.sp_model.Load(self.vocab_file							)




			def    UpperCamelCase__						(					self,	__magic_name__							)				->   List[Any]:






							"""simple docstring"""
							return self.sp_model.encode(lowerCAmelCase_,	out_type=lowerCAmelCase_							)




			def    UpperCamelCase__						(					self,	__magic_name__							)				->   Union[str, Any]:






							"""simple docstring"""
							return self.sp_model.piece_to_id(lowerCAmelCase_							)




			def    UpperCamelCase__						(					self,	__magic_name__							)				->   Optional[int]:






							"""simple docstring"""







							if index < self.sp_model.get_piece_size():
											UpperCamelCase__							:       Union[str, Any]      =   self.sp_model.IdToPiece(lowerCAmelCase_							)
							return token




			def    UpperCamelCase__						(					self,	__magic_name__							)				->   str:






							"""simple docstring"""
							UpperCamelCase__							:       List[str]      =   []
							UpperCamelCase__							:       Dict      =   ''''''
							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
															UpperCamelCase__							:       int      =   []
											else:
															current_sub_tokens.append(lowerCAmelCase_							)
							out_string += self.sp_model.decode(lowerCAmelCase_							)
							return out_string.strip()




			def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   str:






							"""simple docstring"""







							if not os.path.isdir(lowerCAmelCase_							):
											logger.error(f"Vocabulary path ({save_directory}) should be a directory"							)
											return
							UpperCamelCase__							:       Optional[int]      =   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:
															UpperCamelCase__							:       Optional[Any]      =   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


UpperCAmelCase_  							=				False






class 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   pass






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

   '''simple docstring'''


   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




   def    UpperCamelCase__						(					self							)				->   List[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''							)
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       int      =   '''A painting of a squirrel eating a burger '''
       UpperCamelCase__							:       str      =   torch.manual_seed(0							)
       UpperCamelCase__							:       int      =   pipe(
           prompt=__magic_name__,	generator=__magic_name__,	guidance_scale=7.5,	num_inference_steps=2,	output_type='''numpy'''							).images

       with tempfile.TemporaryDirectory() as tmpdirname:
           pipe.save_pretrained(__magic_name__							)
           UpperCamelCase__							:       Union[str, Any]      =   VersatileDiffusionTextToImagePipeline.from_pretrained(__magic_name__							)
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       Optional[Any]      =   generator.manual_seed(0							)
       UpperCamelCase__							:       str      =   pipe(
           prompt=__magic_name__,	generator=__magic_name__,	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    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       str      =   VersatileDiffusionTextToImagePipeline.from_pretrained(
           '''shi-labs/versatile-diffusion''',	torch_dtype=torch.floataa							)
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       Optional[int]      =   '''A painting of a squirrel eating a burger '''
       UpperCamelCase__							:       str      =   torch.manual_seed(0							)
       UpperCamelCase__							:       Optional[int]      =   pipe(
           prompt=__magic_name__,	generator=__magic_name__,	guidance_scale=7.5,	num_inference_steps=50,	output_type='''numpy'''							).images

       UpperCamelCase__							:       int      =   image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       UpperCamelCase__							:       int      =   np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]							)

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

