



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						:Optional[Any]						=					logging.get_logger(__name__)


__snake_case						:List[Any]						=					'''▁'''

__snake_case						:List[Any]						=					{'''vocab_file''': '''spiece.model'''}

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

__snake_case						:List[Any]						=					{
    '''google/reformer-crime-and-punishment''': 52_4288,
}

class    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   int						 =						VOCAB_FILES_NAMES
       UpperCamelCase__  :   str						 =						PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase__  :   Dict						 =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase__  :   Optional[int]						 =						['''input_ids''', '''attention_mask''']



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


           '''simple docstring'''

           __a							=    {} if sp_model_kwargs is None else sp_model_kwargs

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

           __a							=    vocab_file
           __a							=    spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(__SCREAMING_SNAKE_CASE)



       @property
       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           return self.sp_model.get_piece_size()



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

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



       def __getstate__(		self							:					Dict):


           '''simple docstring'''

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



       def __setstate__(		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					List[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 		_lowerCamelCase      (		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           return self.sp_model.piece_to_id(__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           if index < self.sp_model.get_piece_size():
               __a							=    self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE)
           return token



       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Any):


           '''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(__SCREAMING_SNAKE_CASE) + token
                   __a							=    []
               else:
                   current_sub_tokens.append(__SCREAMING_SNAKE_CASE)
           out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE)
           return out_string.strip()





       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Optional[str] = None):


           '''simple docstring'''

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

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

           return (out_vocab_file,)










import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu

class    _A	(     unittest.TestCase   ):



       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

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



       @property
       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    1
           __a							=    3
           __a							=    (32, 32)

           __a							=    floats_tensor((batch_size, num_channels) + sizes					,		rng=random.Random(0)).to(__SCREAMING_SNAKE_CASE)
           return image



       @property
       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           torch.manual_seed(0)
           __a							=    UNetaDConditionModel(
               block_out_channels=(32, 64)					,		layers_per_block=2					,		sample_size=32					,		in_channels=4					,		out_channels=4					,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')					,		cross_attention_dim=32					,		)
           return model



       @property
       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           torch.manual_seed(0)
           __a							=    AutoencoderKL(
               block_out_channels=[32, 64]					,		in_channels=3					,		out_channels=3					,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,		latent_channels=4					,		)
           return model



       @property
       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           torch.manual_seed(0)
           __a							=    CLIPTextConfig(
               bos_token_id=0					,		eos_token_id=2					,		hidden_size=32					,		intermediate_size=37					,		layer_norm_eps=1E-05					,		num_attention_heads=4					,		num_hidden_layers=5					,		pad_token_id=1					,		vocab_size=1_000					,		)
           return CLIPTextModel(__SCREAMING_SNAKE_CASE)



       @property
       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           def extract(*__SCREAMING_SNAKE_CASE							:					int					,		**__SCREAMING_SNAKE_CASE							:					Dict):

               class    _A	:



                      def __init__(		self							:					int):


                          '''simple docstring'''

                          __a							=    torch.ones([0])





                      def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


                          '''simple docstring'''

                          self.pixel_values.to(__SCREAMING_SNAKE_CASE)
                          return self


               return Out()

           return extract



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           __a							=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           __a							=    self.dummy_cond_unet
           __a							=    DDIMScheduler(
               beta_start=0.0_00_85					,		beta_end=0.0_12					,		beta_schedule='''scaled_linear'''					,		clip_sample=__SCREAMING_SNAKE_CASE					,		set_alpha_to_one=__SCREAMING_SNAKE_CASE					,		)

           __a							=    self.dummy_vae
           __a							=    self.dummy_text_encoder
           __a							=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

           # make sure here that pndm scheduler skips prk
           __a							=    StableDiffusionPipeline(
               unet=__SCREAMING_SNAKE_CASE					,		scheduler=__SCREAMING_SNAKE_CASE					,		vae=__SCREAMING_SNAKE_CASE					,		text_encoder=__SCREAMING_SNAKE_CASE					,		tokenizer=__SCREAMING_SNAKE_CASE					,		safety_checker=__SCREAMING_SNAKE_CASE					,		feature_extractor=self.dummy_extractor					,		)
           __a							=    sd_pipe.to(__SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    '''A painting of a squirrel eating a burger'''

           __a							=    torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(0)
           __a							=    sd_pipe([prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=6.0					,		num_inference_steps=2					,		output_type='''np''')
           __a							=    output.images

           __a							=    torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(0)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=6.0					,		num_inference_steps=2					,		output_type='''np'''					,		return_dict=__SCREAMING_SNAKE_CASE					,		)[0]

           __a							=    image[0, -3:, -3:, -1]
           __a							=    image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           __a							=    np.array([0.57_56, 0.61_18, 0.50_05, 0.50_41, 0.54_71, 0.47_26, 0.49_76, 0.48_65, 0.48_64])

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



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           __a							=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           __a							=    self.dummy_cond_unet
           __a							=    PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE)
           __a							=    self.dummy_vae
           __a							=    self.dummy_text_encoder
           __a							=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

           # make sure here that pndm scheduler skips prk
           __a							=    StableDiffusionPipeline(
               unet=__SCREAMING_SNAKE_CASE					,		scheduler=__SCREAMING_SNAKE_CASE					,		vae=__SCREAMING_SNAKE_CASE					,		text_encoder=__SCREAMING_SNAKE_CASE					,		tokenizer=__SCREAMING_SNAKE_CASE					,		safety_checker=__SCREAMING_SNAKE_CASE					,		feature_extractor=self.dummy_extractor					,		)
           __a							=    sd_pipe.to(__SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    '''A painting of a squirrel eating a burger'''
           __a							=    torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(0)
           __a							=    sd_pipe([prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=6.0					,		num_inference_steps=2					,		output_type='''np''')

           __a							=    output.images

           __a							=    torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(0)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=6.0					,		num_inference_steps=2					,		output_type='''np'''					,		return_dict=__SCREAMING_SNAKE_CASE					,		)[0]

           __a							=    image[0, -3:, -3:, -1]
           __a							=    image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           __a							=    np.array([0.51_25, 0.57_16, 0.48_28, 0.50_60, 0.56_50, 0.47_68, 0.51_85, 0.48_95, 0.49_93])

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



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    StableDiffusionPipeline.from_pretrained(
               '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''					,		safety_checker=__SCREAMING_SNAKE_CASE)
           assert isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           assert isinstance(pipe.scheduler					,		__SCREAMING_SNAKE_CASE)
           assert pipe.safety_checker is None

           __a							=    pipe('''example prompt'''					,		num_inference_steps=2).images[0]
           assert image is not None

           # check that there's no error when saving a pipeline with one of the models being None
           with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__SCREAMING_SNAKE_CASE)
               __a							=    StableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE)

           # sanity check that the pipeline still works
           assert pipe.safety_checker is None
           __a							=    pipe('''example prompt'''					,		num_inference_steps=2).images[0]
           assert image is not None





       @unittest.skipIf(torch_device != '''cuda'''					,		'''This test requires a GPU''')
       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           __a							=    self.dummy_cond_unet
           __a							=    PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE)
           __a							=    self.dummy_vae
           __a							=    self.dummy_text_encoder
           __a							=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

           # put models in fp16
           __a							=    unet.half()
           __a							=    vae.half()
           __a							=    bert.half()

           # make sure here that pndm scheduler skips prk
           __a							=    StableDiffusionPipeline(
               unet=__SCREAMING_SNAKE_CASE					,		scheduler=__SCREAMING_SNAKE_CASE					,		vae=__SCREAMING_SNAKE_CASE					,		text_encoder=__SCREAMING_SNAKE_CASE					,		tokenizer=__SCREAMING_SNAKE_CASE					,		safety_checker=__SCREAMING_SNAKE_CASE					,		feature_extractor=self.dummy_extractor					,		)
           __a							=    sd_pipe.to(__SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    '''A painting of a squirrel eating a burger'''
           __a							=    sd_pipe([prompt]					,		num_inference_steps=2					,		output_type='''np''').images

           assert image.shape == (1, 64, 64, 3)


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



       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''					,		safety_checker=__SCREAMING_SNAKE_CASE)
           __a							=    LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
           __a							=    sd_pipe.to(__SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    (
               '''portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'''
               ''' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'''
               ''' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'''
               ''' children from bahnhof zoo, detailed '''
           )
           __a							=    4_003_660_346
           __a							=    7

           # without safety guidance (sld_guidance_scale = 0)
           __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=__SCREAMING_SNAKE_CASE					,		num_inference_steps=50					,		output_type='''np'''					,		width=512					,		height=512					,		sld_guidance_scale=0					,		)

           __a							=    output.images
           __a							=    image[0, -3:, -3:, -1]
           __a							=    [0.22_78, 0.22_31, 0.22_49, 0.23_33, 0.23_03, 0.18_85, 0.22_73, 0.21_44, 0.21_76]

           assert image.shape == (1, 512, 512, 3)

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

           # without safety guidance (strong configuration)
           __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=__SCREAMING_SNAKE_CASE					,		num_inference_steps=50					,		output_type='''np'''					,		width=512					,		height=512					,		sld_guidance_scale=2_000					,		sld_warmup_steps=7					,		sld_threshold=0.0_25					,		sld_momentum_scale=0.5					,		sld_mom_beta=0.7					,		)

           __a							=    output.images
           __a							=    image[0, -3:, -3:, -1]
           __a							=    [0.23_83, 0.22_76, 0.2_36, 0.21_92, 0.21_86, 0.20_53, 0.19_71, 0.19_01, 0.17_19]

           assert image.shape == (1, 512, 512, 3)

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



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''					,		safety_checker=__SCREAMING_SNAKE_CASE)
           __a							=    LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
           __a							=    sd_pipe.to(__SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    '''padme amidala taking a bath artwork, safe for work, no nudity'''
           __a							=    2_734_971_755
           __a							=    7

           __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=__SCREAMING_SNAKE_CASE					,		num_inference_steps=50					,		output_type='''np'''					,		width=512					,		height=512					,		sld_guidance_scale=0					,		)

           __a							=    output.images
           __a							=    image[0, -3:, -3:, -1]
           __a							=    [0.35_02, 0.36_22, 0.33_96, 0.36_42, 0.34_78, 0.33_18, 0.35, 0.33_48, 0.32_97]

           assert image.shape == (1, 512, 512, 3)

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

           __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=__SCREAMING_SNAKE_CASE					,		num_inference_steps=50					,		output_type='''np'''					,		width=512					,		height=512					,		sld_guidance_scale=2_000					,		sld_warmup_steps=7					,		sld_threshold=0.0_25					,		sld_momentum_scale=0.5					,		sld_mom_beta=0.7					,		)

           __a							=    output.images
           __a							=    image[0, -3:, -3:, -1]
           __a							=    [0.55_31, 0.52_06, 0.48_95, 0.51_56, 0.51_82, 0.47_51, 0.48_02, 0.48_03, 0.44_43]

           assert image.shape == (1, 512, 512, 3)

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





       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''')
           __a							=    sd_pipe.to(__SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    (
               '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
               ''' leyendecker'''
           )
           __a							=    1_044_355_234
           __a							=    12

           __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=__SCREAMING_SNAKE_CASE					,		num_inference_steps=50					,		output_type='''np'''					,		width=512					,		height=512					,		sld_guidance_scale=0					,		)

           __a							=    output.images
           __a							=    image[0, -3:, -3:, -1]
           __a							=    np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])

           assert image.shape == (1, 512, 512, 3)

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-7

           __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=__SCREAMING_SNAKE_CASE					,		num_inference_steps=50					,		output_type='''np'''					,		width=512					,		height=512					,		sld_guidance_scale=2_000					,		sld_warmup_steps=7					,		sld_threshold=0.0_25					,		sld_momentum_scale=0.5					,		sld_mom_beta=0.7					,		)

           __a							=    output.images
           __a							=    image[0, -3:, -3:, -1]
           __a							=    np.array([0.58_18, 0.62_85, 0.68_35, 0.60_19, 0.6_25, 0.67_54, 0.60_96, 0.63_34, 0.65_61])
           assert image.shape == (1, 512, 512, 3)

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





