






'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_xlnet import XLNetTokenizer
else:
  a_					=	None


a_					=	logging.get_logger(__name__)

a_					=	{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

a_					=	{
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json',
    },
}

a_					=	{
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

a_					=	'▁'

# Segments (not really needed)
a_					=	0
a_					=	1
a_					=	2
a_					=	3
a_					=	4






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							VOCAB_FILES_NAMES
 snake_case_												=							PRETRAINED_VOCAB_FILES_MAP
 snake_case_												=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 snake_case_												=							"""left"""
 snake_case_												=							XLNetTokenizer




 def __init__( self		:							Optional[Any]   , __lowercase		:							Any=None   , __lowercase		:							Union[str, Any]=None   , __lowercase		:							Tuple=False   , __lowercase		:							Union[str, Any]=True   , __lowercase		:							str=False   , __lowercase		:							Any="<s>"   , __lowercase		:							Tuple="</s>"   , __lowercase		:							Union[str, Any]="<unk>"   , __lowercase		:							Dict="<sep>"   , __lowercase		:							Tuple="<pad>"   , __lowercase		:							List[Any]="<cls>"   , __lowercase		:							Dict="<mask>"   , __lowercase		:							Any=["<eop>", "<eod>"]   , **__lowercase		:							Union[str, Any]   , )		->  Dict:
       # Mask token behave like a normal word, i.e. include the space before it
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=AddedToken(__lowercase   , lstrip=__lowercase   , rstrip=__lowercase ) if isinstance(__lowercase   , __lowercase ) else mask_token

       super().__init__(
           vocab_file=__lowercase   , tokenizer_file=__lowercase   , do_lower_case=__lowercase   , remove_space=__lowercase   , keep_accents=__lowercase   , bos_token=__lowercase   , eos_token=__lowercase   , unk_token=__lowercase   , sep_token=__lowercase   , pad_token=__lowercase   , cls_token=__lowercase   , mask_token=__lowercase   , additional_special_tokens=__lowercase   , **__lowercase   , )

       SCREAMING_SNAKE_CASE__       :			Any													=3
       SCREAMING_SNAKE_CASE__       :			Tuple													=do_lower_case
       SCREAMING_SNAKE_CASE__       :			Any													=remove_space
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=keep_accents
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=vocab_file
       SCREAMING_SNAKE_CASE__       :			List[str]													=False if not self.vocab_file else True




 def        __magic_name__		( self		:							Optional[int]   , __lowercase		:							List[int]   , __lowercase		:							Optional[List[int]] = None )		->  List[int]:
       SCREAMING_SNAKE_CASE__       :			Tuple													=[self.sep_token_id]
       SCREAMING_SNAKE_CASE__       :			List[Any]													=[self.cls_token_id]
       if token_ids_a is None:
             return token_ids_a + sep + cls
       return token_ids_a + sep + token_ids_a + sep + cls




 def        __magic_name__		( self		:							Optional[int]   , __lowercase		:							List[int]   , __lowercase		:							Optional[List[int]] = None )		->  List[int]:
       SCREAMING_SNAKE_CASE__       :			str													=[self.sep_token_id]
       SCREAMING_SNAKE_CASE__       :			str													=[2]

       if token_ids_a is None:
             return len(token_ids_a + sep ) * [0] + cls_segment_id
       return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id




 def        __magic_name__		( self		:							List[str]   , __lowercase		:							str   , __lowercase		:							Optional[str] = None )		->  Tuple[str]:
       if not self.can_save_slow_tokenizer:
             raise ValueError(
                 '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                 '''tokenizer.''' )

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

       if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowercase ):
             copyfile(self.vocab_file   , __lowercase )

       return (out_vocab_file,)







'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






@slow
@require_torch_gpu
class        __SCREAMING_SNAKE_CASE (     unittest.TestCase			):




 def        __magic_name__		( self		:							List[str] )		->  Optional[Any]:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def        __magic_name__		( self		:							Union[str, Any] )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' )
       SCREAMING_SNAKE_CASE__       :			str													=sd_pipe.to(__lowercase )
       sd_pipe.set_progress_bar_config(disable=__lowercase )

       sd_pipe.set_scheduler('''sample_euler''' )

       SCREAMING_SNAKE_CASE__       :			List[Any]													='''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__       :			List[str]													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=sd_pipe([prompt]   , generator=__lowercase   , guidance_scale=9.0   , num_inference_steps=20   , output_type='''np''' )

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=output.images

       SCREAMING_SNAKE_CASE__       :			int													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			Any													=np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )

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




 def        __magic_name__		( self		:							List[str] )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			List[str]													=StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
       SCREAMING_SNAKE_CASE__       :			int													=sd_pipe.to(__lowercase )
       sd_pipe.set_progress_bar_config(disable=__lowercase )

       sd_pipe.set_scheduler('''sample_euler''' )

       SCREAMING_SNAKE_CASE__       :			Any													='''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__       :			Dict													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			Tuple													=sd_pipe([prompt]   , generator=__lowercase   , guidance_scale=9.0   , num_inference_steps=20   , output_type='''np''' )

       SCREAMING_SNAKE_CASE__       :			Dict													=output.images

       SCREAMING_SNAKE_CASE__       :			Dict													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			List[str]													=np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1




 def        __magic_name__		( self		:							List[str] )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			Any													=StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
       SCREAMING_SNAKE_CASE__       :			Tuple													=sd_pipe.to(__lowercase )
       sd_pipe.set_progress_bar_config(disable=__lowercase )

       sd_pipe.set_scheduler('''sample_dpmpp_2m''' )

       SCREAMING_SNAKE_CASE__       :			Tuple													='''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=sd_pipe(
           [prompt]   , generator=__lowercase   , guidance_scale=7.5   , num_inference_steps=15   , output_type='''np'''   , use_karras_sigmas=__lowercase   , )

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=output.images

       SCREAMING_SNAKE_CASE__       :			Dict													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=np.array(
           [0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048] )

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