



import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class    _A	(     __UpperCAmelCase				,__UpperCAmelCase				,__UpperCAmelCase				,unittest.TestCase   ):
       UpperCamelCase__  :   Union[str, Any]						 =						StableDiffusionInpaintPipeline
       UpperCamelCase__  :   Union[str, Any]						 =						TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
       UpperCamelCase__  :   Tuple						 =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       UpperCamelCase__  :   int						 =						frozenset(
           []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       UpperCamelCase__  :   int						 =						frozenset([]   )



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           torch.manual_seed(0)
           __a							=    UNetaDConditionModel(
               block_out_channels=(32, 64)					,		layers_per_block=2					,		sample_size=32					,		in_channels=9					,		out_channels=4					,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')					,		cross_attention_dim=32					,		attention_head_dim=(2, 4)					,		use_linear_projection=__SCREAMING_SNAKE_CASE					,		)
           __a							=    PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE)
           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					,		sample_size=128					,		)
           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					,		hidden_act='''gelu'''					,		projection_dim=512					,		)
           __a							=    CLIPTextModel(__SCREAMING_SNAKE_CASE)
           __a							=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

           __a							=    {
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''vae''': vae,
               '''text_encoder''': text_encoder,
               '''tokenizer''': tokenizer,
               '''safety_checker''': None,
               '''feature_extractor''': None,
           }
           return components



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Tuple=0):


           '''simple docstring'''

           __a							=    floats_tensor((1, 3, 32, 32)					,		rng=random.Random(__SCREAMING_SNAKE_CASE)).to(__SCREAMING_SNAKE_CASE)
           __a							=    image.cpu().permute(0					,		2					,		3					,		1)[0]
           __a							=    Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE)).convert('''RGB''').resize((64, 64))
           __a							=    Image.fromarray(np.uinta(image + 4)).convert('''RGB''').resize((64, 64))
           if str(__SCREAMING_SNAKE_CASE).startswith('''mps'''):
               __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           else:
               __a							=    torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    {
               '''prompt''': '''A painting of a squirrel eating a burger''',
               '''image''': init_image,
               '''mask_image''': mask_image,
               '''generator''': generator,
               '''num_inference_steps''': 2,
               '''guidance_scale''': 6.0,
               '''output_type''': '''numpy''',
           }
           return inputs



       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           __a							=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           __a							=    self.get_dummy_components()
           __a							=    StableDiffusionInpaintPipeline(**__SCREAMING_SNAKE_CASE)
           __a							=    sd_pipe.to(__SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    self.get_dummy_inputs(__SCREAMING_SNAKE_CASE)
           __a							=    sd_pipe(**__SCREAMING_SNAKE_CASE).images
           __a							=    image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           __a							=    np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76])

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





       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           super().test_inference_batch_single_identical(expected_max_diff=3E-3)


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



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/sd2-inpaint/init_image.png''')
           __a							=    load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''')
           __a							=    load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
               '''/yellow_cat_sitting_on_a_park_bench.npy''')

           __a							=    '''stabilityai/stable-diffusion-2-inpainting'''
           __a							=    StableDiffusionInpaintPipeline.from_pretrained(__SCREAMING_SNAKE_CASE					,		safety_checker=__SCREAMING_SNAKE_CASE)
           pipe.to(__SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)
           pipe.enable_attention_slicing()

           __a							=    '''Face of a yellow cat, high resolution, sitting on a park bench'''

           __a							=    torch.manual_seed(0)
           __a							=    pipe(
               prompt=__SCREAMING_SNAKE_CASE					,		image=__SCREAMING_SNAKE_CASE					,		mask_image=__SCREAMING_SNAKE_CASE					,		generator=__SCREAMING_SNAKE_CASE					,		output_type='''np'''					,		)
           __a							=    output.images[0]

           assert image.shape == (512, 512, 3)
           assert np.abs(expected_image - image).max() < 9E-3



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


           '''simple docstring'''

           __a							=    load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/sd2-inpaint/init_image.png''')
           __a							=    load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''')
           __a							=    load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
               '''/yellow_cat_sitting_on_a_park_bench_fp16.npy''')

           __a							=    '''stabilityai/stable-diffusion-2-inpainting'''
           __a							=    StableDiffusionInpaintPipeline.from_pretrained(
               __SCREAMING_SNAKE_CASE					,		torch_dtype=torch.floataa					,		safety_checker=__SCREAMING_SNAKE_CASE					,		)
           pipe.to(__SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)
           pipe.enable_attention_slicing()

           __a							=    '''Face of a yellow cat, high resolution, sitting on a park bench'''

           __a							=    torch.manual_seed(0)
           __a							=    pipe(
               prompt=__SCREAMING_SNAKE_CASE					,		image=__SCREAMING_SNAKE_CASE					,		mask_image=__SCREAMING_SNAKE_CASE					,		generator=__SCREAMING_SNAKE_CASE					,		output_type='''np'''					,		)
           __a							=    output.images[0]

           assert image.shape == (512, 512, 3)
           assert np.abs(expected_image - image).max() < 5E-1





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


           '''simple docstring'''

           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

           __a							=    load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/sd2-inpaint/init_image.png''')
           __a							=    load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''')

           __a							=    '''stabilityai/stable-diffusion-2-inpainting'''
           __a							=    PNDMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE					,		subfolder='''scheduler''')
           __a							=    StableDiffusionInpaintPipeline.from_pretrained(
               __SCREAMING_SNAKE_CASE					,		safety_checker=__SCREAMING_SNAKE_CASE					,		scheduler=__SCREAMING_SNAKE_CASE					,		torch_dtype=torch.floataa					,		)
           pipe.to(__SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)
           pipe.enable_attention_slicing(1)
           pipe.enable_sequential_cpu_offload()

           __a							=    '''Face of a yellow cat, high resolution, sitting on a park bench'''

           __a							=    torch.manual_seed(0)
           __a							=    pipe(
               prompt=__SCREAMING_SNAKE_CASE					,		image=__SCREAMING_SNAKE_CASE					,		mask_image=__SCREAMING_SNAKE_CASE					,		generator=__SCREAMING_SNAKE_CASE					,		num_inference_steps=2					,		output_type='''np'''					,		)

           __a							=    torch.cuda.max_memory_allocated()
           # make sure that less than 2.65 GB is allocated
           assert mem_bytes < 2.65 * 10**9







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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


__snake_case						:Dict						=					logging.get_logger(__name__)

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

__snake_case						:Dict						=					{
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}

class    _A	(     __UpperCAmelCase   ):



       def __init__(		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=False					,		__SCREAMING_SNAKE_CASE							:					List[Any]=True					,		__SCREAMING_SNAKE_CASE							:					List[Any]=False					,		__SCREAMING_SNAKE_CASE							:					Optional[int]="<s>"					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]="</s>"					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]="<unk>"					,		__SCREAMING_SNAKE_CASE							:					Optional[int]="<sep>"					,		__SCREAMING_SNAKE_CASE							:					Optional[int]="<pad>"					,		__SCREAMING_SNAKE_CASE							:					List[Any]="<cls>"					,		__SCREAMING_SNAKE_CASE							:					Optional[int]="<mask>"					,		__SCREAMING_SNAKE_CASE							:					Any=["<eop>", "<eod>"]					,		__SCREAMING_SNAKE_CASE							:					Optional[Dict[str, Any]] = None					,		**__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		):


           '''simple docstring'''

           __a							=    AddedToken(__SCREAMING_SNAKE_CASE					,		lstrip=__SCREAMING_SNAKE_CASE					,		rstrip=__SCREAMING_SNAKE_CASE) if isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE) else mask_token

           __a							=    {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               do_lower_case=__SCREAMING_SNAKE_CASE					,		remove_space=__SCREAMING_SNAKE_CASE					,		keep_accents=__SCREAMING_SNAKE_CASE					,		bos_token=__SCREAMING_SNAKE_CASE					,		eos_token=__SCREAMING_SNAKE_CASE					,		unk_token=__SCREAMING_SNAKE_CASE					,		sep_token=__SCREAMING_SNAKE_CASE					,		pad_token=__SCREAMING_SNAKE_CASE					,		cls_token=__SCREAMING_SNAKE_CASE					,		mask_token=__SCREAMING_SNAKE_CASE					,		additional_special_tokens=__SCREAMING_SNAKE_CASE					,		sp_model_kwargs=self.sp_model_kwargs					,		**__SCREAMING_SNAKE_CASE					,		)

           __a							=    3

           __a							=    do_lower_case
           __a							=    remove_space
           __a							=    keep_accents
           __a							=    vocab_file

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

           try:
               import jieba
           except ModuleNotFoundError as error:
               raise error.__class__(
                   '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
                   '''See https://pypi.org/project/jieba/ for installation.''')
           __a							=    jieba
           __a							=    str.maketrans(''' \n'''					,		'''\u2582\u2583''')



       @property
       # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           return len(self.sp_model)



       def 		_lowerCamelCase      (		self							:					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							:					Any):


           '''simple docstring'''

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



       def __setstate__(		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					List[str]):


           '''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							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           if self.remove_space:
               __a							=    ''' '''.join(inputs.strip().split())
           else:
               __a							=    inputs
           __a							=    outputs.replace('''``'''					,		'''"''').replace('''\'\''''					,		'''"''')

           if not self.keep_accents:
               __a							=    unicodedata.normalize('''NFKD'''					,		__SCREAMING_SNAKE_CASE)
               __a							=    ''''''.join([c for c in outputs if not unicodedata.combining(__SCREAMING_SNAKE_CASE)])
           if self.do_lower_case:
               __a							=    outputs.lower()

           return outputs



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           __a							=    self.preprocess_text(__SCREAMING_SNAKE_CASE)
           __a							=    self.sp_model.encode(__SCREAMING_SNAKE_CASE					,		out_type=__SCREAMING_SNAKE_CASE)
           __a							=    []
           for piece in pieces:
               if len(__SCREAMING_SNAKE_CASE) > 1 and piece[-1] == str(''',''') and piece[-2].isdigit():
                   __a							=    self.sp_model.EncodeAsPieces(piece[:-1].replace(__SCREAMING_SNAKE_CASE					,		''''''))
                   if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                       if len(cur_pieces[0]) == 1:
                           __a							=    cur_pieces[1:]
                       else:
                           __a							=    cur_pieces[0][1:]
                   cur_pieces.append(piece[-1])
                   new_pieces.extend(__SCREAMING_SNAKE_CASE)
               else:
                   new_pieces.append(__SCREAMING_SNAKE_CASE)

           return new_pieces



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


           '''simple docstring'''

           return self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE)



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


           '''simple docstring'''

           return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE)



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


           '''simple docstring'''

           __a							=    ''''''.join(__SCREAMING_SNAKE_CASE).replace(__SCREAMING_SNAKE_CASE					,		''' ''').strip()
           return out_string



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


           '''simple docstring'''

           __a							=    [self.sep_token_id]
           __a							=    [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 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					List[int]					,		__SCREAMING_SNAKE_CASE							:					Optional[List[int]] = None					,		__SCREAMING_SNAKE_CASE							:					bool = False):


           '''simple docstring'''

           if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__SCREAMING_SNAKE_CASE					,		token_ids_a=__SCREAMING_SNAKE_CASE					,		already_has_special_tokens=__SCREAMING_SNAKE_CASE)

           if token_ids_a is not None:
               return ([0] * len(__SCREAMING_SNAKE_CASE)) + [1] + ([0] * len(__SCREAMING_SNAKE_CASE)) + [1, 1]
           return ([0] * len(__SCREAMING_SNAKE_CASE)) + [1, 1]



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					List[int]					,		__SCREAMING_SNAKE_CASE							:					Optional[List[int]] = None):


           '''simple docstring'''

           __a							=    [self.sep_token_id]
           __a							=    [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 		_lowerCamelCase      (		self							:					Optional[int]					,		__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,)





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


           '''simple docstring'''

           __a							=    super()._decode(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           __a							=    text.replace(''' '''					,		'''''').replace('''\u2582'''					,		''' ''').replace('''\u2583'''					,		'''\n''')
           return text





