





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  __lowerCAmelCase	(      _a,					_a,					_a,					unittest.TestCase					):
			lowerCamelCase_					:					Any	      =				StableDiffusionInpaintPipeline
			lowerCamelCase_					:					Any	      =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			lowerCamelCase_					:					Tuple	      =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			lowerCamelCase_					:					Optional[Any]	      =				frozenset(
			    []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			lowerCamelCase_					:					int	      =				frozenset([]					)






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


							'''simple docstring'''



							torch.manual_seed(0   )
							snake_case_						:   List[str]           =  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=__magic_name__ ,							)
							snake_case_						:   str           =  PNDMScheduler(skip_prk_steps=__magic_name__   )
							torch.manual_seed(0   )
							snake_case_						:   Union[str, Any]           =  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   )
							snake_case_						:   Optional[int]           =  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=1000 ,							hidden_act='''gelu''' ,							projection_dim=512 ,							)
							snake_case_						:   Union[str, Any]           =  CLIPTextModel(__magic_name__   )
							snake_case_						:   Any           =  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

							snake_case_						:   Optional[Any]           =  {
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}
							return components






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__=0   )					->       int:


							'''simple docstring'''



							snake_case_						:   Tuple           =  floats_tensor((1, 3, 32, 32) ,							rng=random.Random(__magic_name__   )   ).to(__magic_name__   )
							snake_case_						:   Tuple           =  image.cpu().permute(0 ,							2 ,							3 ,							1   )[0]
							snake_case_						:   str           =  Image.fromarray(np.uinta(__magic_name__   )   ).convert('''RGB'''   ).resize((64, 64)   )
							snake_case_						:   Dict           =  Image.fromarray(np.uinta(image + 4   )   ).convert('''RGB'''   ).resize((64, 64)   )
							if str(__magic_name__   ).startswith('''mps'''   ):
											snake_case_						:   int           =  torch.manual_seed(__magic_name__   )
							else:
											snake_case_						:   Any           =  torch.Generator(device=__magic_name__   ).manual_seed(__magic_name__   )
							snake_case_						:   Optional[Any]           =  {
							    '''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   )					->       Optional[int]:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							snake_case_						:   List[Any]           =  self.get_dummy_components()
							snake_case_						:   Union[str, Any]           =  StableDiffusionInpaintPipeline(**__magic_name__   )
							snake_case_						:   List[str]           =  sd_pipe.to(__magic_name__   )
							sd_pipe.set_progress_bar_config(disable=__magic_name__   )

							snake_case_						:   List[Any]           =  self.get_dummy_inputs(__magic_name__   )
							snake_case_						:   Dict           =  sd_pipe(**__magic_name__   ).images
							snake_case_						:   int           =  image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							snake_case_						:   int           =  np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]   )

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






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


							'''simple docstring'''



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




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






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


							'''simple docstring'''



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






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   str           =  load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/sd2-inpaint/init_image.png'''   )
							snake_case_						:   Optional[Any]           =  load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''   )
							snake_case_						:   Union[str, Any]           =  load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
							    '''/yellow_cat_sitting_on_a_park_bench.npy'''   )

							snake_case_						:   str           =  '''stabilityai/stable-diffusion-2-inpainting'''
							snake_case_						:   Optional[Any]           =  StableDiffusionInpaintPipeline.from_pretrained(__magic_name__ ,							safety_checker=__magic_name__   )
							pipe.to(__magic_name__   )
							pipe.set_progress_bar_config(disable=__magic_name__   )
							pipe.enable_attention_slicing()

							snake_case_						:   int           =  '''Face of a yellow cat, high resolution, sitting on a park bench'''

							snake_case_						:   Any           =  torch.manual_seed(0   )
							snake_case_						:   str           =  pipe(
							    prompt=__magic_name__ ,							image=__magic_name__ ,							mask_image=__magic_name__ ,							generator=__magic_name__ ,							output_type='''np''' ,							)
							snake_case_						:   List[Any]           =  output.images[0]

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






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


							'''simple docstring'''



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

							snake_case_						:   Optional[Any]           =  '''stabilityai/stable-diffusion-2-inpainting'''
							snake_case_						:   str           =  StableDiffusionInpaintPipeline.from_pretrained(
							    __magic_name__ ,							torch_dtype=torch.floataa ,							safety_checker=__magic_name__ ,							)
							pipe.to(__magic_name__   )
							pipe.set_progress_bar_config(disable=__magic_name__   )
							pipe.enable_attention_slicing()

							snake_case_						:   Tuple           =  '''Face of a yellow cat, high resolution, sitting on a park bench'''

							snake_case_						:   int           =  torch.manual_seed(0   )
							snake_case_						:   Optional[int]           =  pipe(
							    prompt=__magic_name__ ,							image=__magic_name__ ,							mask_image=__magic_name__ ,							generator=__magic_name__ ,							output_type='''np''' ,							)
							snake_case_						:   Optional[int]           =  output.images[0]

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






			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



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

							snake_case_						:   Optional[int]           =  load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/sd2-inpaint/init_image.png'''   )
							snake_case_						:   List[str]           =  load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''   )

							snake_case_						:   Any           =  '''stabilityai/stable-diffusion-2-inpainting'''
							snake_case_						:   str           =  PNDMScheduler.from_pretrained(__magic_name__ ,							subfolder='''scheduler'''   )
							snake_case_						:   List[Any]           =  StableDiffusionInpaintPipeline.from_pretrained(
							    __magic_name__ ,							safety_checker=__magic_name__ ,							scheduler=__magic_name__ ,							torch_dtype=torch.floataa ,							)
							pipe.to(__magic_name__   )
							pipe.set_progress_bar_config(disable=__magic_name__   )
							pipe.enable_attention_slicing(1   )
							pipe.enable_sequential_cpu_offload()

							snake_case_						:   str           =  '''Face of a yellow cat, high resolution, sitting on a park bench'''

							snake_case_						:   List[Any]           =  torch.manual_seed(0   )
							snake_case_						:   str           =  pipe(
							    prompt=__magic_name__ ,							image=__magic_name__ ,							mask_image=__magic_name__ ,							generator=__magic_name__ ,							num_inference_steps=2 ,							output_type='''np''' ,							)

							snake_case_						:   List[Any]           =  torch.cuda.max_memory_allocated()
							# make sure that less than 2.65 GB is allocated
							assert mem_bytes < 2.65 * 10**9







from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


lowerCAmelCase_    =    logging.get_logger(__name__)

lowerCAmelCase_    =    {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCAmelCase_    =    {
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

lowerCAmelCase_    =    {
    '''facebook/blenderbot_small-90M''': 5_1_2,
}



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					int	      =				VOCAB_FILES_NAMES
			lowerCamelCase_					:					List[Any]	      =				PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase_					:					int	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase_					:					Optional[Any]	      =				BlenderbotSmallTokenizer






			def __init__(self ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__="<|endoftext|>" ,							__magic_name__="<|endoftext|>" ,							__magic_name__="<|endoftext|>" ,							__magic_name__=False ,							__magic_name__=True ,							**__magic_name__ ,							)					->       Union[str, Any]:


							'''simple docstring'''



							super().__init__(
							    ByteLevelBPETokenizer(
							        vocab=__magic_name__ ,							merges=__magic_name__ ,							add_prefix_space=__magic_name__ ,							trim_offsets=__magic_name__ ,							) ,							bos_token=__magic_name__ ,							eos_token=__magic_name__ ,							unk_token=__magic_name__ ,							**__magic_name__ ,							)
							snake_case_						:   Optional[int]           =  add_prefix_space






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__=None   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
											return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ = None   )					->       List[int]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  [self.sep_token_id]
							snake_case_						:   Optional[Any]           =  [self.cls_token_id]

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