

'''simple docstring'''




from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:int      	=					["""image_processor""", """tokenizer"""]
 __SCREAMING_SNAKE_CASE					:List[str]      	=					"""BridgeTowerImageProcessor"""
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					("""RobertaTokenizer""", """RobertaTokenizerFast""")



 def __init__(   self		:   Dict			,				a__		:   Optional[int]			,				a__		:   Tuple      ):
      super().__init__(a__			,				a__      )



 def __call__(   self		:   Tuple			,				a__		:   Dict			,				a__		:   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,				a__		:   bool = True			,				a__		:   Union[bool, str, PaddingStrategy] = False			,				a__		:   Union[bool, str, TruncationStrategy] = None			,				a__		:   Optional[int] = None			,				a__		:   int = 0			,				a__		:   Optional[int] = None			,				a__		:   Optional[bool] = None			,				a__		:   Optional[bool] = None			,				a__		:   bool = False			,				a__		:   bool = False			,				a__		:   bool = False			,				a__		:   bool = False			,				a__		:   bool = True			,				a__		:   Optional[Union[str, TensorType]] = None			,				**a__		:   Tuple			,				):
      __magic_name__						       =    self.tokenizer(
          text=a__			,				add_special_tokens=a__			,				padding=a__			,				truncation=a__			,				max_length=a__			,				stride=a__			,				pad_to_multiple_of=a__			,				return_token_type_ids=a__			,				return_attention_mask=a__			,				return_overflowing_tokens=a__			,				return_special_tokens_mask=a__			,				return_offsets_mapping=a__			,				return_length=a__			,				verbose=a__			,				return_tensors=a__			,				**a__			,				)
      # add pixel_values + pixel_mask
      __magic_name__						       =    self.image_processor(
          a__			,				return_tensors=a__			,				do_normalize=a__			,				do_center_crop=a__			,				**a__      )
      encoding.update(a__      )

      return encoding



 def 			snake_case__  (   self		:   Dict			,				*a__		:   Optional[int]			,				**a__		:   Union[str, Any]      ):
      return self.tokenizer.batch_decode(*a__			,				**a__      )



 def 			snake_case__  (   self		:   Dict			,				*a__		:   Tuple			,				**a__		:   int      ):
      return self.tokenizer.decode(*a__			,				**a__      )



 @property
 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    self.tokenizer.model_input_names
      __magic_name__						       =    self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )









'''simple docstring'''




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



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      torch.manual_seed(0      )
      __magic_name__						       =    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=a__			,				)
      __magic_name__						       =    PNDMScheduler(skip_prk_steps=a__      )
      torch.manual_seed(0      )
      __magic_name__						       =    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      )
      __magic_name__						       =    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			,				)
      __magic_name__						       =    CLIPTextModel(a__      )
      __magic_name__						       =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

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



 def 			snake_case__  (   self		:   Any			,				a__		:   Optional[int]			,				a__		:   List[Any]=0      ):
      # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
      __magic_name__						       =    floats_tensor((1, 3, 32, 32)			,				rng=random.Random(a__      )      ).to(a__      )
      __magic_name__						       =    image.cpu().permute(0			,				2			,				3			,				1      )[0]
      __magic_name__						       =    Image.fromarray(np.uinta(a__      )      ).convert('''RGB'''      ).resize((64, 64)      )
      __magic_name__						       =    Image.fromarray(np.uinta(image + 4      )      ).convert('''RGB'''      ).resize((64, 64)      )
      if str(a__      ).startswith('''mps'''      ):
           __magic_name__						       =    torch.manual_seed(a__      )
      else:
           __magic_name__						       =    torch.Generator(device=a__      ).manual_seed(a__      )
      __magic_name__						       =    {
          '''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 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
      __magic_name__						       =    self.get_dummy_components()
      __magic_name__						       =    StableDiffusionInpaintPipeline(**a__      )
      __magic_name__						       =    sd_pipe.to(a__      )
      sd_pipe.set_progress_bar_config(disable=a__      )

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

      assert image.shape == (1, 64, 64, 3)
      __magic_name__						       =    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 			snake_case__  (   self		:   List[Any]      ):
      super().test_inference_batch_single_identical(expected_max_diff=3E-3      )






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



 def 			snake_case__  (   self		:   int      ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



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

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

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

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

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



 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/sd2-inpaint/init_image.png'''      )
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''      )
      __magic_name__						       =    load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
          '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''      )

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

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

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

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



 def 			snake_case__  (   self		:   List[Any]      ):
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

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

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

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

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

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






