






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 		_snake_case  (  A__   ,    A__   ,    A__   ,    unittest.TestCase					):
   _lowercase		:    int			     =							StableDiffusionInpaintPipeline
   _lowercase		:    str			     =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   _lowercase		:    Any			     =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   _lowercase		:    List[str]			     =							frozenset(
       []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   _lowercase		:    int			     =							frozenset([]					)






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
      torch.manual_seed(0)
      SCREAMING_SNAKE_CASE						= 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		,					)
      SCREAMING_SNAKE_CASE						= PNDMScheduler(skip_prk_steps=a)
      torch.manual_seed(0)
      SCREAMING_SNAKE_CASE						= 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)
      SCREAMING_SNAKE_CASE						= 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		,					)
      SCREAMING_SNAKE_CASE						= CLIPTextModel(a)
      SCREAMING_SNAKE_CASE						= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

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






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

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

      assert image.shape == (1, 64, 64, 3)
      SCREAMING_SNAKE_CASE						= 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    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
      super().test_inference_batch_single_identical(expected_max_diff=3E-3)



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






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






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

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

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

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

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






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

      SCREAMING_SNAKE_CASE						= 'stabilityai/stable-diffusion-2-inpainting'
      SCREAMING_SNAKE_CASE						= 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()

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

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

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







   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

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

      SCREAMING_SNAKE_CASE						= 'stabilityai/stable-diffusion-2-inpainting'
      SCREAMING_SNAKE_CASE						= PNDMScheduler.from_pretrained(a		,					subfolder='scheduler')
      SCREAMING_SNAKE_CASE						= 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()

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

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

      SCREAMING_SNAKE_CASE						= 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, Union

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

class 		_snake_case  (  A__					):
			_lowercase		:    Union[str, Any]			     =							['''image_processor''', '''tokenizer''']
			_lowercase		:    Optional[int]			     =							'''Pix2StructImageProcessor'''
			_lowercase		:    str			     =							('''T5Tokenizer''', '''T5TokenizerFast''')






			def __init__(       self		,					a		,					a)      ->       Any:
						SCREAMING_SNAKE_CASE						= False
						super().__init__(a		,					a)






			def __call__(       self		,					a=None		,					a = None		,					a = True		,					a = False		,					a = None		,					a = None		,					a = 2048		,					a = 0		,					a = None		,					a = None		,					a = False		,					a = False		,					a = False		,					a = False		,					a = False		,					a = True		,					a = None		,					**a		,					)      ->       BatchEncoding:
						if images is None and text is None:
									raise ValueError('You have to specify either images or text.')

						# Get only text
						if images is None and not self.image_processor.is_vqa:
									SCREAMING_SNAKE_CASE						= self.tokenizer
									SCREAMING_SNAKE_CASE						= self.tokenizer(
									    text=a		,					add_special_tokens=a		,					padding=a		,					truncation=a		,					max_length=a		,					stride=a		,					pad_to_multiple_of=a		,					return_attention_mask=a		,					return_overflowing_tokens=a		,					return_special_tokens_mask=a		,					return_offsets_mapping=a		,					return_token_type_ids=a		,					return_length=a		,					verbose=a		,					return_tensors=a		,					**a		,					)
									return text_encoding

						if not self.image_processor.is_vqa:
									# add pixel_values
									SCREAMING_SNAKE_CASE						= self.image_processor(
									    a		,					return_tensors=a		,					max_patches=a		,					**a)
						else:
									# add pixel_values and bbox
									SCREAMING_SNAKE_CASE						= self.image_processor(
									    a		,					return_tensors=a		,					max_patches=a		,					header_text=a		,					**a)

						if text is not None and not self.image_processor.is_vqa:
									SCREAMING_SNAKE_CASE						= self.tokenizer(
									    text=a		,					add_special_tokens=a		,					padding=a		,					truncation=a		,					max_length=a		,					stride=a		,					pad_to_multiple_of=a		,					return_attention_mask=a		,					return_overflowing_tokens=a		,					return_special_tokens_mask=a		,					return_offsets_mapping=a		,					return_token_type_ids=a		,					return_length=a		,					verbose=a		,					return_tensors=a		,					**a		,					)

									if "attention_mask" in text_encoding:
												SCREAMING_SNAKE_CASE						= text_encoding.pop('attention_mask')
									if "input_ids" in text_encoding:
												SCREAMING_SNAKE_CASE						= text_encoding.pop('input_ids')
						else:
									SCREAMING_SNAKE_CASE						= None

						if text_encoding is not None:
									encoding_image_processor.update(a)

						return encoding_image_processor






			def    SCREAMING_SNAKE_CASE__    (       self		,					*a		,					**a)      ->       List[Any]:
						return self.tokenizer.batch_decode(*a		,					**a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					*a		,					**a)      ->       Optional[int]:
						return self.tokenizer.decode(*a		,					**a)







			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= self.tokenizer.model_input_names
						SCREAMING_SNAKE_CASE						= self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))




