


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       (       _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			unittest.TestCase    ):
 _lowerCAmelCase	   =		StableDiffusionInpaintPipeline
 _lowerCAmelCase	   =		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 _lowerCAmelCase	   =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 _lowerCAmelCase	   =		frozenset(
     []    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 _lowerCAmelCase	   =		frozenset([]    )
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      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=__magic_name__ ,			)
      _a      =							PNDMScheduler(skip_prk_steps=__magic_name__  )
      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=1_28 ,			)
      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=10_00 ,			hidden_act='gelu' ,			projection_dim=5_12 ,			)
      _a      =							CLIPTextModel(__magic_name__  )
      _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 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__=0  )   ->							List[Any]:
      # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
      _a      =							floats_tensor((1, 3, 32, 32) ,			rng=random.Random(__magic_name__  )  ).to(__magic_name__  )
      _a      =							image.cpu().permute(0 ,			2 ,			3 ,			1  )[0]
      _a      =							Image.fromarray(np.uinta(__magic_name__  )  ).convert('RGB'  ).resize((64, 64)  )
      _a      =							Image.fromarray(np.uinta(image + 4  )  ).convert('RGB'  ).resize((64, 64)  )
      if str(__magic_name__  ).startswith('mps'  ):
           _a      =							torch.manual_seed(__magic_name__  )
      else:
           _a      =							torch.Generator(device=__magic_name__  ).manual_seed(__magic_name__  )
      _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 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							'cpu'  # ensure determinism for the device-dependent torch.Generator
      _a      =							self.get_dummy_components()
      _a      =							StableDiffusionInpaintPipeline(**__magic_name__  )
      _a      =							sd_pipe.to(__magic_name__  )
      sd_pipe.set_progress_bar_config(disable=__magic_name__  )

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

      assert image.shape == (1, 64, 64, 3)
      _a      =							np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6]  )

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




 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      super().test_inference_batch_single_identical(expected_max_diff=3e-3  )





@slow
@require_torch_gpu
class 	a       (       unittest.TestCase    ):
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      _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(__magic_name__ ,			safety_checker=__magic_name__  )
      pipe.to(__magic_name__  )
      pipe.set_progress_bar_config(disable=__magic_name__  )
      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=__magic_name__ ,			image=__magic_name__ ,			mask_image=__magic_name__ ,			generator=__magic_name__ ,			output_type='np' ,			)
      _a      =							output.images[0]

      assert image.shape == (5_12, 5_12, 3)
      assert np.abs(expected_image - image  ).max() < 9e-3
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      _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(
          __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()

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

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

      assert image.shape == (5_12, 5_12, 3)
      assert np.abs(expected_image - image  ).max() < 5e-1




 def 							__UpperCAmelCase						(	self  )   ->							str:
      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(__magic_name__ ,			subfolder='scheduler'  )
      _a      =							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()

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

      _a      =							torch.manual_seed(0  )
      _a      =							pipe(
          prompt=__magic_name__ ,			image=__magic_name__ ,			mask_image=__magic_name__ ,			generator=__magic_name__ ,			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 < 2.6_5 * 10**9





'''simple docstring'''






import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_   :      List[str]				    =      logging.get_logger(__name__)

a_   :      str				    =      {
    "microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		"""git_vision_model"""
 def __init__(	self ,			__magic_name__=7_68 ,			__magic_name__=30_72 ,			__magic_name__=12 ,			__magic_name__=12 ,			__magic_name__=3 ,			__magic_name__=2_24 ,			__magic_name__=16 ,			__magic_name__="quick_gelu" ,			__magic_name__=1e-5 ,			__magic_name__=0.0 ,			__magic_name__=0.0_2 ,			**__magic_name__ ,			)   ->							Union[str, Any]:
      super().__init__(**__magic_name__  )

      _a      =							hidden_size
      _a      =							intermediate_size
      _a      =							num_hidden_layers
      _a      =							num_attention_heads
      _a      =							num_channels
      _a      =							patch_size
      _a      =							image_size
      _a      =							initializer_range
      _a      =							attention_dropout
      _a      =							layer_norm_eps
      _a      =							hidden_act




 @classmethod
 def 							__UpperCAmelCase						(	cls ,			__magic_name__ ,			**__magic_name__  )   ->							"PretrainedConfig":
      cls._set_token_in_kwargs(__magic_name__  )

      _a ,				_a      =							cls.get_config_dict(__magic_name__ ,			**__magic_name__  )

      # get the vision config dict if we are loading from GITConfig
      if config_dict.get('model_type'  ) == "git":
           _a      =							config_dict['vision_config']

      if "model_type" in config_dict and hasattr(cls ,			'model_type'  ) and config_dict["model_type"] != cls.model_type:
           logger.warning(
               f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
               f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'  )

      return cls.from_dict(__magic_name__ ,			**__magic_name__  )





class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		"""git"""
 def __init__(	self ,			__magic_name__=None ,			__magic_name__=3_05_22 ,			__magic_name__=7_68 ,			__magic_name__=6 ,			__magic_name__=12 ,			__magic_name__=30_72 ,			__magic_name__="gelu" ,			__magic_name__=0.1 ,			__magic_name__=0.1 ,			__magic_name__=10_24 ,			__magic_name__=0.0_2 ,			__magic_name__=1e-12 ,			__magic_name__=0 ,			__magic_name__="absolute" ,			__magic_name__=True ,			__magic_name__=False ,			__magic_name__=1_01 ,			__magic_name__=1_02 ,			__magic_name__=None ,			**__magic_name__ ,			)   ->							Optional[int]:
      super().__init__(bos_token_id=__magic_name__ ,			eos_token_id=__magic_name__ ,			pad_token_id=__magic_name__ ,			**__magic_name__  )

      if vision_config is None:
           _a      =							{}
           logger.info('vision_config is None. initializing the GitVisionConfig with default values.'  )

      _a      =							GitVisionConfig(**__magic_name__  )
      _a      =							vocab_size
      _a      =							hidden_size
      _a      =							num_hidden_layers
      _a      =							num_attention_heads
      _a      =							hidden_act
      _a      =							intermediate_size
      _a      =							hidden_dropout_prob
      _a      =							attention_probs_dropout_prob
      _a      =							max_position_embeddings
      _a      =							initializer_range
      _a      =							layer_norm_eps
      _a      =							position_embedding_type
      _a      =							use_cache
      _a      =							tie_word_embeddings
      _a      =							num_image_with_embedding

      _a      =							bos_token_id
      _a      =							eos_token_id




 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							copy.deepcopy(self.__dict__  )
      _a      =							self.vision_config.to_dict()
      _a      =							self.__class__.model_type
      return output

