




import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





@skip_mps
class  lowerCamelCase_							(   _SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE ,     unittest.TestCase      ):


 '''simple docstring'''







 lowercase_       					=					StableDiffusionModelEditingPipeline
 lowercase_       					=					TEXT_TO_IMAGE_PARAMS
 lowercase_       					=					TEXT_TO_IMAGE_BATCH_PARAMS
 lowercase_       					=					TEXT_TO_IMAGE_IMAGE_PARAMS
 lowercase_       					=					TEXT_TO_IMAGE_IMAGE_PARAMS



 def    lowerCAmelCase_						(							self						:	List[Any]		):
       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    UNetaDConditionModel(
           block_out_channels=(32, 64)       ,       layers_per_block=2       ,       sample_size=32       ,       in_channels=4       ,       out_channels=4       ,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,       up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,       cross_attention_dim=32       ,       )
       SCREAMING_SNAKE_CASE_					=    DDIMScheduler()
       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       ,       )
       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=1_000       ,       )
       SCREAMING_SNAKE_CASE_					=    CLIPTextModel(_lowerCAmelCase		)
       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    lowerCAmelCase_						(							self						:	Tuple       ,       _lowerCAmelCase						:	str       ,       _lowerCAmelCase						:	Dict=0		):
       SCREAMING_SNAKE_CASE_					=    torch.manual_seed(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    {
           'prompt': 'A field of roses',
           'generator': generator,
           # Setting height and width to None to prevent OOMs on CPU.
           'height': None,
           'width': None,
           'num_inference_steps': 2,
           'guidance_scale': 6.0,
           'output_type': 'numpy',
       }
       return inputs



 def    lowerCAmelCase_						(							self						:	Dict		):
       SCREAMING_SNAKE_CASE_					=    'cpu'  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE_					=    self.get_dummy_components()
       SCREAMING_SNAKE_CASE_					=    StableDiffusionModelEditingPipeline(**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    sd_pipe.to(_lowerCAmelCase		)
       sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    self.get_dummy_inputs(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    sd_pipe(**_lowerCAmelCase		).images
       SCREAMING_SNAKE_CASE_					=    image[0, -3:, -3:, -1]
       assert image.shape == (1, 64, 64, 3)

       SCREAMING_SNAKE_CASE_					=    np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889]		)

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



 def    lowerCAmelCase_						(							self						:	Any		):
       SCREAMING_SNAKE_CASE_					=    'cpu'  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE_					=    self.get_dummy_components()
       SCREAMING_SNAKE_CASE_					=    StableDiffusionModelEditingPipeline(**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    sd_pipe.to(_lowerCAmelCase		)
       sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    self.get_dummy_inputs(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    'french fries'
       SCREAMING_SNAKE_CASE_					=    sd_pipe(**_lowerCAmelCase       ,       negative_prompt=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    output.images
       SCREAMING_SNAKE_CASE_					=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       SCREAMING_SNAKE_CASE_					=    np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913]		)

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



 def    lowerCAmelCase_						(							self						:	Union[str, Any]		):
       SCREAMING_SNAKE_CASE_					=    'cpu'  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE_					=    self.get_dummy_components()
       SCREAMING_SNAKE_CASE_					=    EulerAncestralDiscreteScheduler(
           beta_start=0.0_0085       ,       beta_end=0.012       ,       beta_schedule='scaled_linear'		)
       SCREAMING_SNAKE_CASE_					=    StableDiffusionModelEditingPipeline(**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    sd_pipe.to(_lowerCAmelCase		)
       sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

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

       assert image.shape == (1, 64, 64, 3)

       SCREAMING_SNAKE_CASE_					=    np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027]		)

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



 def    lowerCAmelCase_						(							self						:	List[str]		):
       SCREAMING_SNAKE_CASE_					=    'cpu'  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE_					=    self.get_dummy_components()
       SCREAMING_SNAKE_CASE_					=    PNDMScheduler()
       SCREAMING_SNAKE_CASE_					=    StableDiffusionModelEditingPipeline(**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    sd_pipe.to(_lowerCAmelCase		)
       sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    self.get_dummy_inputs(_lowerCAmelCase		)
       # the pipeline does not expect pndm so test if it raises error.
       with self.assertRaises(_lowerCAmelCase		):
             SCREAMING_SNAKE_CASE_					=    sd_pipe(**_lowerCAmelCase		).images



 def    lowerCAmelCase_						(							self						:	List[str]		):
       super().test_inference_batch_single_identical(expected_max_diff=5E-3		)




 def    lowerCAmelCase_						(							self						:	Union[str, Any]		):
       super().test_attention_slicing_forward_pass(expected_max_diff=5E-3		)





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


 '''simple docstring'''







 def    lowerCAmelCase_						(							self						:	Any		):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



 def    lowerCAmelCase_						(							self						:	int       ,       _lowerCAmelCase						:	str=0		):
       SCREAMING_SNAKE_CASE_					=    torch.manual_seed(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    {
           'prompt': 'A field of roses',
           'generator': generator,
           'num_inference_steps': 3,
           'guidance_scale': 7.5,
           'output_type': 'numpy',
       }
       return inputs



 def    lowerCAmelCase_						(							self						:	Tuple		):
       SCREAMING_SNAKE_CASE_					=    'CompVis/stable-diffusion-v1-4'
       SCREAMING_SNAKE_CASE_					=    StableDiffusionModelEditingPipeline.from_pretrained(_lowerCAmelCase       ,       safety_checker=_lowerCAmelCase		)
       pipe.to(_lowerCAmelCase		)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase		)
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE_					=    self.get_inputs()
       SCREAMING_SNAKE_CASE_					=    pipe(**_lowerCAmelCase		).images
       SCREAMING_SNAKE_CASE_					=    image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)

       SCREAMING_SNAKE_CASE_					=    np.array(
           [0.674_9496, 0.638_6453, 0.5144_3267, 0.6609_4905, 0.6192_1215, 0.549_1332, 0.574_4417, 0.5807_5106, 0.517_4658]		)

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

       # make sure image changes after editing
       pipe.edit_model('A pack of roses'       ,       'A pack of blue roses'		)

       SCREAMING_SNAKE_CASE_					=    pipe(**_lowerCAmelCase		).images
       SCREAMING_SNAKE_CASE_					=    image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)

       assert np.abs(expected_slice - image_slice		).max() > 1E-1




 def    lowerCAmelCase_						(							self						:	Dict		):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       SCREAMING_SNAKE_CASE_					=    'CompVis/stable-diffusion-v1-4'
       SCREAMING_SNAKE_CASE_					=    DDIMScheduler.from_pretrained(_lowerCAmelCase       ,       subfolder='scheduler'		)
       SCREAMING_SNAKE_CASE_					=    StableDiffusionModelEditingPipeline.from_pretrained(
           _lowerCAmelCase       ,       scheduler=_lowerCAmelCase       ,       safety_checker=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    pipe.to(_lowerCAmelCase		)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase		)
       pipe.enable_attention_slicing(1		)
       pipe.enable_sequential_cpu_offload()

       SCREAMING_SNAKE_CASE_					=    self.get_inputs()
       SCREAMING_SNAKE_CASE_					=    pipe(**_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    torch.cuda.max_memory_allocated()
       # make sure that less than 4.4 GB is allocated
       assert mem_bytes < 4.4 * 10**9





def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round((float(__UpperCAmelCase							) * 9 / 5) + 32    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round(float(__UpperCAmelCase							) + 2_7_3.1_5    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round((float(__UpperCAmelCase							) * 9 / 5) + 4_9_1.6_7    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round((float(__UpperCAmelCase							) - 32) * 5 / 9    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round(((float(__UpperCAmelCase							) - 32) * 5 / 9) + 2_7_3.1_5    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round(float(__UpperCAmelCase							) + 4_5_9.6_7    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round(float(__UpperCAmelCase							) - 2_7_3.1_5    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round(((float(__UpperCAmelCase							) - 2_7_3.1_5) * 9 / 5) + 32    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round((float(__UpperCAmelCase							) * 9 / 5)    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round((float(__UpperCAmelCase							) - 4_9_1.6_7) * 5 / 9    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round(float(__UpperCAmelCase							) - 4_5_9.6_7    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round((float(__UpperCAmelCase							) * 5 / 9)    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round((float(__UpperCAmelCase							) * 1.2_5 + 2_7_3.1_5)    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round((float(__UpperCAmelCase							) * 2.2_5 + 32)    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round((float(__UpperCAmelCase							) * 1.2_5)    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round((float(__UpperCAmelCase							) * 2.2_5 + 32 + 4_5_9.6_7)    ,    __UpperCAmelCase							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()