

"""simple docstring"""

import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
							import jax


@require_flax
class 			_snake_case     :







						def        _UpperCamelCase    (self							)			->						int:
											__lowercase						, __lowercase												=			self.prepare_init_args_and_inputs_for_common()

											__lowercase												=			self.model_class(**UpperCAmelCase_							)
											__lowercase												=			model.init(inputs_dict['''prng_key''']     ,							inputs_dict['''sample''']							)
											jax.lax.stop_gradient(UpperCAmelCase_							)

											__lowercase												=			model.apply(UpperCAmelCase_     ,							inputs_dict['''sample''']							)

											if isinstance(UpperCAmelCase_     ,							UpperCAmelCase_							):
																__lowercase												=			output.sample

											self.assertIsNotNone(UpperCAmelCase_							)
											__lowercase												=			inputs_dict['''sample'''].shape
											self.assertEqual(output.shape     ,							UpperCAmelCase_     ,							'''Input and output shapes do not match'''							)







						def        _UpperCamelCase    (self							)			->						Optional[int]:
											__lowercase						, __lowercase												=			self.prepare_init_args_and_inputs_for_common()

											__lowercase												=			1_6
											__lowercase												=			(1_6, 3_2)

											__lowercase												=			self.model_class(**UpperCAmelCase_							)
											__lowercase												=			model.init(inputs_dict['''prng_key''']     ,							inputs_dict['''sample''']							)
											jax.lax.stop_gradient(UpperCAmelCase_							)

											__lowercase												=			model.apply(UpperCAmelCase_     ,							inputs_dict['''sample''']							)

											if isinstance(UpperCAmelCase_     ,							UpperCAmelCase_							):
																__lowercase												=			output.sample

											self.assertIsNotNone(UpperCAmelCase_							)
											__lowercase												=			inputs_dict['''sample'''].shape
											self.assertEqual(output.shape     ,							UpperCAmelCase_     ,							'''Input and output shapes do not match'''							)







						def        _UpperCamelCase    (self							)			->						List[Any]:
											__lowercase												=			'''kwargs''' in inspect.signature(self.model_class.__init__							).parameters
											__lowercase												=			len(self.model_class._deprecated_kwargs							) > 0

											if has_kwarg_in_model_class and not has_deprecated_kwarg:
																raise ValueError(
																    f'''{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs'''
																    ''' under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are'''
																    ''' no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ='''
																    ''' [<deprecated_argument>]`'''							)

											if not has_kwarg_in_model_class and has_deprecated_kwarg:
																raise ValueError(
																    f'''{self.model_class} doesn\'t have `**kwargs` in its __init__ method but has defined deprecated kwargs'''
																    ''' under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to'''
																    f''' {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument'''
																    ''' from `_deprecated_kwargs = [<deprecated_argument>]`'''							)





"""simple docstring"""

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 			_snake_case     (						UpperCamelCase						,			UpperCamelCase						,			UpperCamelCase						,			unittest.TestCase       ):
						__snake_case 			=     StableDiffusionModelEditingPipeline
						__snake_case 			=     TEXT_TO_IMAGE_PARAMS
						__snake_case 			=     TEXT_TO_IMAGE_BATCH_PARAMS
						__snake_case 			=     TEXT_TO_IMAGE_IMAGE_PARAMS
						__snake_case 			=     TEXT_TO_IMAGE_IMAGE_PARAMS







						def        _UpperCamelCase    (self							)			->						int:
											torch.manual_seed(0							)
											__lowercase												=			UNetaDConditionModel(
											    block_out_channels=(3_2, 6_4)     ,							layers_per_block=2     ,							sample_size=3_2     ,							in_channels=4     ,							out_channels=4     ,							down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')     ,							up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')     ,							cross_attention_dim=3_2     ,							)
											__lowercase												=			DDIMScheduler()
											torch.manual_seed(0							)
											__lowercase												=			AutoencoderKL(
											    block_out_channels=[3_2, 6_4]     ,							in_channels=3     ,							out_channels=3     ,							down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']     ,							up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,							latent_channels=4     ,							)
											torch.manual_seed(0							)
											__lowercase												=			CLIPTextConfig(
											    bos_token_id=0     ,							eos_token_id=2     ,							hidden_size=3_2     ,							intermediate_size=3_7     ,							layer_norm_eps=1e-05     ,							num_attention_heads=4     ,							num_hidden_layers=5     ,							pad_token_id=1     ,							vocab_size=1_0_0_0     ,							)
											__lowercase												=			CLIPTextModel(UpperCAmelCase_							)
											__lowercase												=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

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







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_=0							)			->						List[str]:
											__lowercase												=			torch.manual_seed(UpperCAmelCase_							)
											__lowercase												=			{
											    '''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        _UpperCamelCase    (self							)			->						Optional[int]:
											__lowercase												=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
											__lowercase												=			self.get_dummy_components()
											__lowercase												=			StableDiffusionModelEditingPipeline(**UpperCAmelCase_							)
											__lowercase												=			sd_pipe.to(UpperCAmelCase_							)
											sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_							)

											__lowercase												=			self.get_dummy_inputs(UpperCAmelCase_							)
											__lowercase												=			sd_pipe(**UpperCAmelCase_							).images
											__lowercase												=			image[0, -3:, -3:, -1]
											assert image.shape == (1, 6_4, 6_4, 3)

											__lowercase												=			np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]							)

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







						def        _UpperCamelCase    (self							)			->						Tuple:
											__lowercase												=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
											__lowercase												=			self.get_dummy_components()
											__lowercase												=			StableDiffusionModelEditingPipeline(**UpperCAmelCase_							)
											__lowercase												=			sd_pipe.to(UpperCAmelCase_							)
											sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_							)

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

											assert image.shape == (1, 6_4, 6_4, 3)

											__lowercase												=			np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]							)

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







						def        _UpperCamelCase    (self							)			->						Dict:
											__lowercase												=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
											__lowercase												=			self.get_dummy_components()
											__lowercase												=			EulerAncestralDiscreteScheduler(
											    beta_start=0.0_0_0_8_5     ,							beta_end=0.0_1_2     ,							beta_schedule='''scaled_linear'''							)
											__lowercase												=			StableDiffusionModelEditingPipeline(**UpperCAmelCase_							)
											__lowercase												=			sd_pipe.to(UpperCAmelCase_							)
											sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_							)

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

											assert image.shape == (1, 6_4, 6_4, 3)

											__lowercase												=			np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]							)

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







						def        _UpperCamelCase    (self							)			->						str:
											__lowercase												=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
											__lowercase												=			self.get_dummy_components()
											__lowercase												=			PNDMScheduler()
											__lowercase												=			StableDiffusionModelEditingPipeline(**UpperCAmelCase_							)
											__lowercase												=			sd_pipe.to(UpperCAmelCase_							)
											sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_							)

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







						def        _UpperCamelCase    (self							)			->						Tuple:
											super().test_inference_batch_single_identical(expected_max_diff=5e-3							)







						def        _UpperCamelCase    (self							)			->						str:
											super().test_attention_slicing_forward_pass(expected_max_diff=5e-3							)



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







						def        _UpperCamelCase    (self							)			->						int:
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_=0							)			->						int:
											__lowercase												=			torch.manual_seed(UpperCAmelCase_							)
											__lowercase												=			{
											    '''prompt''': '''A field of roses''',
											    '''generator''': generator,
											    '''num_inference_steps''': 3,
											    '''guidance_scale''': 7.5,
											    '''output_type''': '''numpy''',
											}
											return inputs







						def        _UpperCamelCase    (self							)			->						Optional[int]:
											__lowercase												=			'''CompVis/stable-diffusion-v1-4'''
											__lowercase												=			StableDiffusionModelEditingPipeline.from_pretrained(UpperCAmelCase_     ,							safety_checker=UpperCAmelCase_							)
											pipe.to(UpperCAmelCase_							)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_							)
											pipe.enable_attention_slicing()

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

											assert image.shape == (1, 5_1_2, 5_1_2, 3)

											__lowercase												=			np.array(
											    [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]							)

											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'''							)

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

											assert image.shape == (1, 5_1_2, 5_1_2, 3)

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







						def        _UpperCamelCase    (self							)			->						Optional[int]:
											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

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

											__lowercase												=			self.get_inputs()
											__lowercase												=			pipe(**UpperCAmelCase_							)

											__lowercase												=			torch.cuda.max_memory_allocated()
											# make sure that less than 4.4 GB is allocated
											assert mem_bytes < 4.4 * 1_0**9


