


import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 							lowerCamelCase   (  _UpperCamelCase							,						_UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		Dict										=						StableDiffusionDiffEditPipeline
	_lowerCAmelCase     :		Dict										=						TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
	_lowerCAmelCase     :		List[Any]										=						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
	_lowerCAmelCase     :		Tuple										=						frozenset(
	    [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	_lowerCAmelCase     :		int										=						frozenset([] )


	def  A(    self):
							torch.manual_seed(0)
							__UpperCAmelCase			:							List[str]	    =	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				,     attention_head_dim=(2, 4)				,     use_linear_projection=lowercase__				,     )
							__UpperCAmelCase			:							str	    =	DDIMScheduler(
							    beta_start=0.0_0_0_8_5				,     beta_end=0.0_1_2				,     beta_schedule='''scaled_linear'''				,     clip_sample=lowercase__				,     set_alpha_to_one=lowercase__				,     )
							__UpperCAmelCase			:							str	    =	DDIMInverseScheduler(
							    beta_start=0.0_0_0_8_5				,     beta_end=0.0_1_2				,     beta_schedule='''scaled_linear'''				,     clip_sample=lowercase__				,     set_alpha_to_zero=lowercase__				,     )
							torch.manual_seed(0)
							__UpperCAmelCase			:							Any	    =	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				,     sample_size=1_2_8				,     )
							torch.manual_seed(0)
							__UpperCAmelCase			:							List[str]	    =	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				,     hidden_act='''gelu'''				,     projection_dim=5_1_2				,     )
							__UpperCAmelCase			:							Optional[Any]	    =	CLIPTextModel(lowercase__)
							__UpperCAmelCase			:							Dict	    =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

							__UpperCAmelCase			:							Optional[Any]	    =	{
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''inverse_scheduler''': inverse_scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}

							return components


	def  A(    self				,     lowercase__				,     lowercase__=0):
							__UpperCAmelCase			:							Dict	    =	floats_tensor((1, 1_6, 1_6)				,     rng=random.Random(lowercase__)).to(lowercase__)
							__UpperCAmelCase			:							List[str]	    =	floats_tensor((1, 2, 4, 1_6, 1_6)				,     rng=random.Random(lowercase__)).to(lowercase__)
							if str(lowercase__).startswith('''mps'''):
													__UpperCAmelCase			:							Any	    =	torch.manual_seed(lowercase__)
							else:
													__UpperCAmelCase			:							List[Any]	    =	torch.Generator(device=lowercase__).manual_seed(lowercase__)
							__UpperCAmelCase			:							List[Any]	    =	{
							    '''prompt''': '''a dog and a newt''',
							    '''mask_image''': mask,
							    '''image_latents''': latents,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''inpaint_strength''': 1.0,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							}

							return inputs


	def  A(    self				,     lowercase__				,     lowercase__=0):
							__UpperCAmelCase			:							Tuple	    =	floats_tensor((1, 3, 3_2, 3_2)				,     rng=random.Random(lowercase__)).to(lowercase__)
							__UpperCAmelCase			:							int	    =	image.cpu().permute(0				,     2				,     3				,     1)[0]
							__UpperCAmelCase			:							int	    =	Image.fromarray(np.uinta(lowercase__)).convert('''RGB''')
							if str(lowercase__).startswith('''mps'''):
													__UpperCAmelCase			:							List[str]	    =	torch.manual_seed(lowercase__)
							else:
													__UpperCAmelCase			:							Optional[Any]	    =	torch.Generator(device=lowercase__).manual_seed(lowercase__)
							__UpperCAmelCase			:							List[str]	    =	{
							    '''image''': image,
							    '''source_prompt''': '''a cat and a frog''',
							    '''target_prompt''': '''a dog and a newt''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''num_maps_per_mask''': 2,
							    '''mask_encode_strength''': 1.0,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							}

							return inputs


	def  A(    self				,     lowercase__				,     lowercase__=0):
							__UpperCAmelCase			:							Any	    =	floats_tensor((1, 3, 3_2, 3_2)				,     rng=random.Random(lowercase__)).to(lowercase__)
							__UpperCAmelCase			:							Optional[int]	    =	image.cpu().permute(0				,     2				,     3				,     1)[0]
							__UpperCAmelCase			:							Optional[Any]	    =	Image.fromarray(np.uinta(lowercase__)).convert('''RGB''')
							if str(lowercase__).startswith('''mps'''):
													__UpperCAmelCase			:							Optional[Any]	    =	torch.manual_seed(lowercase__)
							else:
													__UpperCAmelCase			:							List[str]	    =	torch.Generator(device=lowercase__).manual_seed(lowercase__)
							__UpperCAmelCase			:							int	    =	{
							    '''image''': image,
							    '''prompt''': '''a cat and a frog''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''inpaint_strength''': 1.0,
							    '''guidance_scale''': 6.0,
							    '''decode_latents''': True,
							    '''output_type''': '''numpy''',
							}
							return inputs


	def  A(    self):
							if not hasattr(self.pipeline_class				,     '''_optional_components'''):
													return

							__UpperCAmelCase			:							Optional[int]	    =	self.get_dummy_components()
							__UpperCAmelCase			:							str	    =	self.pipeline_class(**lowercase__)
							pipe.to(lowercase__)
							pipe.set_progress_bar_config(disable=lowercase__)

							# set all optional components to None and update pipeline config accordingly
							for optional_component in pipe._optional_components:
													setattr(lowercase__				,     lowercase__				,     lowercase__)
							pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components})

							__UpperCAmelCase			:							Tuple	    =	self.get_dummy_inputs(lowercase__)
							__UpperCAmelCase			:							List[str]	    =	pipe(**lowercase__)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(lowercase__)
													__UpperCAmelCase			:							Optional[Any]	    =	self.pipeline_class.from_pretrained(lowercase__)
													pipe_loaded.to(lowercase__)
													pipe_loaded.set_progress_bar_config(disable=lowercase__)

							for optional_component in pipe._optional_components:
													self.assertTrue(
													    getattr(lowercase__				,     lowercase__) is None				,     F"`{optional_component}` did not stay set to None after loading."				,     )

							__UpperCAmelCase			:							Optional[Any]	    =	self.get_dummy_inputs(lowercase__)
							__UpperCAmelCase			:							List[str]	    =	pipe_loaded(**lowercase__)[0]

							__UpperCAmelCase			:							Optional[int]	    =	np.abs(output - output_loaded).max()
							self.assertLess(lowercase__				,     1e-4)


	def  A(    self):
							__UpperCAmelCase			:							int	    =	'''cpu'''

							__UpperCAmelCase			:							List[Any]	    =	self.get_dummy_components()
							__UpperCAmelCase			:							Tuple	    =	self.pipeline_class(**lowercase__)
							pipe.to(lowercase__)
							pipe.set_progress_bar_config(disable=lowercase__)

							__UpperCAmelCase			:							str	    =	self.get_dummy_mask_inputs(lowercase__)
							__UpperCAmelCase			:							List[Any]	    =	pipe.generate_mask(**lowercase__)
							__UpperCAmelCase			:							Tuple	    =	mask[0, -3:, -3:]

							self.assertEqual(mask.shape				,     (1, 1_6, 1_6))
							__UpperCAmelCase			:							str	    =	np.array([0] * 9)
							__UpperCAmelCase			:							List[Any]	    =	np.abs(mask_slice.flatten() - expected_slice).max()
							self.assertLessEqual(lowercase__				,     1e-3)
							self.assertEqual(mask[0, -3, -4]				,     0)


	def  A(    self):
							__UpperCAmelCase			:							int	    =	'''cpu'''

							__UpperCAmelCase			:							Tuple	    =	self.get_dummy_components()
							__UpperCAmelCase			:							Tuple	    =	self.pipeline_class(**lowercase__)
							pipe.to(lowercase__)
							pipe.set_progress_bar_config(disable=lowercase__)

							__UpperCAmelCase			:							List[str]	    =	self.get_dummy_inversion_inputs(lowercase__)
							__UpperCAmelCase			:							int	    =	pipe.invert(**lowercase__).images
							__UpperCAmelCase			:							Optional[Any]	    =	image[0, -1, -3:, -3:]

							self.assertEqual(image.shape				,     (2, 3_2, 3_2, 3))
							__UpperCAmelCase			:							Union[str, Any]	    =	np.array(
							    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]				,     )
							__UpperCAmelCase			:							List[Any]	    =	np.abs(image_slice.flatten() - expected_slice).max()
							self.assertLessEqual(lowercase__				,     1e-3)


	def  A(    self):
							super().test_inference_batch_single_identical(expected_max_diff=5e-3)


	def  A(    self):
							__UpperCAmelCase			:							str	    =	'''cpu'''

							__UpperCAmelCase			:							Tuple	    =	self.get_dummy_components()

							__UpperCAmelCase			:							List[str]	    =	{'''beta_start''': 0.0_0_0_8_5, '''beta_end''': 0.0_1_2, '''beta_schedule''': '''scaled_linear'''}
							__UpperCAmelCase			:							Dict	    =	DPMSolverMultistepScheduler(**lowercase__)
							__UpperCAmelCase			:							Optional[Any]	    =	DPMSolverMultistepInverseScheduler(**lowercase__)

							__UpperCAmelCase			:							List[str]	    =	self.pipeline_class(**lowercase__)
							pipe.to(lowercase__)
							pipe.set_progress_bar_config(disable=lowercase__)

							__UpperCAmelCase			:							List[Any]	    =	self.get_dummy_inversion_inputs(lowercase__)
							__UpperCAmelCase			:							Any	    =	pipe.invert(**lowercase__).images
							__UpperCAmelCase			:							Tuple	    =	image[0, -1, -3:, -3:]

							self.assertEqual(image.shape				,     (2, 3_2, 3_2, 3))
							__UpperCAmelCase			:							List[str]	    =	np.array(
							    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]				,     )
							__UpperCAmelCase			:							List[Any]	    =	np.abs(image_slice.flatten() - expected_slice).max()
							self.assertLessEqual(lowercase__				,     1e-3)






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


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


	@classmethod
	def  A(    cls):
							__UpperCAmelCase			:							Tuple	    =	load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png''')

							__UpperCAmelCase			:							List[Any]	    =	raw_image.convert('''RGB''').resize((7_6_8, 7_6_8))

							__UpperCAmelCase			:							Optional[Any]	    =	raw_image


	def  A(    self):
							__UpperCAmelCase			:							Any	    =	torch.manual_seed(0)

							__UpperCAmelCase			:							Optional[Any]	    =	StableDiffusionDiffEditPipeline.from_pretrained(
							    '''stabilityai/stable-diffusion-2-1'''				,     safety_checker=lowercase__				,     torch_dtype=torch.floataa)
							__UpperCAmelCase			:							List[Any]	    =	DDIMScheduler.from_config(pipe.scheduler.config)
							__UpperCAmelCase			:							str	    =	DDIMInverseScheduler.from_config(pipe.scheduler.config)
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=lowercase__)

							__UpperCAmelCase			:							Optional[Any]	    =	'''a bowl of fruit'''
							__UpperCAmelCase			:							List[Any]	    =	'''a bowl of pears'''

							__UpperCAmelCase			:							str	    =	pipe.generate_mask(
							    image=self.raw_image				,     source_prompt=lowercase__				,     target_prompt=lowercase__				,     generator=lowercase__				,     )

							__UpperCAmelCase			:							Dict	    =	pipe.invert(
							    prompt=lowercase__				,     image=self.raw_image				,     inpaint_strength=0.7				,     generator=lowercase__).latents

							__UpperCAmelCase			:							Any	    =	pipe(
							    prompt=lowercase__				,     mask_image=lowercase__				,     image_latents=lowercase__				,     generator=lowercase__				,     negative_prompt=lowercase__				,     inpaint_strength=0.7				,     output_type='''numpy'''				,     ).images[0]

							__UpperCAmelCase			:							Optional[int]	    =	(
							    np.array(
							        load_image(
							            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							            '''/diffedit/pears.png''').resize((7_6_8, 7_6_8)))
							    / 2_5_5
							)
							assert np.abs((expected_image - image).max()) < 5e-1


	def  A(    self):
							__UpperCAmelCase			:							Optional[int]	    =	torch.manual_seed(0)

							__UpperCAmelCase			:							Optional[Any]	    =	StableDiffusionDiffEditPipeline.from_pretrained(
							    '''stabilityai/stable-diffusion-2-1'''				,     safety_checker=lowercase__				,     torch_dtype=torch.floataa)
							__UpperCAmelCase			:							Any	    =	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
							__UpperCAmelCase			:							Optional[Any]	    =	DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config)
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=lowercase__)

							__UpperCAmelCase			:							Any	    =	'''a bowl of fruit'''
							__UpperCAmelCase			:							Optional[int]	    =	'''a bowl of pears'''

							__UpperCAmelCase			:							int	    =	pipe.generate_mask(
							    image=self.raw_image				,     source_prompt=lowercase__				,     target_prompt=lowercase__				,     generator=lowercase__				,     )

							__UpperCAmelCase			:							Optional[Any]	    =	pipe.invert(
							    prompt=lowercase__				,     image=self.raw_image				,     inpaint_strength=0.7				,     generator=lowercase__				,     num_inference_steps=2_5				,     ).latents

							__UpperCAmelCase			:							Dict	    =	pipe(
							    prompt=lowercase__				,     mask_image=lowercase__				,     image_latents=lowercase__				,     generator=lowercase__				,     negative_prompt=lowercase__				,     inpaint_strength=0.7				,     num_inference_steps=2_5				,     output_type='''numpy'''				,     ).images[0]

							__UpperCAmelCase			:							Dict	    =	(
							    np.array(
							        load_image(
							            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							            '''/diffedit/pears.png''').resize((7_6_8, 7_6_8)))
							    / 2_5_5
							)
							assert np.abs((expected_image - image).max()) < 5e-1





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


lowerCAmelCase       		=    logging.get_logger(__name__)

lowerCAmelCase       		=    {
    """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json""",
}




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Tuple										=						'''gpt_neox_japanese'''


	def __init__(    self				,     lowercase__=3_2_0_0_0				,     lowercase__=2_5_6_0				,     lowercase__=3_2				,     lowercase__=3_2				,     lowercase__=4				,     lowercase__="gelu"				,     lowercase__=1.0_0				,     lowercase__=1_0_0_0_0				,     lowercase__=2_0_4_8				,     lowercase__=0.0_2				,     lowercase__=1e-5				,     lowercase__=True				,     lowercase__=3_1_9_9_6				,     lowercase__=3_1_9_9_9				,     lowercase__=0.1				,     lowercase__=0.0				,     **lowercase__				,     ):
							super().__init__(bos_token_id=lowercase__				,     eos_token_id=lowercase__				,     **lowercase__)
							__UpperCAmelCase			:							Optional[Any]	    =	vocab_size
							__UpperCAmelCase			:							Dict	    =	max_position_embeddings
							__UpperCAmelCase			:							int	    =	hidden_size
							__UpperCAmelCase			:							List[Any]	    =	num_hidden_layers
							__UpperCAmelCase			:							Union[str, Any]	    =	num_attention_heads
							__UpperCAmelCase			:							Optional[int]	    =	intermediate_multiple_size
							__UpperCAmelCase			:							Any	    =	hidden_act
							__UpperCAmelCase			:							str	    =	rotary_pct
							__UpperCAmelCase			:							Any	    =	rotary_emb_base
							__UpperCAmelCase			:							Dict	    =	initializer_range
							__UpperCAmelCase			:							Tuple	    =	layer_norm_eps
							__UpperCAmelCase			:							Optional[int]	    =	use_cache
							__UpperCAmelCase			:							str	    =	attention_dropout
							__UpperCAmelCase			:							Any	    =	hidden_dropout

