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 _lowercase       (							_UpperCAmelCase ,					_UpperCAmelCase ,					unittest.TestCase    ):





							"""simple docstring"""




							lowerCAmelCase__							=       StableDiffusionDiffEditPipeline
							lowerCAmelCase__							=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
							lowerCAmelCase__							=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
							lowerCAmelCase__							=       frozenset(
							    []    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							lowerCAmelCase__							=       frozenset([]    )



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														torch.manual_seed(0	)
														_lowercase					=						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  , attention_head_dim=(2, 4)  , use_linear_projection=__lowerCAmelCase  , )
														_lowercase					=						DDIMScheduler(
														    beta_start=0.00_085  , beta_end=0.012  , beta_schedule="""scaled_linear"""  , clip_sample=__lowerCAmelCase  , set_alpha_to_one=__lowerCAmelCase  , )
														_lowercase					=						DDIMInverseScheduler(
														    beta_start=0.00_085  , beta_end=0.012  , beta_schedule="""scaled_linear"""  , clip_sample=__lowerCAmelCase  , set_alpha_to_zero=__lowerCAmelCase  , )
														torch.manual_seed(0	)
														_lowercase					=						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	)
														_lowercase					=						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  , )
														_lowercase					=						CLIPTextModel(__lowerCAmelCase	)
														_lowercase					=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""	)

														_lowercase					=						{
														    '''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      _UpperCAmelCase							(     self  , UpperCAmelCase  , UpperCAmelCase=0	):







														'''simple docstring'''






														_lowercase					=						floats_tensor((1, 16, 16)  , rng=random.Random(__lowerCAmelCase	)	).to(__lowerCAmelCase	)
														_lowercase					=						floats_tensor((1, 2, 4, 16, 16)  , rng=random.Random(__lowerCAmelCase	)	).to(__lowerCAmelCase	)
														if str(__lowerCAmelCase	).startswith("""mps"""	):
																					_lowercase					=						torch.manual_seed(__lowerCAmelCase	)
														else:
																					_lowercase					=						torch.Generator(device=__lowerCAmelCase	).manual_seed(__lowerCAmelCase	)
														_lowercase					=						{
														    '''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      _UpperCAmelCase							(     self  , UpperCAmelCase  , UpperCAmelCase=0	):







														'''simple docstring'''






														_lowercase					=						floats_tensor((1, 3, 32, 32)  , rng=random.Random(__lowerCAmelCase	)	).to(__lowerCAmelCase	)
														_lowercase					=						image.cpu().permute(0  , 2  , 3  , 1	)[0]
														_lowercase					=						Image.fromarray(np.uinta(__lowerCAmelCase	)	).convert("""RGB"""	)
														if str(__lowerCAmelCase	).startswith("""mps"""	):
																					_lowercase					=						torch.manual_seed(__lowerCAmelCase	)
														else:
																					_lowercase					=						torch.Generator(device=__lowerCAmelCase	).manual_seed(__lowerCAmelCase	)
														_lowercase					=						{
														    '''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      _UpperCAmelCase							(     self  , UpperCAmelCase  , UpperCAmelCase=0	):







														'''simple docstring'''






														_lowercase					=						floats_tensor((1, 3, 32, 32)  , rng=random.Random(__lowerCAmelCase	)	).to(__lowerCAmelCase	)
														_lowercase					=						image.cpu().permute(0  , 2  , 3  , 1	)[0]
														_lowercase					=						Image.fromarray(np.uinta(__lowerCAmelCase	)	).convert("""RGB"""	)
														if str(__lowerCAmelCase	).startswith("""mps"""	):
																					_lowercase					=						torch.manual_seed(__lowerCAmelCase	)
														else:
																					_lowercase					=						torch.Generator(device=__lowerCAmelCase	).manual_seed(__lowerCAmelCase	)
														_lowercase					=						{
														    '''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      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														if not hasattr(self.pipeline_class  , """_optional_components"""	):
																					return

														_lowercase					=						self.get_dummy_components()
														_lowercase					=						self.pipeline_class(**__lowerCAmelCase	)
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

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

														_lowercase					=						self.get_dummy_inputs(__lowerCAmelCase	)
														_lowercase					=						pipe(**__lowerCAmelCase	)[0]

														with tempfile.TemporaryDirectory() as tmpdir:
																					pipe.save_pretrained(__lowerCAmelCase	)
																					_lowercase					=						self.pipeline_class.from_pretrained(__lowerCAmelCase	)
																					pipe_loaded.to(__lowerCAmelCase	)
																					pipe_loaded.set_progress_bar_config(disable=__lowerCAmelCase	)

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

														_lowercase					=						self.get_dummy_inputs(__lowerCAmelCase	)
														_lowercase					=						pipe_loaded(**__lowerCAmelCase	)[0]

														_lowercase					=						np.abs(output - output_loaded	).max()
														self.assertLess(__lowerCAmelCase  , 1e-4	)



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						'''cpu'''

														_lowercase					=						self.get_dummy_components()
														_lowercase					=						self.pipeline_class(**__lowerCAmelCase	)
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														_lowercase					=						self.get_dummy_mask_inputs(__lowerCAmelCase	)
														_lowercase					=						pipe.generate_mask(**__lowerCAmelCase	)
														_lowercase					=						mask[0, -3:, -3:]

														self.assertEqual(mask.shape  , (1, 16, 16)	)
														_lowercase					=						np.array([0] * 9	)
														_lowercase					=						np.abs(mask_slice.flatten() - expected_slice	).max()
														self.assertLessEqual(__lowerCAmelCase  , 1e-3	)
														self.assertEqual(mask[0, -3, -4]  , 0	)



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						'''cpu'''

														_lowercase					=						self.get_dummy_components()
														_lowercase					=						self.pipeline_class(**__lowerCAmelCase	)
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														_lowercase					=						self.get_dummy_inversion_inputs(__lowerCAmelCase	)
														_lowercase					=						pipe.invert(**__lowerCAmelCase	).images
														_lowercase					=						image[0, -1, -3:, -3:]

														self.assertEqual(image.shape  , (2, 32, 32, 3)	)
														_lowercase					=						np.array(
														    [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]  , )
														_lowercase					=						np.abs(image_slice.flatten() - expected_slice	).max()
														self.assertLessEqual(__lowerCAmelCase  , 1e-3	)



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														super().test_inference_batch_single_identical(expected_max_diff=5e-3	)



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						'''cpu'''

														_lowercase					=						self.get_dummy_components()

														_lowercase					=						{'''beta_start''': 0.00_085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
														_lowercase					=						DPMSolverMultistepScheduler(**__lowerCAmelCase	)
														_lowercase					=						DPMSolverMultistepInverseScheduler(**__lowerCAmelCase	)

														_lowercase					=						self.pipeline_class(**__lowerCAmelCase	)
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														_lowercase					=						self.get_dummy_inversion_inputs(__lowerCAmelCase	)
														_lowercase					=						pipe.invert(**__lowerCAmelCase	).images
														_lowercase					=						image[0, -1, -3:, -3:]

														self.assertEqual(image.shape  , (2, 32, 32, 3)	)
														_lowercase					=						np.array(
														    [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]  , )
														_lowercase					=						np.abs(image_slice.flatten() - expected_slice	).max()
														self.assertLessEqual(__lowerCAmelCase  , 1e-3	)






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





							"""simple docstring"""




							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()



							@classmethod
							def      _UpperCAmelCase							(     cls	):







														'''simple docstring'''






														_lowercase					=						load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"""	)

														_lowercase					=						raw_image.convert("""RGB"""	).resize((768, 768)	)

														_lowercase					=						raw_image



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						torch.manual_seed(0	)

														_lowercase					=						StableDiffusionDiffEditPipeline.from_pretrained(
														    """stabilityai/stable-diffusion-2-1"""  , safety_checker=__lowerCAmelCase  , torch_dtype=torch.floataa	)
														_lowercase					=						DDIMScheduler.from_config(pipe.scheduler.config	)
														_lowercase					=						DDIMInverseScheduler.from_config(pipe.scheduler.config	)
														pipe.enable_model_cpu_offload()
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														_lowercase					=						'''a bowl of fruit'''
														_lowercase					=						'''a bowl of pears'''

														_lowercase					=						pipe.generate_mask(
														    image=self.raw_image  , source_prompt=__lowerCAmelCase  , target_prompt=__lowerCAmelCase  , generator=__lowerCAmelCase  , )

														_lowercase					=						pipe.invert(
														    prompt=__lowerCAmelCase  , image=self.raw_image  , inpaint_strength=0.7  , generator=__lowerCAmelCase	).latents

														_lowercase					=						pipe(
														    prompt=__lowerCAmelCase  , mask_image=__lowerCAmelCase  , image_latents=__lowerCAmelCase  , generator=__lowerCAmelCase  , negative_prompt=__lowerCAmelCase  , inpaint_strength=0.7  , output_type="""numpy"""  , ).images[0]

														_lowercase					=						(
														    np.array(
														        load_image(
														            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														            """/diffedit/pears.png"""	).resize((768, 768)	)	)
														    / 255
														)
														assert np.abs((expected_image - image).max()	) < 5e-1



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						torch.manual_seed(0	)

														_lowercase					=						StableDiffusionDiffEditPipeline.from_pretrained(
														    """stabilityai/stable-diffusion-2-1"""  , safety_checker=__lowerCAmelCase  , torch_dtype=torch.floataa	)
														_lowercase					=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
														_lowercase					=						DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config	)
														pipe.enable_model_cpu_offload()
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														_lowercase					=						'''a bowl of fruit'''
														_lowercase					=						'''a bowl of pears'''

														_lowercase					=						pipe.generate_mask(
														    image=self.raw_image  , source_prompt=__lowerCAmelCase  , target_prompt=__lowerCAmelCase  , generator=__lowerCAmelCase  , )

														_lowercase					=						pipe.invert(
														    prompt=__lowerCAmelCase  , image=self.raw_image  , inpaint_strength=0.7  , generator=__lowerCAmelCase  , num_inference_steps=25  , ).latents

														_lowercase					=						pipe(
														    prompt=__lowerCAmelCase  , mask_image=__lowerCAmelCase  , image_latents=__lowerCAmelCase  , generator=__lowerCAmelCase  , negative_prompt=__lowerCAmelCase  , inpaint_strength=0.7  , num_inference_steps=25  , output_type="""numpy"""  , ).images[0]

														_lowercase					=						(
														    np.array(
														        load_image(
														            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														            """/diffedit/pears.png"""	).resize((768, 768)	)	)
														    / 255
														)
														assert np.abs((expected_image - image).max()	) < 5e-1










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


SCREAMING_SNAKE_CASE__   :      Optional[int]				=		{"""tokenization_herbert""": ["""HerbertTokenizer"""]}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		["""HerbertTokenizerFast"""]


if TYPE_CHECKING:
					from .tokenization_herbert import HerbertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_herbert_fast import HerbertTokenizerFast

else:
					import sys

					SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






