




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



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								torch.manual_seed(0   )
								lowerCAmelCase_      			=  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=_a,	)
								lowerCAmelCase_      			=  DDIMScheduler(
								    beta_start=0.00_085,	beta_end=0.012,	beta_schedule='''scaled_linear''',	clip_sample=_a,	set_alpha_to_one=_a,	)
								lowerCAmelCase_      			=  DDIMInverseScheduler(
								    beta_start=0.00_085,	beta_end=0.012,	beta_schedule='''scaled_linear''',	clip_sample=_a,	set_alpha_to_zero=_a,	)
								torch.manual_seed(0   )
								lowerCAmelCase_      			=  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   )
								lowerCAmelCase_      			=  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,	)
								lowerCAmelCase_      			=  CLIPTextModel(_a   )
								lowerCAmelCase_      			=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

								lowerCAmelCase_      			=  {
								    "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        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__=0   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  floats_tensor((1, 16, 16),	rng=random.Random(_a   )   ).to(_a   )
								lowerCAmelCase_      			=  floats_tensor((1, 2, 4, 16, 16),	rng=random.Random(_a   )   ).to(_a   )
								if str(_a   ).startswith('''mps'''   ):
														lowerCAmelCase_      			=  torch.manual_seed(_a   )
								else:
														lowerCAmelCase_      			=  torch.Generator(device=_a   ).manual_seed(_a   )
								lowerCAmelCase_      			=  {
								    "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        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__=0   ):







								"""simple docstring"""



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







								"""simple docstring"""



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







								"""simple docstring"""



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

								lowerCAmelCase_      			=  self.get_dummy_components()
								lowerCAmelCase_      			=  self.pipeline_class(**_a   )
								pipe.to(_a   )
								pipe.set_progress_bar_config(disable=_a   )

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

								lowerCAmelCase_      			=  self.get_dummy_inputs(_a   )
								lowerCAmelCase_      			=  pipe(**_a   )[0]

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

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

								lowerCAmelCase_      			=  self.get_dummy_inputs(_a   )
								lowerCAmelCase_      			=  pipe_loaded(**_a   )[0]

								lowerCAmelCase_      			=  np.abs(output - output_loaded   ).max()
								self.assertLess(_a,	1E-4   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  "cpu"

								lowerCAmelCase_      			=  self.get_dummy_components()
								lowerCAmelCase_      			=  self.pipeline_class(**_a   )
								pipe.to(_a   )
								pipe.set_progress_bar_config(disable=_a   )

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

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



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  "cpu"

								lowerCAmelCase_      			=  self.get_dummy_components()
								lowerCAmelCase_      			=  self.pipeline_class(**_a   )
								pipe.to(_a   )
								pipe.set_progress_bar_config(disable=_a   )

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

								self.assertEqual(image.shape,	(2, 32, 32, 3)   )
								lowerCAmelCase_      			=  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],	)
								lowerCAmelCase_      			=  np.abs(image_slice.flatten() - expected_slice   ).max()
								self.assertLessEqual(_a,	1E-3   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  "cpu"

								lowerCAmelCase_      			=  self.get_dummy_components()

								lowerCAmelCase_      			=  {"beta_start": 0.00_085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
								lowerCAmelCase_      			=  DPMSolverMultistepScheduler(**_a   )
								lowerCAmelCase_      			=  DPMSolverMultistepInverseScheduler(**_a   )

								lowerCAmelCase_      			=  self.pipeline_class(**_a   )
								pipe.to(_a   )
								pipe.set_progress_bar_config(disable=_a   )

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

								self.assertEqual(image.shape,	(2, 32, 32, 3)   )
								lowerCAmelCase_      			=  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],	)
								lowerCAmelCase_      			=  np.abs(image_slice.flatten() - expected_slice   ).max()
								self.assertLessEqual(_a,	1E-3   )







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



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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



		@classmethod
		def        SCREAMING_SNAKE_CASE__			(     cls   ):







								"""simple docstring"""



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

								lowerCAmelCase_      			=  raw_image.convert('''RGB'''   ).resize((768, 768)   )

								lowerCAmelCase_      			=  raw_image



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  torch.manual_seed(0   )

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

								lowerCAmelCase_      			=  "a bowl of fruit"
								lowerCAmelCase_      			=  "a bowl of pears"

								lowerCAmelCase_      			=  pipe.generate_mask(
								    image=self.raw_image,	source_prompt=_a,	target_prompt=_a,	generator=_a,	)

								lowerCAmelCase_      			=  pipe.invert(
								    prompt=_a,	image=self.raw_image,	inpaint_strength=0.7,	generator=_a   ).latents

								lowerCAmelCase_      			=  pipe(
								    prompt=_a,	mask_image=_a,	image_latents=_a,	generator=_a,	negative_prompt=_a,	inpaint_strength=0.7,	output_type='''numpy''',	).images[0]

								lowerCAmelCase_      			=  (
								    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        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  torch.manual_seed(0   )

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

								lowerCAmelCase_      			=  "a bowl of fruit"
								lowerCAmelCase_      			=  "a bowl of pears"

								lowerCAmelCase_      			=  pipe.generate_mask(
								    image=self.raw_image,	source_prompt=_a,	target_prompt=_a,	generator=_a,	)

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

								lowerCAmelCase_      			=  pipe(
								    prompt=_a,	mask_image=_a,	image_latents=_a,	generator=_a,	negative_prompt=_a,	inpaint_strength=0.7,	num_inference_steps=25,	output_type='''numpy''',	).images[0]

								lowerCAmelCase_      			=  (
								    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






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

snake_case :		List[str]							      =  "facebook/wmt19-en-de"
snake_case :		Dict							      =  FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
snake_case :		List[str]							      =  FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

snake_case :		int							      =  FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
snake_case :		Optional[Any]							      =  tokenizer(["Making tiny model"], return_tensors="pt")
snake_case :		List[str]							      =  tiny_model(**batch)

print("test output:", len(outputs.logits[0]))

# Save
snake_case :		Dict							      =  "tiny-wmt19-en-de"
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-de
