




"""simple docstring"""

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

 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       torch.manual_seed(0						)
       _A     =  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__       ,				)
       _A     =  DDIMScheduler(
           beta_start=0.0_0085       ,				beta_end=0.012       ,				beta_schedule='scaled_linear'       ,				clip_sample=lowerCamelCase__       ,				set_alpha_to_one=lowerCamelCase__       ,				)
       _A     =  DDIMInverseScheduler(
           beta_start=0.0_0085       ,				beta_end=0.012       ,				beta_schedule='scaled_linear'       ,				clip_sample=lowerCamelCase__       ,				set_alpha_to_zero=lowerCamelCase__       ,				)
       torch.manual_seed(0						)
       _A     =  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						)
       _A     =  CLIPTextConfig(
           bos_token_id=0       ,				eos_token_id=2       ,				hidden_size=32       ,				intermediate_size=37       ,				layer_norm_eps=1E-0_5       ,				num_attention_heads=4       ,				num_hidden_layers=5       ,				pad_token_id=1       ,				vocab_size=1_000       ,				hidden_act='gelu'       ,				projection_dim=512       ,				)
       _A     =  CLIPTextModel(lowerCamelCase__						)
       _A     =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

       _A     =  {
           """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      lowerCAmelCase_				(					self			:			List[str]       ,				_UpperCAmelCase			:			List[Any]       ,				_UpperCAmelCase			:			str=0						):
       _A     =  floats_tensor((1, 16, 16)       ,				rng=random.Random(lowerCamelCase__						)						).to(lowerCamelCase__						)
       _A     =  floats_tensor((1, 2, 4, 16, 16)       ,				rng=random.Random(lowerCamelCase__						)						).to(lowerCamelCase__						)
       if str(lowerCamelCase__						).startswith('mps'						):
             _A     =  torch.manual_seed(lowerCamelCase__						)
       else:
             _A     =  torch.Generator(device=lowerCamelCase__						).manual_seed(lowerCamelCase__						)
       _A     =  {
           """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      lowerCAmelCase_				(					self			:			Dict       ,				_UpperCAmelCase			:			str       ,				_UpperCAmelCase			:			Tuple=0						):
       _A     =  floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(lowerCamelCase__						)						).to(lowerCamelCase__						)
       _A     =  image.cpu().permute(0       ,				2       ,				3       ,				1						)[0]
       _A     =  Image.fromarray(np.uinta(lowerCamelCase__						)						).convert('RGB'						)
       if str(lowerCamelCase__						).startswith('mps'						):
             _A     =  torch.manual_seed(lowerCamelCase__						)
       else:
             _A     =  torch.Generator(device=lowerCamelCase__						).manual_seed(lowerCamelCase__						)
       _A     =  {
           """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      lowerCAmelCase_				(					self			:			Optional[int]       ,				_UpperCAmelCase			:			List[Any]       ,				_UpperCAmelCase			:			List[Any]=0						):
       _A     =  floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(lowerCamelCase__						)						).to(lowerCamelCase__						)
       _A     =  image.cpu().permute(0       ,				2       ,				3       ,				1						)[0]
       _A     =  Image.fromarray(np.uinta(lowerCamelCase__						)						).convert('RGB'						)
       if str(lowerCamelCase__						).startswith('mps'						):
             _A     =  torch.manual_seed(lowerCamelCase__						)
       else:
             _A     =  torch.Generator(device=lowerCamelCase__						).manual_seed(lowerCamelCase__						)
       _A     =  {
           """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      lowerCAmelCase_				(					self			:			List[Any]						):
       if not hasattr(self.pipeline_class       ,				'_optional_components'						):
             return

       _A     =  self.get_dummy_components()
       _A     =  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}						)

       _A     =  self.get_dummy_inputs(lowerCamelCase__						)
       _A     =  pipe(**lowerCamelCase__						)[0]

       with tempfile.TemporaryDirectory() as tmpdir:
             pipe.save_pretrained(lowerCamelCase__						)
             _A     =  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.'''       ,				)

       _A     =  self.get_dummy_inputs(lowerCamelCase__						)
       _A     =  pipe_loaded(**lowerCamelCase__						)[0]

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

 def      lowerCAmelCase_				(					self			:			str						):
       _A     =  """cpu"""

       _A     =  self.get_dummy_components()
       _A     =  self.pipeline_class(**lowerCamelCase__						)
       pipe.to(lowerCamelCase__						)
       pipe.set_progress_bar_config(disable=lowerCamelCase__						)

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

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

 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       _A     =  """cpu"""

       _A     =  self.get_dummy_components()
       _A     =  self.pipeline_class(**lowerCamelCase__						)
       pipe.to(lowerCamelCase__						)
       pipe.set_progress_bar_config(disable=lowerCamelCase__						)

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

       self.assertEqual(image.shape       ,				(2, 32, 32, 3)						)
       _A     =  np.array(
           [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]       ,				)
       _A     =  np.abs(image_slice.flatten() - expected_slice						).max()
       self.assertLessEqual(lowerCamelCase__       ,				1E-3						)

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



 def      lowerCAmelCase_				(					self			:			Any						):
       _A     =  """cpu"""

       _A     =  self.get_dummy_components()

       _A     =  {"""beta_start""": 0.0_0085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
       _A     =  DPMSolverMultistepScheduler(**lowerCamelCase__						)
       _A     =  DPMSolverMultistepInverseScheduler(**lowerCamelCase__						)

       _A     =  self.pipeline_class(**lowerCamelCase__						)
       pipe.to(lowerCamelCase__						)
       pipe.set_progress_bar_config(disable=lowerCamelCase__						)

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

       self.assertEqual(image.shape       ,				(2, 32, 32, 3)						)
       _A     =  np.array(
           [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]       ,				)
       _A     =  np.abs(image_slice.flatten() - expected_slice						).max()
       self.assertLessEqual(lowerCamelCase__       ,				1E-3						)





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



 '''simple docstring'''

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

 @classmethod
 def      lowerCAmelCase_				(					cls			:			List[Any]						):
       _A     =  load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'						)

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

       _A     =  raw_image

 def      lowerCAmelCase_				(					self			:			Dict						):
       _A     =  torch.manual_seed(0						)

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

       _A     =  """a bowl of fruit"""
       _A     =  """a bowl of pears"""

       _A     =  pipe.generate_mask(
           image=self.raw_image       ,				source_prompt=lowerCamelCase__       ,				target_prompt=lowerCamelCase__       ,				generator=lowerCamelCase__       ,				)

       _A     =  pipe.invert(
           prompt=lowerCamelCase__       ,				image=self.raw_image       ,				inpaint_strength=0.7       ,				generator=lowerCamelCase__						).latents

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

       _A     =  (
           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      lowerCAmelCase_				(					self			:			Optional[int]						):
       _A     =  torch.manual_seed(0						)

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

       _A     =  """a bowl of fruit"""
       _A     =  """a bowl of pears"""

       _A     =  pipe.generate_mask(
           image=self.raw_image       ,				source_prompt=lowerCamelCase__       ,				target_prompt=lowerCamelCase__       ,				generator=lowerCamelCase__       ,				)

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

       _A     =  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]

       _A     =  (
           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





"""simple docstring"""


from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							float = 1 / sqrt(2  )  ):
 lowercase__       :       str				=						tau * frequency / samplerate
 lowercase__       :       Union[str, Any]				=						sin(lowerCamelCase__  )
 lowercase__       :       int				=						cos(lowerCamelCase__  )
 lowercase__       :       Union[str, Any]				=						_sin / (2 * q_factor)

 lowercase__       :       str				=						(1 - _cos) / 2
 lowercase__       :       Optional[Any]				=						1 - _cos

 lowercase__       :       List[Any]				=						1 + alpha
 lowercase__       :       Dict				=						-2 * _cos
 lowercase__       :       List[str]				=						1 - alpha

 lowercase__       :       int				=						IIRFilter(2  )
 filt.set_coefficients([aa, aa, aa]       ,		[ba, ba, ba]  )
 return filt


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							float = 1 / sqrt(2  )  ):
 lowercase__       :       Optional[int]				=						tau * frequency / samplerate
 lowercase__       :       Any				=						sin(lowerCamelCase__  )
 lowercase__       :       Any				=						cos(lowerCamelCase__  )
 lowercase__       :       Tuple				=						_sin / (2 * q_factor)

 lowercase__       :       Tuple				=						(1 + _cos) / 2
 lowercase__       :       str				=						-1 - _cos

 lowercase__       :       Dict				=						1 + alpha
 lowercase__       :       Dict				=						-2 * _cos
 lowercase__       :       int				=						1 - alpha

 lowercase__       :       str				=						IIRFilter(2  )
 filt.set_coefficients([aa, aa, aa]       ,		[ba, ba, ba]  )
 return filt


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							float = 1 / sqrt(2  )  ):
 lowercase__       :       Optional[int]				=						tau * frequency / samplerate
 lowercase__       :       str				=						sin(lowerCamelCase__  )
 lowercase__       :       int				=						cos(lowerCamelCase__  )
 lowercase__       :       Dict				=						_sin / (2 * q_factor)

 lowercase__       :       Dict				=						_sin / 2
 lowercase__       :       Union[str, Any]				=						0
 lowercase__       :       int				=						-ba

 lowercase__       :       List[Any]				=						1 + alpha
 lowercase__       :       Tuple				=						-2 * _cos
 lowercase__       :       List[str]				=						1 - alpha

 lowercase__       :       Dict				=						IIRFilter(2  )
 filt.set_coefficients([aa, aa, aa]       ,		[ba, ba, ba]  )
 return filt


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							float = 1 / sqrt(2  )  ):
 lowercase__       :       str				=						tau * frequency / samplerate
 lowercase__       :       Dict				=						sin(lowerCamelCase__  )
 lowercase__       :       Union[str, Any]				=						cos(lowerCamelCase__  )
 lowercase__       :       Tuple				=						_sin / (2 * q_factor)

 lowercase__       :       List[Any]				=						1 - alpha
 lowercase__       :       Any				=						-2 * _cos
 lowercase__       :       Optional[Any]				=						1 + alpha

 lowercase__       :       List[Any]				=						IIRFilter(2  )
 filt.set_coefficients([ba, ba, ba]       ,		[ba, ba, ba]  )
 return filt


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							float       ,		lowerCamelCase__	:							float = 1 / sqrt(2  )       ,		):
 lowercase__       :       Tuple				=						tau * frequency / samplerate
 lowercase__       :       str				=						sin(lowerCamelCase__  )
 lowercase__       :       List[str]				=						cos(lowerCamelCase__  )
 lowercase__       :       Optional[int]				=						_sin / (2 * q_factor)
 lowercase__       :       Optional[Any]				=						10 ** (gain_db / 40)

 lowercase__       :       Optional[Any]				=						1 + alpha * big_a
 lowercase__       :       Dict				=						-2 * _cos
 lowercase__       :       Tuple				=						1 - alpha * big_a
 lowercase__       :       List[str]				=						1 + alpha / big_a
 lowercase__       :       List[Any]				=						-2 * _cos
 lowercase__       :       Optional[int]				=						1 - alpha / big_a

 lowercase__       :       Union[str, Any]				=						IIRFilter(2  )
 filt.set_coefficients([aa, aa, aa]       ,		[ba, ba, ba]  )
 return filt


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							float       ,		lowerCamelCase__	:							float = 1 / sqrt(2  )       ,		):
 lowercase__       :       Optional[int]				=						tau * frequency / samplerate
 lowercase__       :       Dict				=						sin(lowerCamelCase__  )
 lowercase__       :       Union[str, Any]				=						cos(lowerCamelCase__  )
 lowercase__       :       int				=						_sin / (2 * q_factor)
 lowercase__       :       Union[str, Any]				=						10 ** (gain_db / 40)
 lowercase__       :       Dict				=						(big_a + 1) - (big_a - 1) * _cos
 lowercase__       :       Union[str, Any]				=						(big_a + 1) + (big_a - 1) * _cos
 lowercase__       :       List[Any]				=						(big_a - 1) - (big_a + 1) * _cos
 lowercase__       :       Dict				=						(big_a - 1) + (big_a + 1) * _cos
 lowercase__       :       Optional[int]				=						2 * sqrt(lowerCamelCase__  ) * alpha

 lowercase__       :       str				=						big_a * (pmc + aaa)
 lowercase__       :       Optional[Any]				=						2 * big_a * mpc
 lowercase__       :       str				=						big_a * (pmc - aaa)
 lowercase__       :       Dict				=						ppmc + aaa
 lowercase__       :       List[Any]				=						-2 * pmpc
 lowercase__       :       List[Any]				=						ppmc - aaa

 lowercase__       :       Optional[int]				=						IIRFilter(2  )
 filt.set_coefficients([aa, aa, aa]       ,		[ba, ba, ba]  )
 return filt






def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							float       ,		lowerCamelCase__	:							float = 1 / sqrt(2  )       ,		):
 lowercase__       :       int				=						tau * frequency / samplerate
 lowercase__       :       List[Any]				=						sin(lowerCamelCase__  )
 lowercase__       :       List[Any]				=						cos(lowerCamelCase__  )
 lowercase__       :       List[str]				=						_sin / (2 * q_factor)
 lowercase__       :       int				=						10 ** (gain_db / 40)
 lowercase__       :       str				=						(big_a + 1) - (big_a - 1) * _cos
 lowercase__       :       Optional[Any]				=						(big_a + 1) + (big_a - 1) * _cos
 lowercase__       :       Optional[int]				=						(big_a - 1) - (big_a + 1) * _cos
 lowercase__       :       int				=						(big_a - 1) + (big_a + 1) * _cos
 lowercase__       :       Optional[Any]				=						2 * sqrt(lowerCamelCase__  ) * alpha

 lowercase__       :       str				=						big_a * (ppmc + aaa)
 lowercase__       :       Union[str, Any]				=						-2 * big_a * pmpc
 lowercase__       :       List[Any]				=						big_a * (ppmc - aaa)
 lowercase__       :       str				=						pmc + aaa
 lowercase__       :       int				=						2 * mpc
 lowercase__       :       Any				=						pmc - aaa

 lowercase__       :       int				=						IIRFilter(2  )
 filt.set_coefficients([aa, aa, aa]       ,		[ba, ba, ba]  )
 return filt