






'''simple docstring'''



from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


SCREAMING_SNAKE_CASE							=						{
    'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
    'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      '''ernie_m'''
      UpperCamelCase_             =      {"dropout": "classifier_dropout", "num_classes": "num_labels"}






      def __init__(			self      :		Any  ,   UpperCAmelCase      :		int = 25_0002  ,   UpperCAmelCase      :		int = 768  ,   UpperCAmelCase      :		int = 12  ,   UpperCAmelCase      :		int = 12  ,   UpperCAmelCase      :		int = 3072  ,   UpperCAmelCase      :		str = "gelu"  ,   UpperCAmelCase      :		float = 0.1  ,   UpperCAmelCase      :		float = 0.1  ,   UpperCAmelCase      :		int = 514  ,   UpperCAmelCase      :		float = 0.0_2  ,   UpperCAmelCase      :		int = 1  ,   UpperCAmelCase      :		float = 1e-05  ,   UpperCAmelCase      :		List[Any]=None  ,   UpperCAmelCase      :		List[str]=False  ,   UpperCAmelCase      :		Optional[int]=0.0  ,   **UpperCAmelCase      :		List[str]  ,   )	->	Optional[int]:


       '''simple docstring'''

       super().__init__(pad_token_id=UpperCAmelCase  ,   **UpperCAmelCase		)
       lowercase       : Optional[Any]						=vocab_size
       lowercase       : int						=hidden_size
       lowercase       : List[str]						=num_hidden_layers
       lowercase       : Union[str, Any]						=num_attention_heads
       lowercase       : Any						=intermediate_size
       lowercase       : Optional[int]						=hidden_act
       lowercase       : Optional[int]						=hidden_dropout_prob
       lowercase       : int						=attention_probs_dropout_prob
       lowercase       : Union[str, Any]						=max_position_embeddings
       lowercase       : List[Any]						=initializer_range
       lowercase       : str						=layer_norm_eps
       lowercase       : Dict						=classifier_dropout
       lowercase       : List[str]						=is_decoder
       lowercase       : str						=act_dropout





'''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    lowerCAmelCase       (     UpperCamelCase_    ,  UpperCamelCase_    ,  unittest.TestCase  ):
 A_				:				int	=			StableDiffusionDiffEditPipeline
 A_				:				List[Any]	=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
 A_				:				str	=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
 A_				:				Dict	=			frozenset(
     []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 A_				:				Union[str, Any]	=			frozenset([]  )


 def    _A	(    self  : Tuple    ):
  '''simple docstring'''

  torch.manual_seed(0    )
  lowerCAmelCase__     :   str     		=	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__     :   str     		=	DDIMScheduler(
      beta_start=0.00085			,							beta_end=0.012			,							beta_schedule="scaled_linear"			,							clip_sample=a__			,							set_alpha_to_one=a__			,							)
  lowerCAmelCase__     :   str     		=	DDIMInverseScheduler(
      beta_start=0.00085			,							beta_end=0.012			,							beta_schedule="scaled_linear"			,							clip_sample=a__			,							set_alpha_to_zero=a__			,							)
  torch.manual_seed(0    )
  lowerCAmelCase__     :   int     		=	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__     :   Dict     		=	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__     :   Tuple     		=	CLIPTextModel(a__    )
  lowerCAmelCase__     :   int     		=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )

  lowerCAmelCase__     :   List[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  : Dict			,							a__  : List[Any]			,							a__  : str=0    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	floats_tensor((1, 16, 16)			,							rng=random.Random(a__    )    ).to(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	floats_tensor((1, 2, 4, 16, 16)			,							rng=random.Random(a__    )    ).to(a__    )
  if str(a__    ).startswith("mps"    ):
   lowerCAmelCase__     :   Any     		=	torch.manual_seed(a__    )
  else:
   lowerCAmelCase__     :   int     		=	torch.Generator(device=a__    ).manual_seed(a__    )
  lowerCAmelCase__     :   Optional[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  : Union[str, Any]			,							a__  : Union[str, Any]			,							a__  : Tuple=0    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	floats_tensor((1, 3, 32, 32)			,							rng=random.Random(a__    )    ).to(a__    )
  lowerCAmelCase__     :   Optional[Any]     		=	image.cpu().permute(0			,							2			,							3			,							1    )[0]
  lowerCAmelCase__     :   Tuple     		=	Image.fromarray(np.uinta(a__    )    ).convert("RGB"    )
  if str(a__    ).startswith("mps"    ):
   lowerCAmelCase__     :   str     		=	torch.manual_seed(a__    )
  else:
   lowerCAmelCase__     :   List[str]     		=	torch.Generator(device=a__    ).manual_seed(a__    )
  lowerCAmelCase__     :   Dict     		=	{
      "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  : Tuple			,							a__  : Any			,							a__  : Optional[Any]=0    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[Any]     		=	floats_tensor((1, 3, 32, 32)			,							rng=random.Random(a__    )    ).to(a__    )
  lowerCAmelCase__     :   List[str]     		=	image.cpu().permute(0			,							2			,							3			,							1    )[0]
  lowerCAmelCase__     :   Tuple     		=	Image.fromarray(np.uinta(a__    )    ).convert("RGB"    )
  if str(a__    ).startswith("mps"    ):
   lowerCAmelCase__     :   Dict     		=	torch.manual_seed(a__    )
  else:
   lowerCAmelCase__     :   Tuple     		=	torch.Generator(device=a__    ).manual_seed(a__    )
  lowerCAmelCase__     :   Dict     		=	{
      "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  : List[str]    ):
  '''simple docstring'''

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

  lowerCAmelCase__     :   int     		=	self.get_dummy_components()
  lowerCAmelCase__     :   Optional[Any]     		=	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__     :   str     		=	self.get_dummy_inputs(a__    )
  lowerCAmelCase__     :   List[Any]     		=	pipe(**a__    )[0]

  with tempfile.TemporaryDirectory() as tmpdir:
   pipe.save_pretrained(a__    )
   lowerCAmelCase__     :   Tuple     		=	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__     :   Union[str, Any]     		=	self.get_dummy_inputs(a__    )
  lowerCAmelCase__     :   int     		=	pipe_loaded(**a__    )[0]

  lowerCAmelCase__     :   Dict     		=	np.abs(output - output_loaded    ).max()
  self.assertLess(a__			,							1e-4    )


 def    _A	(    self  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	"cpu"

  lowerCAmelCase__     :   Any     		=	self.get_dummy_components()
  lowerCAmelCase__     :   List[Any]     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Union[str, Any]     		=	self.get_dummy_mask_inputs(a__    )
  lowerCAmelCase__     :   List[str]     		=	pipe.generate_mask(**a__    )
  lowerCAmelCase__     :   Optional[Any]     		=	mask[0, -3:, -3:]

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


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   int     		=	"cpu"

  lowerCAmelCase__     :   Union[str, Any]     		=	self.get_dummy_components()
  lowerCAmelCase__     :   List[Any]     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   List[str]     		=	self.get_dummy_inversion_inputs(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	pipe.invert(**a__    ).images
  lowerCAmelCase__     :   Dict     		=	image[0, -1, -3:, -3:]

  self.assertEqual(image.shape			,							(2, 32, 32, 3)    )
  lowerCAmelCase__     :   Tuple     		=	np.array(
      [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]			,							)
  lowerCAmelCase__     :   Union[str, Any]     		=	np.abs(image_slice.flatten() - expected_slice    ).max()
  self.assertLessEqual(a__			,							1e-3    )


 def    _A	(    self  : str    ):
  '''simple docstring'''

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


 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[int]     		=	"cpu"

  lowerCAmelCase__     :   Optional[int]     		=	self.get_dummy_components()

  lowerCAmelCase__     :   List[Any]     		=	{"beta_start": 0.00085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
  lowerCAmelCase__     :   List[Any]     		=	DPMSolverMultistepScheduler(**a__    )
  lowerCAmelCase__     :   List[Any]     		=	DPMSolverMultistepInverseScheduler(**a__    )

  lowerCAmelCase__     :   Any     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Dict     		=	self.get_dummy_inversion_inputs(a__    )
  lowerCAmelCase__     :   Optional[Any]     		=	pipe.invert(**a__    ).images
  lowerCAmelCase__     :   Any     		=	image[0, -1, -3:, -3:]

  self.assertEqual(image.shape			,							(2, 32, 32, 3)    )
  lowerCAmelCase__     :   Dict     		=	np.array(
      [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]			,							)
  lowerCAmelCase__     :   int     		=	np.abs(image_slice.flatten() - expected_slice    ).max()
  self.assertLessEqual(a__			,							1e-3    )




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


 def    _A	(    self  : int    ):
  '''simple docstring'''

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


 @classmethod
 def    _A	(    cls  : Dict    ):
  '''simple docstring'''

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

  lowerCAmelCase__     :   Dict     		=	raw_image.convert("RGB"    ).resize((768, 768)    )

  lowerCAmelCase__     :   str     		=	raw_image


 def    _A	(    self  : Dict    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	torch.manual_seed(0    )

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

  lowerCAmelCase__     :   Optional[Any]     		=	"a bowl of fruit"
  lowerCAmelCase__     :   Union[str, Any]     		=	"a bowl of pears"

  lowerCAmelCase__     :   Optional[Any]     		=	pipe.generate_mask(
      image=self.raw_image			,							source_prompt=a__			,							target_prompt=a__			,							generator=a__			,							)

  lowerCAmelCase__     :   str     		=	pipe.invert(
      prompt=a__			,							image=self.raw_image			,							inpaint_strength=0.7			,							generator=a__    ).latents

  lowerCAmelCase__     :   Tuple     		=	pipe(
      prompt=a__			,							mask_image=a__			,							image_latents=a__			,							generator=a__			,							negative_prompt=a__			,							inpaint_strength=0.7			,							output_type="numpy"			,							).images[0]

  lowerCAmelCase__     :   List[str]     		=	(
      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    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	torch.manual_seed(0    )

  lowerCAmelCase__     :   str     		=	StableDiffusionDiffEditPipeline.from_pretrained(
      "stabilityai/stable-diffusion-2-1"			,							safety_checker=a__			,							torch_dtype=torch.floataa    )
  lowerCAmelCase__     :   int     		=	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
  lowerCAmelCase__     :   Any     		=	DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config    )
  pipe.enable_model_cpu_offload()
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Any     		=	"a bowl of fruit"
  lowerCAmelCase__     :   Optional[int]     		=	"a bowl of pears"

  lowerCAmelCase__     :   Optional[Any]     		=	pipe.generate_mask(
      image=self.raw_image			,							source_prompt=a__			,							target_prompt=a__			,							generator=a__			,							)

  lowerCAmelCase__     :   Optional[int]     		=	pipe.invert(
      prompt=a__			,							image=self.raw_image			,							inpaint_strength=0.7			,							generator=a__			,							num_inference_steps=25			,							).latents

  lowerCAmelCase__     :   Any     		=	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__     :   Optional[int]     		=	(
      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





