





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 UpperCAmelCase_				( _SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE     ,		unittest.TestCase ):
  __lowerCAmelCase   :						Optional[int]							=   StableDiffusionDiffEditPipeline
  __lowerCAmelCase   :						int							=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
  __lowerCAmelCase   :						int							=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
  __lowerCAmelCase   :						Optional[Any]							=   frozenset(
      [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  __lowerCAmelCase   :						Tuple							=   frozenset([] )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       torch.manual_seed(0 )
       A_          =       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=__snake_case ,)
       A_          =       DDIMScheduler(
           beta_start=0.0_0085 ,beta_end=0.012 ,beta_schedule='''scaled_linear''' ,clip_sample=__snake_case ,set_alpha_to_one=__snake_case ,)
       A_          =       DDIMInverseScheduler(
           beta_start=0.0_0085 ,beta_end=0.012 ,beta_schedule='''scaled_linear''' ,clip_sample=__snake_case ,set_alpha_to_zero=__snake_case ,)
       torch.manual_seed(0 )
       A_          =       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 )
       A_          =       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 ,)
       A_          =       CLIPTextModel(__snake_case )
       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 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case=0 ):



       """simple docstring"""





       A_          =       floats_tensor((1, 1_6, 1_6) ,rng=random.Random(__snake_case ) ).to(__snake_case )
       A_          =       floats_tensor((1, 2, 4, 1_6, 1_6) ,rng=random.Random(__snake_case ) ).to(__snake_case )
       if str(__snake_case ).startswith('''mps''' ):
            A_          =       torch.manual_seed(__snake_case )
       else:
            A_          =       torch.Generator(device=__snake_case ).manual_seed(__snake_case )
       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 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case=0 ):



       """simple docstring"""





       A_          =       floats_tensor((1, 3, 3_2, 3_2) ,rng=random.Random(__snake_case ) ).to(__snake_case )
       A_          =       image.cpu().permute(0 ,2 ,3 ,1 )[0]
       A_          =       Image.fromarray(np.uinta(__snake_case ) ).convert('''RGB''' )
       if str(__snake_case ).startswith('''mps''' ):
            A_          =       torch.manual_seed(__snake_case )
       else:
            A_          =       torch.Generator(device=__snake_case ).manual_seed(__snake_case )
       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 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case=0 ):



       """simple docstring"""





       A_          =       floats_tensor((1, 3, 3_2, 3_2) ,rng=random.Random(__snake_case ) ).to(__snake_case )
       A_          =       image.cpu().permute(0 ,2 ,3 ,1 )[0]
       A_          =       Image.fromarray(np.uinta(__snake_case ) ).convert('''RGB''' )
       if str(__snake_case ).startswith('''mps''' ):
            A_          =       torch.manual_seed(__snake_case )
       else:
            A_          =       torch.Generator(device=__snake_case ).manual_seed(__snake_case )
       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 						__UpperCAmelCase     (							self ):



       """simple docstring"""





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

       A_          =       self.get_dummy_components()
       A_          =       self.pipeline_class(**__snake_case )
       pipe.to(__snake_case )
       pipe.set_progress_bar_config(disable=__snake_case )

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

       A_          =       self.get_dummy_inputs(__snake_case )
       A_          =       pipe(**__snake_case )[0]

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

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

       A_          =       self.get_dummy_inputs(__snake_case )
       A_          =       pipe_loaded(**__snake_case )[0]

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


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       '''cpu'''

       A_          =       self.get_dummy_components()
       A_          =       self.pipeline_class(**__snake_case )
       pipe.to(__snake_case )
       pipe.set_progress_bar_config(disable=__snake_case )

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

       self.assertEqual(mask.shape ,(1, 1_6, 1_6) )
       A_          =       np.array([0] * 9 )
       A_          =       np.abs(mask_slice.flatten() - expected_slice ).max()
       self.assertLessEqual(__snake_case ,1E-3 )
       self.assertEqual(mask[0, -3, -4] ,0 )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       '''cpu'''

       A_          =       self.get_dummy_components()
       A_          =       self.pipeline_class(**__snake_case )
       pipe.to(__snake_case )
       pipe.set_progress_bar_config(disable=__snake_case )

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

       self.assertEqual(image.shape ,(2, 3_2, 3_2, 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(__snake_case ,1E-3 )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





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




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       '''cpu'''

       A_          =       self.get_dummy_components()

       A_          =       {'''beta_start''': 0.0_0085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
       A_          =       DPMSolverMultistepScheduler(**__snake_case )
       A_          =       DPMSolverMultistepInverseScheduler(**__snake_case )

       A_          =       self.pipeline_class(**__snake_case )
       pipe.to(__snake_case )
       pipe.set_progress_bar_config(disable=__snake_case )

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

       self.assertEqual(image.shape ,(2, 3_2, 3_2, 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(__snake_case ,1E-3 )





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


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





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


  @classmethod
  def 						__UpperCAmelCase     (							cls ):



       """simple docstring"""





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

       A_          =       raw_image.convert('''RGB''' ).resize((7_6_8, 7_6_8) )

       A_          =       raw_image


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       torch.manual_seed(0 )

       A_          =       StableDiffusionDiffEditPipeline.from_pretrained(
           '''stabilityai/stable-diffusion-2-1''' ,safety_checker=__snake_case ,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=__snake_case )

       A_          =       '''a bowl of fruit'''
       A_          =       '''a bowl of pears'''

       A_          =       pipe.generate_mask(
           image=self.raw_image ,source_prompt=__snake_case ,target_prompt=__snake_case ,generator=__snake_case ,)

       A_          =       pipe.invert(
           prompt=__snake_case ,image=self.raw_image ,inpaint_strength=0.7 ,generator=__snake_case ).latents

       A_          =       pipe(
           prompt=__snake_case ,mask_image=__snake_case ,image_latents=__snake_case ,generator=__snake_case ,negative_prompt=__snake_case ,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((7_6_8, 7_6_8) ) )
           / 2_5_5
       )
       assert np.abs((expected_image - image).max() ) < 5E-1




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       torch.manual_seed(0 )

       A_          =       StableDiffusionDiffEditPipeline.from_pretrained(
           '''stabilityai/stable-diffusion-2-1''' ,safety_checker=__snake_case ,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=__snake_case )

       A_          =       '''a bowl of fruit'''
       A_          =       '''a bowl of pears'''

       A_          =       pipe.generate_mask(
           image=self.raw_image ,source_prompt=__snake_case ,target_prompt=__snake_case ,generator=__snake_case ,)

       A_          =       pipe.invert(
           prompt=__snake_case ,image=self.raw_image ,inpaint_strength=0.7 ,generator=__snake_case ,num_inference_steps=2_5 ,).latents

       A_          =       pipe(
           prompt=__snake_case ,mask_image=__snake_case ,image_latents=__snake_case ,generator=__snake_case ,negative_prompt=__snake_case ,inpaint_strength=0.7 ,num_inference_steps=2_5 ,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((7_6_8, 7_6_8) ) )
           / 2_5_5
       )
       assert np.abs((expected_image - image).max() ) < 5E-1










import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a__      :     Any         =		logging.get_logger(__name__)

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[Any]				)	->				Optional[int]:
     '''simple docstring'''


     A_          =       OrderedDict()
     for key, value in state_dict.items():
          if key.startswith('''module.encoder'''				):
               A_          =       key.replace('''module.encoder'''  ,						'''glpn.encoder'''				)
          if key.startswith('''module.decoder'''				):
               A_          =       key.replace('''module.decoder'''  ,						'''decoder.stages'''				)
          if "patch_embed" in key:
               # replace for example patch_embed1 by patch_embeddings.0
               A_          =       key[key.find('''patch_embed'''				) + len('''patch_embed'''				)]
               A_          =       key.replace(f'patch_embed{idx}'  ,						f'patch_embeddings.{int(_UpperCAmelCase				)-1}'				)
          if "norm" in key:
               A_          =       key.replace('''norm'''  ,						'''layer_norm'''				)
          if "glpn.encoder.layer_norm" in key:
               # replace for example layer_norm1 by layer_norm.0
               A_          =       key[key.find('''glpn.encoder.layer_norm'''				) + len('''glpn.encoder.layer_norm'''				)]
               A_          =       key.replace(f'layer_norm{idx}'  ,						f'layer_norm.{int(_UpperCAmelCase				)-1}'				)
          if "layer_norm1" in key:
               A_          =       key.replace('''layer_norm1'''  ,						'''layer_norm_1'''				)
          if "layer_norm2" in key:
               A_          =       key.replace('''layer_norm2'''  ,						'''layer_norm_2'''				)
          if "block" in key:
               # replace for example block1 by block.0
               A_          =       key[key.find('''block'''				) + len('''block'''				)]
               A_          =       key.replace(f'block{idx}'  ,						f'block.{int(_UpperCAmelCase				)-1}'				)
          if "attn.q" in key:
               A_          =       key.replace('''attn.q'''  ,						'''attention.self.query'''				)
          if "attn.proj" in key:
               A_          =       key.replace('''attn.proj'''  ,						'''attention.output.dense'''				)
          if "attn" in key:
               A_          =       key.replace('''attn'''  ,						'''attention.self'''				)
          if "fc1" in key:
               A_          =       key.replace('''fc1'''  ,						'''dense1'''				)
          if "fc2" in key:
               A_          =       key.replace('''fc2'''  ,						'''dense2'''				)
          if "linear_pred" in key:
               A_          =       key.replace('''linear_pred'''  ,						'''classifier'''				)
          if "linear_fuse" in key:
               A_          =       key.replace('''linear_fuse.conv'''  ,						'''linear_fuse'''				)
               A_          =       key.replace('''linear_fuse.bn'''  ,						'''batch_norm'''				)
          if "linear_c" in key:
               # replace for example linear_c4 by linear_c.3
               A_          =       key[key.find('''linear_c'''				) + len('''linear_c'''				)]
               A_          =       key.replace(f'linear_c{idx}'  ,						f'linear_c.{int(_UpperCAmelCase				)-1}'				)
          if "bot_conv" in key:
               A_          =       key.replace('''bot_conv'''  ,						'''0.convolution'''				)
          if "skip_conv1" in key:
               A_          =       key.replace('''skip_conv1'''  ,						'''1.convolution'''				)
          if "skip_conv2" in key:
               A_          =       key.replace('''skip_conv2'''  ,						'''2.convolution'''				)
          if "fusion1" in key:
               A_          =       key.replace('''fusion1'''  ,						'''1.fusion'''				)
          if "fusion2" in key:
               A_          =       key.replace('''fusion2'''  ,						'''2.fusion'''				)
          if "fusion3" in key:
               A_          =       key.replace('''fusion3'''  ,						'''3.fusion'''				)
          if "fusion" in key and "conv" in key:
               A_          =       key.replace('''conv'''  ,						'''convolutional_layer'''				)
          if key.startswith('''module.last_layer_depth'''				):
               A_          =       key.replace('''module.last_layer_depth'''  ,						'''head.head'''				)
          A_          =       value

     return new_state_dict

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[Any]  ,						_UpperCAmelCase							:Union[str, Any]				)	->				Tuple:
     '''simple docstring'''


     for i in range(config.num_encoder_blocks				):
          for j in range(config.depths[i]				):
               # read in weights + bias of keys and values (which is a single matrix in the original implementation)
               A_          =       state_dict.pop(f'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'				)
               A_          =       state_dict.pop(f'glpn.encoder.block.{i}.{j}.attention.self.kv.bias'				)
               # next, add keys and values (in that order) to the state dict
               A_          =       kv_weight[
                   : config.hidden_sizes[i], :
               ]
               A_          =       kv_bias[: config.hidden_sizes[i]]
               A_          =       kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               A_          =       kv_bias[config.hidden_sizes[i] :]

def      UpperCAmelCase_    (    )	->				str:
     '''simple docstring'''


     A_          =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     A_          =       Image.open(requests.get(_UpperCAmelCase  ,						stream=_UpperCAmelCase				).raw				)

     return image

@torch.no_grad()
def      UpperCAmelCase_    (    _UpperCAmelCase							:Tuple  ,						_UpperCAmelCase							:List[Any]  ,						_UpperCAmelCase							:Union[str, Any]=False  ,						_UpperCAmelCase							:Any=None				)	->				Optional[int]:
     '''simple docstring'''


     A_          =       GLPNConfig(hidden_sizes=[64, 128, 320, 512]  ,						decoder_hidden_size=64  ,						depths=[3, 8, 27, 3]				)

     # load image processor (only resize + rescale)
     A_          =       GLPNImageProcessor()

     # prepare image
     A_          =       prepare_img()
     A_          =       image_processor(images=_UpperCAmelCase  ,						return_tensors='''pt'''				).pixel_values

     logger.info('''Converting model...'''				)

     # load original state dict
     A_          =       torch.load(_UpperCAmelCase  ,						map_location=torch.device('''cpu'''				)				)

     # rename keys
     A_          =       rename_keys(_UpperCAmelCase				)

     # key and value matrices need special treatment
     read_in_k_v(_UpperCAmelCase  ,						_UpperCAmelCase				)

     # create HuggingFace model and load state dict
     A_          =       GLPNForDepthEstimation(_UpperCAmelCase				)
     model.load_state_dict(_UpperCAmelCase				)
     model.eval()

     # forward pass
     A_          =       model(_UpperCAmelCase				)
     A_          =       outputs.predicted_depth

     # verify output
     if model_name is not None:
          if "nyu" in model_name:
               A_          =       torch.tensor(
                   [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]]				)
          elif "kitti" in model_name:
               A_          =       torch.tensor(
                   [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]				)
          else:
               raise ValueError(f'Unknown model name: {model_name}'				)

          A_          =       torch.Size([1, 480, 640]				)

          assert predicted_depth.shape == expected_shape
          assert torch.allclose(predicted_depth[0, :3, :3]  ,						_UpperCAmelCase  ,						atol=1e-4				)
          print('''Looks ok!'''				)

     # finally, push to hub if required
     if push_to_hub:
          logger.info('''Pushing model and image processor to the hub...'''				)
          model.push_to_hub(
              repo_path_or_name=Path(_UpperCAmelCase  ,						_UpperCAmelCase				)  ,						organization='''nielsr'''  ,						commit_message='''Add model'''  ,						use_temp_dir=_UpperCAmelCase  ,						)
          image_processor.push_to_hub(
              repo_path_or_name=Path(_UpperCAmelCase  ,						_UpperCAmelCase				)  ,						organization='''nielsr'''  ,						commit_message='''Add image processor'''  ,						use_temp_dir=_UpperCAmelCase  ,						)


if __name__ == "__main__":
      a__      :     Dict         =		argparse.ArgumentParser()

      parser.add_argument(
          '--checkpoint_path',
          default=None,
          type=str,
          help='Path to the original PyTorch checkpoint (.pth file).',
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
      )
      parser.add_argument(
          '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
      )
      parser.add_argument(
          '--model_name',
          default='glpn-kitti',
          type=str,
          help='Name of the model in case you\'re pushing to the hub.',
      )
      a__      :     int         =		parser.parse_args()
      convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)



