





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





   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      torch.manual_seed(0   )
      a__						:    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=__UpperCamelCase       ,      )
      a__						:    Optional[int]   		=	DDIMScheduler(
          beta_start=0.00_085       ,      beta_end=0.012       ,      beta_schedule="scaled_linear"       ,      clip_sample=__UpperCamelCase       ,      set_alpha_to_one=__UpperCamelCase       ,      )
      a__						:    List[Any]   		=	DDIMInverseScheduler(
          beta_start=0.00_085       ,      beta_end=0.012       ,      beta_schedule="scaled_linear"       ,      clip_sample=__UpperCamelCase       ,      set_alpha_to_zero=__UpperCamelCase       ,      )
      torch.manual_seed(0   )
      a__						:    Tuple   		=	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__						:    Optional[Any]   		=	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=1_000       ,      hidden_act="gelu"       ,      projection_dim=512       ,      )
      a__						:    Tuple   		=	CLIPTextModel(__UpperCamelCase   )
      a__						:    Union[str, Any]   		=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )

      a__						:    List[str]   		=	{
          "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 						_snake_case			(			self       ,      snake_case       ,      snake_case=0   )					->	Dict:
      """simple docstring"""





      a__						:    List[str]   		=	floats_tensor((1, 16, 16)       ,      rng=random.Random(__UpperCamelCase   )   ).to(__UpperCamelCase   )
      a__						:    Any   		=	floats_tensor((1, 2, 4, 16, 16)       ,      rng=random.Random(__UpperCamelCase   )   ).to(__UpperCamelCase   )
      if str(__UpperCamelCase   ).startswith("mps"   ):
         a__						:    Tuple   		=	torch.manual_seed(__UpperCamelCase   )
      else:
         a__						:    Any   		=	torch.Generator(device=__UpperCamelCase   ).manual_seed(__UpperCamelCase   )
      a__						:    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 						_snake_case			(			self       ,      snake_case       ,      snake_case=0   )					->	Union[str, Any]:
      """simple docstring"""





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





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





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

      a__						:    int   		=	self.get_dummy_components()
      a__						:    Optional[int]   		=	self.pipeline_class(**__UpperCamelCase   )
      pipe.to(__UpperCamelCase   )
      pipe.set_progress_bar_config(disable=__UpperCamelCase   )

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

      a__						:    str   		=	self.get_dummy_inputs(__UpperCamelCase   )
      a__						:    Optional[int]   		=	pipe(**__UpperCamelCase   )[0]

      with tempfile.TemporaryDirectory() as tmpdir:
         pipe.save_pretrained(__UpperCamelCase   )
         a__						:    List[str]   		=	self.pipeline_class.from_pretrained(__UpperCamelCase   )
         pipe_loaded.to(__UpperCamelCase   )
         pipe_loaded.set_progress_bar_config(disable=__UpperCamelCase   )

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

      a__						:    Optional[int]   		=	self.get_dummy_inputs(__UpperCamelCase   )
      a__						:    List[Any]   		=	pipe_loaded(**__UpperCamelCase   )[0]

      a__						:    int   		=	np.abs(output - output_loaded   ).max()
      self.assertLess(__UpperCamelCase       ,      1E-4   )





   def 						_snake_case			(			self   )					->	Any:
      """simple docstring"""





      a__						:    Dict   		=	"cpu"

      a__						:    Tuple   		=	self.get_dummy_components()
      a__						:    int   		=	self.pipeline_class(**__UpperCamelCase   )
      pipe.to(__UpperCamelCase   )
      pipe.set_progress_bar_config(disable=__UpperCamelCase   )

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

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





   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      a__						:    Dict   		=	"cpu"

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

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

      self.assertEqual(image.shape       ,      (2, 32, 32, 3)   )
      a__						:    List[str]   		=	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]       ,      )
      a__						:    Any   		=	np.abs(image_slice.flatten() - expected_slice   ).max()
      self.assertLessEqual(__UpperCamelCase       ,      1E-3   )





   def 						_snake_case			(			self   )					->	Optional[int]:
      """simple docstring"""





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





   def 						_snake_case			(			self   )					->	Any:
      """simple docstring"""





      a__						:    Optional[int]   		=	"cpu"

      a__						:    List[str]   		=	self.get_dummy_components()

      a__						:    List[str]   		=	{"beta_start": 0.00_085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
      a__						:    Any   		=	DPMSolverMultistepScheduler(**__UpperCamelCase   )
      a__						:    Any   		=	DPMSolverMultistepInverseScheduler(**__UpperCamelCase   )

      a__						:    Tuple   		=	self.pipeline_class(**__UpperCamelCase   )
      pipe.to(__UpperCamelCase   )
      pipe.set_progress_bar_config(disable=__UpperCamelCase   )

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

      self.assertEqual(image.shape       ,      (2, 32, 32, 3)   )
      a__						:    int   		=	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]       ,      )
      a__						:    Tuple   		=	np.abs(image_slice.flatten() - expected_slice   ).max()
      self.assertLessEqual(__UpperCamelCase       ,      1E-3   )







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





   def 						_snake_case			(			self   )					->	Any:
      """simple docstring"""





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





   @classmethod
   def 						_snake_case			(			cls   )					->	Tuple:
      """simple docstring"""





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

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

      a__						:    Union[str, Any]   		=	raw_image





   def 						_snake_case			(			self   )					->	Optional[Any]:
      """simple docstring"""





      a__						:    Any   		=	torch.manual_seed(0   )

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

      a__						:    str   		=	"a bowl of fruit"
      a__						:    Optional[int]   		=	"a bowl of pears"

      a__						:    Dict   		=	pipe.generate_mask(
          image=self.raw_image       ,      source_prompt=__UpperCamelCase       ,      target_prompt=__UpperCamelCase       ,      generator=__UpperCamelCase       ,      )

      a__						:    List[str]   		=	pipe.invert(
          prompt=__UpperCamelCase       ,      image=self.raw_image       ,      inpaint_strength=0.7       ,      generator=__UpperCamelCase   ).latents

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

      a__						:    Optional[Any]   		=	(
          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 						_snake_case			(			self   )					->	int:
      """simple docstring"""





      a__						:    str   		=	torch.manual_seed(0   )

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

      a__						:    Union[str, Any]   		=	"a bowl of fruit"
      a__						:    List[str]   		=	"a bowl of pears"

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

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

      a__						:    Optional[int]   		=	pipe(
          prompt=__UpperCamelCase       ,      mask_image=__UpperCamelCase       ,      image_latents=__UpperCamelCase       ,      generator=__UpperCamelCase       ,      negative_prompt=__UpperCamelCase       ,      inpaint_strength=0.7       ,      num_inference_steps=25       ,      output_type="numpy"       ,      ).images[0]

      a__						:    List[Any]   		=	(
          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




import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation





def a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			384
	snake_case_   		=			7
	if "tiny" in model_name:
		snake_case_   		=			96
		snake_case_   		=			(2, 2, 6, 2)
		snake_case_   		=			(3, 6, 12, 24)
	elif "small" in model_name:
		snake_case_   		=			96
		snake_case_   		=			(2, 2, 18, 2)
		snake_case_   		=			(3, 6, 12, 24)
	elif "base" in model_name:
		snake_case_   		=			128
		snake_case_   		=			(2, 2, 18, 2)
		snake_case_   		=			(4, 8, 16, 32)
		snake_case_   		=			12
		snake_case_   		=			512
	elif "large" in model_name:
		snake_case_   		=			192
		snake_case_   		=			(2, 2, 18, 2)
		snake_case_   		=			(6, 12, 24, 48)
		snake_case_   		=			12
		snake_case_   		=			768

	# set label information
	snake_case_   		=			150
	snake_case_   		=			'huggingface/label-files'
	snake_case_   		=			'ade20k-id2label.json'
	snake_case_   		=			json.load(open(hf_hub_download(lowercase__      ,			lowercase__      ,			repo_type='dataset'						)      ,			'r'						)						)
	snake_case_   		=			{int(lowercase__						): v for k, v in idalabel.items()}
	snake_case_   		=			{v: k for k, v in idalabel.items()}

	snake_case_   		=			SwinConfig(
	    embed_dim=lowercase__      ,			depths=lowercase__      ,			num_heads=lowercase__      ,			window_size=lowercase__      ,			out_features=['stage1', 'stage2', 'stage3', 'stage4']      ,			)
	snake_case_   		=			UperNetConfig(
	    backbone_config=lowercase__      ,			auxiliary_in_channels=lowercase__      ,			num_labels=lowercase__      ,			idalabel=lowercase__      ,			labelaid=lowercase__      ,			)

	return config





def a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			[]

	# fmt: off
	# stem
	rename_keys.append(('backbone.patch_embed.projection.weight', 'backbone.embeddings.patch_embeddings.projection.weight')						)
	rename_keys.append(('backbone.patch_embed.projection.bias', 'backbone.embeddings.patch_embeddings.projection.bias')						)
	rename_keys.append(('backbone.patch_embed.norm.weight', 'backbone.embeddings.norm.weight')						)
	rename_keys.append(('backbone.patch_embed.norm.bias', 'backbone.embeddings.norm.bias')						)
	# stages
	for i in range(len(config.backbone_config.depths						)						):
		for j in range(config.backbone_config.depths[i]						):
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")						)

		if i < 3:
			rename_keys.append((f"""backbone.stages.{i}.downsample.reduction.weight""", f"""backbone.encoder.layers.{i}.downsample.reduction.weight""")						)
			rename_keys.append((f"""backbone.stages.{i}.downsample.norm.weight""", f"""backbone.encoder.layers.{i}.downsample.norm.weight""")						)
			rename_keys.append((f"""backbone.stages.{i}.downsample.norm.bias""", f"""backbone.encoder.layers.{i}.downsample.norm.bias""")						)
		rename_keys.append((f"""backbone.norm{i}.weight""", f"""backbone.hidden_states_norms.stage{i+1}.weight""")						)
		rename_keys.append((f"""backbone.norm{i}.bias""", f"""backbone.hidden_states_norms.stage{i+1}.bias""")						)

	# decode head
	rename_keys.extend(
	    [
	        ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
	        ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
	        ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
	        ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
	    ]						)
	# fmt: on

	return rename_keys





def a(lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	snake_case_   		=			dct.pop(lowercase__						)
	snake_case_   		=			val





def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	snake_case_   		=			[int(backbone_config.embed_dim * 2**i						) for i in range(len(backbone_config.depths						)						)]
	for i in range(len(backbone_config.depths						)						):
		snake_case_   		=			num_features[i]
		for j in range(backbone_config.depths[i]						):
			# fmt: off
			# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
			snake_case_   		=			state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""						)
			snake_case_   		=			state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"""						)
			# next, add query, keys and values (in that order) to the state dict
			snake_case_   		=			in_proj_weight[:dim, :]
			snake_case_   		=			in_proj_bias[: dim]
			snake_case_   		=			in_proj_weight[
			    dim : dim * 2, :
			]
			snake_case_   		=			in_proj_bias[
			    dim : dim * 2
			]
			snake_case_   		=			in_proj_weight[
			    -dim :, :
			]
			snake_case_   		=			in_proj_bias[-dim :]
			# fmt: on





def a(lowercase__						):





	'''simple docstring'''
	snake_case_		,							snake_case_   		=			x.shape
	snake_case_   		=			x.reshape(lowercase__      ,			4      ,			in_channel // 4						)
	snake_case_   		=			x[:, [0, 2, 1, 3], :].transpose(1      ,			2						).reshape(lowercase__      ,			lowercase__						)
	return x





def a(lowercase__						):





	'''simple docstring'''
	snake_case_		,							snake_case_   		=			x.shape
	snake_case_   		=			x.reshape(lowercase__      ,			in_channel // 4      ,			4						)
	snake_case_   		=			x[:, :, [0, 2, 1, 3]].transpose(1      ,			2						).reshape(lowercase__      ,			lowercase__						)

	return x





def a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			x.shape[0]
	snake_case_   		=			x.reshape(4      ,			in_channel // 4						)
	snake_case_   		=			x[[0, 2, 1, 3], :].transpose(0      ,			1						).reshape(lowercase__						)
	return x





def a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			x.shape[0]
	snake_case_   		=			x.reshape(in_channel // 4      ,			4						)
	snake_case_   		=			x[:, [0, 2, 1, 3]].transpose(0      ,			1						).reshape(lowercase__						)
	return x





def a(lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	snake_case_   		=			{
	    'upernet-swin-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth',
	    'upernet-swin-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth',
	    'upernet-swin-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth',
	    'upernet-swin-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth',
	}
	snake_case_   		=			model_name_to_url[model_name]
	snake_case_   		=			torch.hub.load_state_dict_from_url(lowercase__      ,			map_location='cpu'      ,			file_name=lowercase__						)[
	    'state_dict'
	]

	for name, param in state_dict.items():
		print(lowercase__      ,			param.shape						)

	snake_case_   		=			get_upernet_config(lowercase__						)
	snake_case_   		=			UperNetForSemanticSegmentation(lowercase__						)
	model.eval()

	# replace "bn" => "batch_norm"
	for key in state_dict.copy().keys():
		snake_case_   		=			state_dict.pop(lowercase__						)
		if "bn" in key:
			snake_case_   		=			key.replace('bn'      ,			'batch_norm'						)
		snake_case_   		=			val

	# rename keys
	snake_case_   		=			create_rename_keys(lowercase__						)
	for src, dest in rename_keys:
		rename_key(lowercase__      ,			lowercase__      ,			lowercase__						)
	read_in_q_k_v(lowercase__      ,			config.backbone_config						)

	# fix downsample parameters
	for key, value in state_dict.items():
		if "downsample" in key:
			if "reduction" in key:
				snake_case_   		=			reverse_correct_unfold_reduction_order(lowercase__						)
			if "norm" in key:
				snake_case_   		=			reverse_correct_unfold_norm_order(lowercase__						)

	model.load_state_dict(lowercase__						)

	# verify on image
	snake_case_   		=			'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
	snake_case_   		=			Image.open(requests.get(lowercase__      ,			stream=lowercase__						).raw						).convert('RGB'						)

	snake_case_   		=			SegformerImageProcessor()
	snake_case_   		=			processor(lowercase__      ,			return_tensors='pt'						).pixel_values

	with torch.no_grad():
		snake_case_   		=			model(lowercase__						)
		snake_case_   		=			outputs.logits

	print(logits.shape						)
	print('First values of logits:'      ,			logits[0, 0, :3, :3]						)
	# assert values
	if model_name == "upernet-swin-tiny":
		snake_case_   		=			torch.tensor(
		    [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]						)
	elif model_name == "upernet-swin-small":
		snake_case_   		=			torch.tensor(
		    [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]						)
	elif model_name == "upernet-swin-base":
		snake_case_   		=			torch.tensor(
		    [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]						)
	elif model_name == "upernet-swin-large":
		snake_case_   		=			torch.tensor(
		    [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]						)
	print('Logits:'      ,			outputs.logits[0, 0, :3, :3]						)
	assert torch.allclose(outputs.logits[0, 0, :3, :3]      ,			lowercase__      ,			atol=1e-4						)
	print('Looks ok!'						)

	if pytorch_dump_folder_path is not None:
		print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""						)
		model.save_pretrained(lowercase__						)
		print(f"""Saving processor to {pytorch_dump_folder_path}"""						)
		processor.save_pretrained(lowercase__						)

	if push_to_hub:
		print(f"""Pushing model and processor for {model_name} to hub"""						)
		model.push_to_hub(f"""openmmlab/{model_name}"""						)
		processor.push_to_hub(f"""openmmlab/{model_name}"""						)


if __name__ == "__main__":
		A									=  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--model_name',
		    default='upernet-swin-tiny',
		    type=str,
		    choices=[f"""upernet-swin-{size}""" for size in ['tiny', 'small', 'base', 'large']],
		    help='Name of the Swin + UperNet model you\'d like to convert.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
		)
		parser.add_argument(
		    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
		)

		A									=  parser.parse_args()
		convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
