





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class     snake_case_		(   a,		a,		a,		unittest.TestCase							):






 '''simple docstring'''





 __UpperCamelCase       =      StableDiffusionInpaintPipeline
 __UpperCamelCase       =      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 __UpperCamelCase       =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __UpperCamelCase       =      frozenset(
     []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 __UpperCamelCase       =      frozenset([]							)

 def  __UpperCAmelCase						(  self		)	->       Any:
    torch.manual_seed(0		)
    UpperCAmelCase__						=UNetaDConditionModel(
        block_out_channels=(32, 64),		layers_per_block=2,		sample_size=32,		in_channels=9,		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_,		)
    UpperCAmelCase__						=PNDMScheduler(skip_prk_steps=A_		)
    torch.manual_seed(0		)
    UpperCAmelCase__						=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		)
    UpperCAmelCase__						=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,		)
    UpperCAmelCase__						=CLIPTextModel(A_		)
    UpperCAmelCase__						=CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

    UpperCAmelCase__						={
        "unet": unet,
        "scheduler": scheduler,
        "vae": vae,
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "safety_checker": None,
        "feature_extractor": None,
    }
    return components

 def  __UpperCAmelCase						(  self,		A_,		A_=0		)	->       List[Any]:
    # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
    UpperCAmelCase__						=floats_tensor((1, 3, 32, 32),		rng=random.Random(A_		)		).to(A_		)
    UpperCAmelCase__						=image.cpu().permute(0,		2,		3,		1		)[0]
    UpperCAmelCase__						=Image.fromarray(np.uinta(A_		)		).convert("RGB"		).resize((64, 64)		)
    UpperCAmelCase__						=Image.fromarray(np.uinta(image + 4		)		).convert("RGB"		).resize((64, 64)		)
    if str(A_		).startswith("mps"		):
       UpperCAmelCase__						=torch.manual_seed(A_		)
    else:
       UpperCAmelCase__						=torch.Generator(device=A_		).manual_seed(A_		)
    UpperCAmelCase__						={
        "prompt": "A painting of a squirrel eating a burger",
        "image": init_image,
        "mask_image": mask_image,
        "generator": generator,
        "num_inference_steps": 2,
        "guidance_scale": 6.0,
        "output_type": "numpy",
    }
    return inputs

 def  __UpperCAmelCase						(  self		)	->       Dict:
    UpperCAmelCase__						="cpu"  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase__						=self.get_dummy_components()
    UpperCAmelCase__						=StableDiffusionInpaintPipeline(**A_		)
    UpperCAmelCase__						=sd_pipe.to(A_		)
    sd_pipe.set_progress_bar_config(disable=A_		)

    UpperCAmelCase__						=self.get_dummy_inputs(A_		)
    UpperCAmelCase__						=sd_pipe(**A_		).images
    UpperCAmelCase__						=image[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)
    UpperCAmelCase__						=np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]		)

    assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2






 def  __UpperCAmelCase						(  self		)	->       Tuple:
    super().test_inference_batch_single_identical(expected_max_diff=3E-3		)

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






 '''simple docstring'''





 def  __UpperCAmelCase						(  self		)	->       Optional[int]:
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()

 def  __UpperCAmelCase						(  self		)	->       List[str]:
    UpperCAmelCase__						=load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/sd2-inpaint/init_image.png"		)
    UpperCAmelCase__						=load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"		)
    UpperCAmelCase__						=load_numpy(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
        "/yellow_cat_sitting_on_a_park_bench.npy"		)

    UpperCAmelCase__						="stabilityai/stable-diffusion-2-inpainting"
    UpperCAmelCase__						=StableDiffusionInpaintPipeline.from_pretrained(A_,		safety_checker=A_		)
    pipe.to(A_		)
    pipe.set_progress_bar_config(disable=A_		)
    pipe.enable_attention_slicing()

    UpperCAmelCase__						="Face of a yellow cat, high resolution, sitting on a park bench"

    UpperCAmelCase__						=torch.manual_seed(0		)
    UpperCAmelCase__						=pipe(
        prompt=A_,		image=A_,		mask_image=A_,		generator=A_,		output_type="np",		)
    UpperCAmelCase__						=output.images[0]

    assert image.shape == (512, 512, 3)
    assert np.abs(expected_image - image		).max() < 9E-3

 def  __UpperCAmelCase						(  self		)	->       Optional[int]:
    UpperCAmelCase__						=load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/sd2-inpaint/init_image.png"		)
    UpperCAmelCase__						=load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"		)
    UpperCAmelCase__						=load_numpy(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
        "/yellow_cat_sitting_on_a_park_bench_fp16.npy"		)

    UpperCAmelCase__						="stabilityai/stable-diffusion-2-inpainting"
    UpperCAmelCase__						=StableDiffusionInpaintPipeline.from_pretrained(
        A_,		torch_dtype=torch.floataa,		safety_checker=A_,		)
    pipe.to(A_		)
    pipe.set_progress_bar_config(disable=A_		)
    pipe.enable_attention_slicing()

    UpperCAmelCase__						="Face of a yellow cat, high resolution, sitting on a park bench"

    UpperCAmelCase__						=torch.manual_seed(0		)
    UpperCAmelCase__						=pipe(
        prompt=A_,		image=A_,		mask_image=A_,		generator=A_,		output_type="np",		)
    UpperCAmelCase__						=output.images[0]

    assert image.shape == (512, 512, 3)
    assert np.abs(expected_image - image		).max() < 5E-1






 def  __UpperCAmelCase						(  self		)	->       int:
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()

    UpperCAmelCase__						=load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/sd2-inpaint/init_image.png"		)
    UpperCAmelCase__						=load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"		)

    UpperCAmelCase__						="stabilityai/stable-diffusion-2-inpainting"
    UpperCAmelCase__						=PNDMScheduler.from_pretrained(A_,		subfolder="scheduler"		)
    UpperCAmelCase__						=StableDiffusionInpaintPipeline.from_pretrained(
        A_,		safety_checker=A_,		scheduler=A_,		torch_dtype=torch.floataa,		)
    pipe.to(A_		)
    pipe.set_progress_bar_config(disable=A_		)
    pipe.enable_attention_slicing(1		)
    pipe.enable_sequential_cpu_offload()

    UpperCAmelCase__						="Face of a yellow cat, high resolution, sitting on a park bench"

    UpperCAmelCase__						=torch.manual_seed(0		)
    UpperCAmelCase__						=pipe(
        prompt=A_,		image=A_,		mask_image=A_,		generator=A_,		num_inference_steps=2,		output_type="np",		)

    UpperCAmelCase__						=torch.cuda.max_memory_allocated()
    # make sure that less than 2.65 GB is allocated
    assert mem_bytes < 2.65 * 10**9










import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase_												=	logging.get_logger(__name__)






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=SwinConfig.from_pretrained(
       "microsoft/swin-tiny-patch4-window7-224"				,							out_features=["stage1", "stage2", "stage3", "stage4"]     )
   UpperCAmelCase__						=MaskFormerConfig(backbone_config=A     )

   UpperCAmelCase__						="huggingface/label-files"
   if "ade20k-full" in model_name:
      # this should be ok
      UpperCAmelCase__						=847
      UpperCAmelCase__						="maskformer-ade20k-full-id2label.json"
   elif "ade" in model_name:
      # this should be ok
      UpperCAmelCase__						=150
      UpperCAmelCase__						="ade20k-id2label.json"
   elif "coco-stuff" in model_name:
      # this should be ok
      UpperCAmelCase__						=171
      UpperCAmelCase__						="maskformer-coco-stuff-id2label.json"
   elif "coco" in model_name:
      # TODO
      UpperCAmelCase__						=133
      UpperCAmelCase__						="coco-panoptic-id2label.json"
   elif "cityscapes" in model_name:
      # this should be ok
      UpperCAmelCase__						=19
      UpperCAmelCase__						="cityscapes-id2label.json"
   elif "vistas" in model_name:
      # this should be ok
      UpperCAmelCase__						=65
      UpperCAmelCase__						="mapillary-vistas-id2label.json"

   UpperCAmelCase__						=json.load(open(hf_hub_download(A				,							A				,							repo_type="dataset"     )				,							"r"     )     )
   UpperCAmelCase__						={int(A     ): v for k, v in idalabel.items()}

   return config






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=[]
   # stem
   # fmt: off
   rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight")     )
   rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias")     )
   rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight")     )
   rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.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.layers.{i}.blocks.{j}.norm1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")     )
         rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")     )
         rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")     )
         rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")     )
         rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")     )
         rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")     )
         rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")     )
         rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")     )
         rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")     )
         rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")     )
         rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")     )
         rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")     )

      if i < 3:
         rename_keys.append((F"""backbone.layers.{i}.downsample.reduction.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")     )
         rename_keys.append((F"""backbone.layers.{i}.downsample.norm.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")     )
         rename_keys.append((F"""backbone.layers.{i}.downsample.norm.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")     )
      rename_keys.append((F"""backbone.norm{i}.weight""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")     )
      rename_keys.append((F"""backbone.norm{i}.bias""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")     )

   # FPN
   rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight")     )
   rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight")     )
   rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias")     )
   for source_index, target_index in zip(range(3				,							0				,							-1     )				,							range(0				,							3     )     ):
      rename_keys.append((F"""sem_seg_head.adapter_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")     )
      rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")     )
      rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")     )
      rename_keys.append((F"""sem_seg_head.layer_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")     )
      rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")     )
      rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")     )
   rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight")     )
   rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias")     )

   # Transformer decoder
   for idx in range(config.decoder_config.decoder_layers     ):
      # self-attention out projection
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")     )
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")     )
      # cross-attention out projection
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")     )
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")     )
      # MLP 1
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")     )
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")     )
      # MLP 2
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")     )
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")     )
      # layernorm 1 (self-attention layernorm)
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")     )
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")     )
      # layernorm 2 (cross-attention layernorm)
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")     )
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")     )
      # layernorm 3 (final layernorm)
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")     )
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")     )

   rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight")     )
   rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias")     )

   # heads on top
   rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight")     )

   rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight")     )
   rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias")     )

   rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight")     )
   rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias")     )

   for i in range(3     ):
      rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", F"""mask_embedder.{i}.0.weight""")     )
      rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", F"""mask_embedder.{i}.0.bias""")     )
   # fmt: on

   return rename_keys






def 				_UpperCAmelCase			(    A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=dct.pop(A     )
   UpperCAmelCase__						=val






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=[int(backbone_config.embed_dim * 2**i     ) for i in range(len(backbone_config.depths     )     )]
   for i in range(len(backbone_config.depths     )     ):
      UpperCAmelCase__						=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)
         UpperCAmelCase__						=state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""     )
         UpperCAmelCase__						=state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""     )
         # next, add query, keys and values (in that order) to the state dict
         UpperCAmelCase__						=in_proj_weight[:dim, :]
         UpperCAmelCase__						=in_proj_bias[: dim]
         UpperCAmelCase__						=in_proj_weight[
             dim : dim * 2, :
         ]
         UpperCAmelCase__						=in_proj_bias[
             dim : dim * 2
         ]
         UpperCAmelCase__						=in_proj_weight[
             -dim :, :
         ]
         UpperCAmelCase__						=in_proj_bias[-dim :]
         # fmt: on






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=config.decoder_config.hidden_size
   for idx in range(config.decoder_config.decoder_layers     ):
      # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
      UpperCAmelCase__						=state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""     )
      UpperCAmelCase__						=state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""     )
      # next, add query, keys and values (in that order) to the state dict
      UpperCAmelCase__						=in_proj_weight[: hidden_size, :]
      UpperCAmelCase__						=in_proj_bias[:config.hidden_size]
      UpperCAmelCase__						=in_proj_weight[hidden_size : hidden_size * 2, :]
      UpperCAmelCase__						=in_proj_bias[hidden_size : hidden_size * 2]
      UpperCAmelCase__						=in_proj_weight[-hidden_size :, :]
      UpperCAmelCase__						=in_proj_bias[-hidden_size :]
      # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
      UpperCAmelCase__						=state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""     )
      UpperCAmelCase__						=state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""     )
      # next, add query, keys and values (in that order) to the state dict
      UpperCAmelCase__						=in_proj_weight[: hidden_size, :]
      UpperCAmelCase__						=in_proj_bias[:config.hidden_size]
      UpperCAmelCase__						=in_proj_weight[hidden_size : hidden_size * 2, :]
      UpperCAmelCase__						=in_proj_bias[hidden_size : hidden_size * 2]
      UpperCAmelCase__						=in_proj_weight[-hidden_size :, :]
      UpperCAmelCase__						=in_proj_bias[-hidden_size :]
   # fmt: on






def 				_UpperCAmelCase			(    ):




   '''simple docstring'''

   UpperCAmelCase__						="http://images.cocodataset.org/val2017/000000039769.jpg"
   UpperCAmelCase__						=Image.open(requests.get(A				,							stream=A     ).raw     )
   return im






@torch.no_grad()
def 				_UpperCAmelCase			(    A				,							A				,							A				,							A = False     ):




   '''simple docstring'''

   UpperCAmelCase__						=get_maskformer_config(A     )

   # load original state_dict
   with open(A				,							"rb"     ) as f:
      UpperCAmelCase__						=pickle.load(A     )
   UpperCAmelCase__						=data["model"]

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

   # rename keys
   UpperCAmelCase__						=create_rename_keys(A     )
   for src, dest in rename_keys:
      rename_key(A				,							A				,							A     )
   read_in_swin_q_k_v(A				,							config.backbone_config     )
   read_in_decoder_q_k_v(A				,							A     )

   # update to torch tensors
   for key, value in state_dict.items():
      UpperCAmelCase__						=torch.from_numpy(A     )

   # load 🤗 model
   UpperCAmelCase__						=MaskFormerForInstanceSegmentation(A     )
   model.eval()

   for name, param in model.named_parameters():
      print(A				,							param.shape     )

   UpperCAmelCase__			,							UpperCAmelCase__						=model.load_state_dict(A				,							strict=A     )
   assert missing_keys == [
       "model.pixel_level_module.encoder.model.layernorm.weight",
       "model.pixel_level_module.encoder.model.layernorm.bias",
   ]
   assert len(A     ) == 0, F"""Unexpected keys: {unexpected_keys}"""

   # verify results
   UpperCAmelCase__						=prepare_img()
   if "vistas" in model_name:
      UpperCAmelCase__						=65
   elif "cityscapes" in model_name:
      UpperCAmelCase__						=65535
   else:
      UpperCAmelCase__						=255
   UpperCAmelCase__						=True if "ade" in model_name else False
   UpperCAmelCase__						=MaskFormerImageProcessor(ignore_index=A				,							reduce_labels=A     )

   UpperCAmelCase__						=image_processor(A				,							return_tensors="pt"     )

   UpperCAmelCase__						=model(**A     )

   print("Logits:"				,							outputs.class_queries_logits[0, :3, :3]     )

   if model_name == "maskformer-swin-tiny-ade":
      UpperCAmelCase__						=torch.tensor(
          [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]     )
   assert torch.allclose(outputs.class_queries_logits[0, :3, :3]				,							A				,							atol=1e-4     )
   print("Looks ok!"     )

   if pytorch_dump_folder_path is not None:
      print(F"""Saving model and image processor to {pytorch_dump_folder_path}"""     )
      Path(A     ).mkdir(exist_ok=A     )
      model.save_pretrained(A     )
      image_processor.save_pretrained(A     )

   if push_to_hub:
      print("Pushing model and image processor to the hub..."     )
      model.push_to_hub(F"""nielsr/{model_name}"""     )
      image_processor.push_to_hub(F"""nielsr/{model_name}"""     )


if __name__ == "__main__":
    UpperCamelCase_												=	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--model_name',
        default='maskformer-swin-tiny-ade',
        type=str,
        help=('Name of the MaskFormer model you\'d like to convert',),
    )
    parser.add_argument(
        '--checkpoint_path',
        default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
        type=str,
        help='Path to the original state dict (.pth file).',
    )
    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.'
    )

    UpperCamelCase_												=	parser.parse_args()
    convert_maskformer_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )



