

'''simple docstring'''




from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_lowerCAmelCase         =						logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowerCAmelCase         =						"\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"




def 						UpperCamelCase	(			a		,						a		,						a=8			)				->     Any:






     '''simple docstring'''
     __magic_name__						       =    height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     __magic_name__						       =    width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor






class   _SCREAMING_SNAKE_CASE   (   __a     ):



 def __init__(   self		:   Union[str, Any]			,				a__		:   UNetaDConditionModel			,				a__		:   DDPMScheduler			,				a__		:   VQModel			,				):
      super().__init__()

      self.register_modules(
          unet=a__			,				scheduler=a__			,				movq=a__			,				)
      __magic_name__						       =    2 ** (len(self.movq.config.block_out_channels      ) - 1)



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   List[Any]			,				a__		:   List[str]			,				a__		:   Tuple			,				a__		:   int			,				a__		:   Tuple			,				a__		:   Tuple      ):
      if latents is None:
           __magic_name__						       =    randn_tensor(a__			,				generator=a__			,				device=a__			,				dtype=a__      )
      else:
           if latents.shape != shape:
                raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''      )
           __magic_name__						       =    latents.to(a__      )

      __magic_name__						       =    latents * scheduler.init_noise_sigma
      return latents



 def 			snake_case__  (   self		:   int			,				a__		:   Optional[Any]=0      ):
      if is_accelerate_available():
           from accelerate import cpu_offload
      else:
           raise ImportError('''Please install accelerate via `pip install accelerate`'''      )

      __magic_name__						       =    torch.device(F'''cuda:{gpu_id}'''      )

      __magic_name__						       =    [
          self.unet,
          self.movq,
      ]
      for cpu_offloaded_model in models:
           if cpu_offloaded_model is not None:
                cpu_offload(a__			,				a__      )



 def 			snake_case__  (   self		:   Dict			,				a__		:   List[Any]=0      ):
      if is_accelerate_available() and is_accelerate_version('''>='''			,				'''0.17.0.dev0'''      ):
           from accelerate import cpu_offload_with_hook
      else:
           raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''      )

      __magic_name__						       =    torch.device(F'''cuda:{gpu_id}'''      )

      if self.device.type != "cpu":
           self.to('''cpu'''			,				silence_dtype_warnings=a__      )
           torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

      __magic_name__						       =    None
      for cpu_offloaded_model in [self.unet, self.movq]:
           __magic_name__       ,  __magic_name__						       =    cpu_offload_with_hook(a__			,				a__			,				prev_module_hook=a__      )

      # We'll offload the last model manually.
      __magic_name__						       =    hook



 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 			snake_case__  (   self		:   Tuple      ):
      if not hasattr(self.unet			,				'''_hf_hook'''      ):
           return self.device
      for module in self.unet.modules():
           if (
               hasattr(a__			,				'''_hf_hook'''      )
               and hasattr(module._hf_hook			,				'''execution_device'''      )
               and module._hf_hook.execution_device is not None
           ):
                return torch.device(module._hf_hook.execution_device      )
      return self.device



 @torch.no_grad()
 @replace_example_docstring(a__      )
 def __call__(   self		:   int			,				a__		:   Union[torch.FloatTensor, List[torch.FloatTensor]]			,				a__		:   Union[torch.FloatTensor, List[torch.FloatTensor]]			,				a__		:   torch.FloatTensor			,				a__		:   int = 512			,				a__		:   int = 512			,				a__		:   int = 100			,				a__		:   float = 4.0			,				a__		:   int = 1			,				a__		:   Optional[Union[torch.Generator, List[torch.Generator]]] = None			,				a__		:   Optional[torch.FloatTensor] = None			,				a__		:   Optional[str] = "pil"			,				a__		:   bool = True			,				):
      __magic_name__						       =    self._execution_device

      __magic_name__						       =    guidance_scale > 1.0

      if isinstance(a__			,				a__      ):
           __magic_name__						       =    torch.cat(a__			,				dim=0      )
      if isinstance(a__			,				a__      ):
           __magic_name__						       =    torch.cat(a__			,				dim=0      )
      if isinstance(a__			,				a__      ):
           __magic_name__						       =    torch.cat(a__			,				dim=0      )

      __magic_name__						       =    image_embeds.shape[0] * num_images_per_prompt

      if do_classifier_free_guidance:
           __magic_name__						       =    image_embeds.repeat_interleave(a__			,				dim=0      )
           __magic_name__						       =    negative_image_embeds.repeat_interleave(a__			,				dim=0      )
           __magic_name__						       =    hint.repeat_interleave(a__			,				dim=0      )

      __magic_name__						       =    torch.cat([negative_image_embeds, image_embeds]			,				dim=0      ).to(dtype=self.unet.dtype			,				device=a__      )
      __magic_name__						       =    torch.cat([hint, hint]			,				dim=0      ).to(dtype=self.unet.dtype			,				device=a__      )
      self.scheduler.set_timesteps(a__			,				device=a__      )
      __magic_name__						       =    self.scheduler.timesteps

      __magic_name__						       =    self.movq.config.latent_channels

      __magic_name__       ,  __magic_name__						       =    downscale_height_and_width(a__			,				a__			,				self.movq_scale_factor      )

      # create initial latent
      __magic_name__						       =    self.prepare_latents(
          (batch_size, num_channels_latents, height, width)			,				image_embeds.dtype			,				a__			,				a__			,				a__			,				self.scheduler			,				)

      for i, t in enumerate(self.progress_bar(a__      )      ):
           # expand the latents if we are doing classifier free guidance
           __magic_name__						       =    torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents

           __magic_name__						       =    {'''image_embeds''': image_embeds, '''hint''': hint}
           __magic_name__						       =    self.unet(
               sample=a__			,				timestep=a__			,				encoder_hidden_states=a__			,				added_cond_kwargs=a__			,				return_dict=a__			,				)[0]

           if do_classifier_free_guidance:
                __magic_name__       ,  __magic_name__						       =    noise_pred.split(latents.shape[1]			,				dim=1      )
                __magic_name__       ,  __magic_name__						       =    noise_pred.chunk(2      )
                __magic_name__       ,  __magic_name__						       =    variance_pred.chunk(2      )
                __magic_name__						       =    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                __magic_name__						       =    torch.cat([noise_pred, variance_pred_text]			,				dim=1      )

           if not (
               hasattr(self.scheduler.config			,				'''variance_type'''      )
               and self.scheduler.config.variance_type in ["learned", "learned_range"]
           ):
                __magic_name__       ,  __magic_name__						       =    noise_pred.split(latents.shape[1]			,				dim=1      )

           # compute the previous noisy sample x_t -> x_t-1
           __magic_name__						       =    self.scheduler.step(
               a__			,				a__			,				a__			,				generator=a__			,				)[0]
      # post-processing
      __magic_name__						       =    self.movq.decode(a__			,				force_not_quantize=a__      )['''sample''']

      if output_type not in ["pt", "np", "pil"]:
           raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''      )

      if output_type in ["np", "pil"]:
           __magic_name__						       =    image * 0.5 + 0.5
           __magic_name__						       =    image.clamp(0			,				1      )
           __magic_name__						       =    image.cpu().permute(0			,				2			,				3			,				1      ).float().numpy()

      if output_type == "pil":
           __magic_name__						       =    self.numpy_to_pil(a__      )

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=a__      )









'''simple docstring'''




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()
_lowerCAmelCase         =						logging.get_logger(__name__)




def 						UpperCamelCase	(			a			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    SwinConfig.from_pretrained(
         '''microsoft/swin-tiny-patch4-window7-224'''		,						out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']			)
     __magic_name__						       =    MaskFormerConfig(backbone_config=a			)

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

     __magic_name__						       =    json.load(open(hf_hub_download(a		,						a		,						repo_type='''dataset'''			)		,						'''r'''			)			)
     __magic_name__						       =    {int(a			): v for k, v in idalabel.items()}

     return config




def 						UpperCamelCase	(			a			)				->     Tuple:






     '''simple docstring'''
     __magic_name__						       =    []
     # 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			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    dct.pop(a			)
     __magic_name__						       =    val




def 						UpperCamelCase	(			a		,						a			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    [int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
     for i in range(len(backbone_config.depths			)			):
          __magic_name__						       =    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)
               __magic_name__						       =    state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''			)
               __magic_name__						       =    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
               __magic_name__						       =    in_proj_weight[:dim, :]
               __magic_name__						       =    in_proj_bias[: dim]
               __magic_name__						       =    in_proj_weight[
                   dim : dim * 2, :
               ]
               __magic_name__						       =    in_proj_bias[
                   dim : dim * 2
               ]
               __magic_name__						       =    in_proj_weight[
                   -dim :, :
               ]
               __magic_name__						       =    in_proj_bias[-dim :]
               # fmt: on




def 						UpperCamelCase	(			a		,						a			)				->     int:






     '''simple docstring'''
     # fmt: off
     __magic_name__						       =    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)
          __magic_name__						       =    state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''			)
          __magic_name__						       =    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
          __magic_name__						       =    in_proj_weight[: hidden_size, :]
          __magic_name__						       =    in_proj_bias[:config.hidden_size]
          __magic_name__						       =    in_proj_weight[hidden_size : hidden_size * 2, :]
          __magic_name__						       =    in_proj_bias[hidden_size : hidden_size * 2]
          __magic_name__						       =    in_proj_weight[-hidden_size :, :]
          __magic_name__						       =    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)
          __magic_name__						       =    state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''			)
          __magic_name__						       =    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
          __magic_name__						       =    in_proj_weight[: hidden_size, :]
          __magic_name__						       =    in_proj_bias[:config.hidden_size]
          __magic_name__						       =    in_proj_weight[hidden_size : hidden_size * 2, :]
          __magic_name__						       =    in_proj_bias[hidden_size : hidden_size * 2]
          __magic_name__						       =    in_proj_weight[-hidden_size :, :]
          __magic_name__						       =    in_proj_bias[-hidden_size :]
     # fmt: on




def 						UpperCamelCase	(			)				->     torch.Tensor:






     '''simple docstring'''
     __magic_name__						       =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     __magic_name__						       =    Image.open(requests.get(a		,						stream=a			).raw			)
     return im




@torch.no_grad()
def 						UpperCamelCase	(			a		,						a		,						a		,						a = False			)				->     Dict:






     '''simple docstring'''
     __magic_name__						       =    get_maskformer_config(a			)

     # load original state_dict
     with open(a		,						'''rb'''			) as f:
          __magic_name__						       =    pickle.load(a			)
     __magic_name__						       =    data['''model''']

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

     # rename keys
     __magic_name__						       =    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():
          __magic_name__						       =    torch.from_numpy(a			)

     # load 🤗 model
     __magic_name__						       =    MaskFormerForInstanceSegmentation(a			)
     model.eval()

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

     __magic_name__       ,  __magic_name__						       =    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
     __magic_name__						       =    prepare_img()
     if "vistas" in model_name:
          __magic_name__						       =    65
     elif "cityscapes" in model_name:
          __magic_name__						       =    6_5535
     else:
          __magic_name__						       =    255
     __magic_name__						       =    True if '''ade''' in model_name else False
     __magic_name__						       =    MaskFormerImageProcessor(ignore_index=a		,						reduce_labels=a			)

     __magic_name__						       =    image_processor(a		,						return_tensors='''pt'''			)

     __magic_name__						       =    model(**a			)

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

     if model_name == "maskformer-swin-tiny-ade":
          __magic_name__						       =    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__":
      _lowerCAmelCase         =						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."
      )

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






