




from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
       import PIL

if is_torch_available():
       import torch


lowerCamelCase	:	Any				=   logging.get_logger(__name__)




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      _snake_case												=						["""pixel_values"""]

      def __init__(				self    ,				A = True    ,				A = None    ,				A = PILImageResampling.BILINEAR    ,				A = True    ,				A = 1 / 2_5_5    ,				A = True    ,				A = None    ,				A = True    ,				**A    ,				)		->    None:
         super().__init__(**A     )
         snake_case				:    Optional[Any]           =	size if size is not None else {"""shortest_edge""": 2_2_4}
         snake_case				:    Optional[Any]           =	get_size_dict(A    ,				default_to_square=A     )
         snake_case				:    Tuple           =	crop_size if crop_size is not None else {"""height""": 2_5_6, """width""": 2_5_6}
         snake_case				:    Any           =	get_size_dict(A    ,				param_name="""crop_size"""     )

         snake_case				:    int           =	do_resize
         snake_case				:    Tuple           =	size
         snake_case				:    Optional[int]           =	resample
         snake_case				:    Tuple           =	do_rescale
         snake_case				:    Optional[int]           =	rescale_factor
         snake_case				:    str           =	do_center_crop
         snake_case				:    Union[str, Any]           =	crop_size
         snake_case				:    Tuple           =	do_flip_channel_order

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A = PIL.Image.BILINEAR    ,				A = None    ,				**A    ,				)		->    np.ndarray:
         snake_case				:    Union[str, Any]           =	get_size_dict(A    ,				default_to_square=A     )
         if "shortest_edge" not in size:
            raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""     )
         snake_case				:    Any           =	get_resize_output_image_size(A    ,				size=size["""shortest_edge"""]    ,				default_to_square=A     )
         return resize(A    ,				size=A    ,				resample=A    ,				data_format=A    ,				**A     )

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A = None    ,				**A    ,				)		->    np.ndarray:
         snake_case				:    Optional[Any]           =	get_size_dict(A     )
         if "height" not in size or "width" not in size:
            raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""     )
         return center_crop(A    ,				size=(size["""height"""], size["""width"""])    ,				data_format=A    ,				**A     )

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A = None    ,				**A    ,				)		->    str:
         return rescale(A    ,				scale=A    ,				data_format=A    ,				**A     )

      def      UpperCAmelCase				(				self    ,				A    ,				A = None     )		->    np.ndarray:
         return flip_channel_order(A    ,				data_format=A     )

      def      UpperCAmelCase				(				self    ,				A    ,				A = None    ,				A = None    ,				A = None    ,				A = None    ,				A = None    ,				A = None    ,				A = None    ,				A = None    ,				A = None    ,				A = ChannelDimension.FIRST    ,				**A    ,				)		->    PIL.Image.Image:
         snake_case				:    Dict           =	do_resize if do_resize is not None else self.do_resize
         snake_case				:    Optional[Any]           =	resample if resample is not None else self.resample
         snake_case				:    Union[str, Any]           =	do_rescale if do_rescale is not None else self.do_rescale
         snake_case				:    int           =	rescale_factor if rescale_factor is not None else self.rescale_factor
         snake_case				:    Optional[int]           =	do_center_crop if do_center_crop is not None else self.do_center_crop
         snake_case				:    int           =	(
             do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
         )

         snake_case				:    List[str]           =	size if size is not None else self.size
         snake_case				:    List[Any]           =	get_size_dict(A    ,				default_to_square=A     )
         snake_case				:    int           =	crop_size if crop_size is not None else self.crop_size
         snake_case				:    Union[str, Any]           =	get_size_dict(A    ,				param_name="""crop_size"""     )

         snake_case				:    Dict           =	make_list_of_images(A     )

         if not valid_images(A     ):
            raise ValueError(
                """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                """torch.Tensor, tf.Tensor or jax.ndarray."""     )

         if do_resize and size is None:
            raise ValueError("""Size must be specified if do_resize is True."""     )

         if do_rescale and rescale_factor is None:
            raise ValueError("""Rescale factor must be specified if do_rescale is True."""     )

         if do_center_crop and crop_size is None:
            raise ValueError("""Crop size must be specified if do_center_crop is True."""     )

         # All transformations expect numpy arrays.
         snake_case				:    List[Any]           =	[to_numpy_array(A     ) for image in images]

         if do_resize:
            snake_case				:    Optional[Any]           =	[self.resize(image=A    ,				size=A    ,				resample=A     ) for image in images]

         if do_center_crop:
            snake_case				:    List[str]           =	[self.center_crop(image=A    ,				size=A     ) for image in images]

         if do_rescale:
            snake_case				:    Dict           =	[self.rescale(image=A    ,				scale=A     ) for image in images]

         # the pretrained checkpoints assume images are BGR, not RGB
         if do_flip_channel_order:
            snake_case				:    int           =	[self.flip_channel_order(image=A     ) for image in images]

         snake_case				:    Optional[int]           =	[to_channel_dimension_format(A    ,				A     ) for image in images]

         snake_case				:    Optional[int]           =	{"""pixel_values""": images}
         return BatchFeature(data=A    ,				tensor_type=A     )

      def      UpperCAmelCase				(				self    ,				A    ,				A = None     )		->    Optional[int]:
         snake_case				:    str           =	outputs.logits

         # Resize logits and compute semantic segmentation maps
         if target_sizes is not None:
            if len(A     ) != len(A     ):
               raise ValueError(
                   """Make sure that you pass in as many target sizes as the batch dimension of the logits"""     )

            if is_torch_tensor(A     ):
               snake_case				:    Tuple           =	target_sizes.numpy()

            snake_case				:    str           =	[]

            for idx in range(len(A     )     ):
               snake_case				:    Optional[int]           =	torch.nn.functional.interpolate(
                   logits[idx].unsqueeze(dim=0     )    ,				size=target_sizes[idx]    ,				mode="""bilinear"""    ,				align_corners=A     )
               snake_case				:    int           =	resized_logits[0].argmax(dim=0     )
               semantic_segmentation.append(A     )
         else:
            snake_case				:    Optional[Any]           =	logits.argmax(dim=1     )
            snake_case				:    str           =	[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]     )]

         return semantic_segmentation











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	:	List[Any]				=   logging.get_logger(__name__)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       int:
   snake_case				:    Dict           =	SwinConfig.from_pretrained(
       """microsoft/swin-tiny-patch4-window7-224"""						,out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]					)
   snake_case				:    List[str]           =	MaskFormerConfig(backbone_config=lowercase					)

   snake_case				:    List[Any]           =	"""huggingface/label-files"""
   if "ade20k-full" in model_name:
      # this should be ok
      snake_case				:    Dict           =	847
      snake_case				:    List[str]           =	"""maskformer-ade20k-full-id2label.json"""
   elif "ade" in model_name:
      # this should be ok
      snake_case				:    Union[str, Any]           =	150
      snake_case				:    List[Any]           =	"""ade20k-id2label.json"""
   elif "coco-stuff" in model_name:
      # this should be ok
      snake_case				:    Union[str, Any]           =	171
      snake_case				:    int           =	"""maskformer-coco-stuff-id2label.json"""
   elif "coco" in model_name:
      # TODO
      snake_case				:    Optional[Any]           =	133
      snake_case				:    Optional[Any]           =	"""coco-panoptic-id2label.json"""
   elif "cityscapes" in model_name:
      # this should be ok
      snake_case				:    Tuple           =	19
      snake_case				:    int           =	"""cityscapes-id2label.json"""
   elif "vistas" in model_name:
      # this should be ok
      snake_case				:    int           =	65
      snake_case				:    Any           =	"""mapillary-vistas-id2label.json"""

   snake_case				:    Optional[Any]           =	json.load(open(hf_hub_download(lowercase						,lowercase						,repo_type="""dataset"""					)						,"""r"""					)					)
   snake_case				:    List[Any]           =	{int(lowercase					): v for k, v in idalabel.items()}

   return config




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[str]:
   snake_case				:    Union[str, Any]           =	[]
   # 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 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       str:
   snake_case				:    Tuple           =	dct.pop(lowercase					)
   snake_case				:    int           =	val




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       int:
   snake_case				:    List[str]           =	[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				:    Optional[Any]           =	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				:    Tuple           =	state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""					)
         snake_case				:    Optional[Any]           =	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
         snake_case				:    Optional[Any]           =	in_proj_weight[:dim, :]
         snake_case				:    Optional[int]           =	in_proj_bias[: dim]
         snake_case				:    Union[str, Any]           =	in_proj_weight[
             dim : dim * 2, :
         ]
         snake_case				:    Tuple           =	in_proj_bias[
             dim : dim * 2
         ]
         snake_case				:    List[Any]           =	in_proj_weight[
             -dim :, :
         ]
         snake_case				:    Any           =	in_proj_bias[-dim :]
         # fmt: on




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Any:
   # fmt: off
   snake_case				:    int           =	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)
      snake_case				:    Any           =	state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""					)
      snake_case				:    Tuple           =	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
      snake_case				:    Optional[int]           =	in_proj_weight[: hidden_size, :]
      snake_case				:    Any           =	in_proj_bias[:config.hidden_size]
      snake_case				:    Any           =	in_proj_weight[hidden_size : hidden_size * 2, :]
      snake_case				:    int           =	in_proj_bias[hidden_size : hidden_size * 2]
      snake_case				:    Any           =	in_proj_weight[-hidden_size :, :]
      snake_case				:    Union[str, Any]           =	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)
      snake_case				:    Dict           =	state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""					)
      snake_case				:    str           =	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
      snake_case				:    Dict           =	in_proj_weight[: hidden_size, :]
      snake_case				:    Dict           =	in_proj_bias[:config.hidden_size]
      snake_case				:    List[Any]           =	in_proj_weight[hidden_size : hidden_size * 2, :]
      snake_case				:    Optional[Any]           =	in_proj_bias[hidden_size : hidden_size * 2]
      snake_case				:    Tuple           =	in_proj_weight[-hidden_size :, :]
      snake_case				:    str           =	in_proj_bias[-hidden_size :]
   # fmt: on




def 						SCREAMING_SNAKE_CASE__ (   )  ->       torch.Tensor:
   snake_case				:    Any           =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   snake_case				:    Any           =	Image.open(requests.get(lowercase						,stream=lowercase					).raw					)
   return im




@torch.no_grad()
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase = False					)  ->       Dict:
   snake_case				:    List[str]           =	get_maskformer_config(lowercase					)

   # load original state_dict
   with open(lowercase						,"""rb"""					) as f:
      snake_case				:    Optional[Any]           =	pickle.load(lowercase					)
   snake_case				:    Optional[Any]           =	data["""model"""]

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

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

   # update to torch tensors
   for key, value in state_dict.items():
      snake_case				:    List[Any]           =	torch.from_numpy(lowercase					)

   # load 🤗 model
   snake_case				:    int           =	MaskFormerForInstanceSegmentation(lowercase					)
   model.eval()

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

   snake_case     , snake_case				:    Optional[int]           =	model.load_state_dict(lowercase						,strict=lowercase					)
   assert missing_keys == [
       "model.pixel_level_module.encoder.model.layernorm.weight",
       "model.pixel_level_module.encoder.model.layernorm.bias",
   ]
   assert len(lowercase					) == 0, f"""Unexpected keys: {unexpected_keys}"""

   # verify results
   snake_case				:    List[str]           =	prepare_img()
   if "vistas" in model_name:
      snake_case				:    Optional[int]           =	65
   elif "cityscapes" in model_name:
      snake_case				:    int           =	65535
   else:
      snake_case				:    List[str]           =	255
   snake_case				:    List[Any]           =	True if """ade""" in model_name else False
   snake_case				:    Optional[int]           =	MaskFormerImageProcessor(ignore_index=lowercase						,reduce_labels=lowercase					)

   snake_case				:    Tuple           =	image_processor(lowercase						,return_tensors="""pt"""					)

   snake_case				:    str           =	model(**lowercase					)

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

   if model_name == "maskformer-swin-tiny-ade":
      snake_case				:    Union[str, Any]           =	torch.tensor(
          [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]					)
   assert torch.allclose(outputs.class_queries_logits[0, :3, :3]						,lowercase						,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(lowercase					).mkdir(exist_ok=lowercase					)
      model.save_pretrained(lowercase					)
      image_processor.save_pretrained(lowercase					)

   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	:	Optional[Any]				=   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	:	List[str]				=   parser.parse_args()
       convert_maskformer_checkpoint(
           args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )





