



"""simple docstring"""





import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Tuple=None       ,			__UpperCamelCase   :      int=None     ):


						'''simple docstring'''






						return field(default_factory=lambda: default       ,			metadata=__UpperCamelCase     )



@dataclass
class  _lowerCAmelCase     :





		"""simple docstring"""






		_lowerCamelCase      	=							field(
		    metadata={'''help''': '''The csv file to plot.'''}	,					)
		_lowerCamelCase      	=							field(
		    default=SCREAMING_SNAKE_CASE__	,					metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}	,					)
		_lowerCamelCase      	=							field(
		    default=SCREAMING_SNAKE_CASE__	,					metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}	,					)
		_lowerCamelCase      	=							field(
		    default=SCREAMING_SNAKE_CASE__	,					metadata={'''help''': '''Disable logarithmic scale when plotting'''}	,					)
		_lowerCamelCase      	=							field(
		    default=SCREAMING_SNAKE_CASE__	,					metadata={
		        '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
		    }	,					)
		_lowerCamelCase      	=							field(
		    default=SCREAMING_SNAKE_CASE__	,					metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}	,					)
		_lowerCamelCase      	=							list_field(
		    default=SCREAMING_SNAKE_CASE__	,					metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}		)




def 						__lowerCAmelCase    (		__UpperCamelCase   :      Dict     ):


						'''simple docstring'''






						try:
												int(__UpperCamelCase     )
												return True
						except ValueError:
												return False
def 						__lowerCAmelCase    (		__UpperCamelCase   :      List[str]     ):


						'''simple docstring'''






						try:
												float(__UpperCamelCase     )
												return True
						except ValueError:
												return False



class  _lowerCAmelCase     :





		"""simple docstring"""






		def __init__(       self				,     _lowercase )			->    Tuple:







								'''simple docstring'''

								snake_case_ :			Any            =      args
								snake_case_ :			int            =      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )

								with open(self.args.csv_file				,     newline="""""" ) as csv_file:
														snake_case_ :			int            =      csv.DictReader(_lowercase )
														for row in reader:
																				snake_case_ :			Union[str, Any]            =      row["""model"""]
																				self.result_dict[model_name]["bsz"].append(int(row["""batch_size"""] ) )
																				self.result_dict[model_name]["seq_len"].append(int(row["""sequence_length"""] ) )
																				if can_convert_to_int(row["""result"""] ):
																										# value is not None
																										snake_case_ :			str            =      int(row["""result"""] )
																				elif can_convert_to_float(row["""result"""] ):
																										# value is not None
																										snake_case_ :			Optional[int]            =      float(row["""result"""] )





		def  UpperCAmelCase__				(       self )			->    int:







								'''simple docstring'''

								snake_case_      ,		snake_case_ :			List[str]            =      plt.subplots()
								snake_case_ :			Dict            =      """Time usage""" if self.args.is_time else """Memory usage"""
								snake_case_ :			str            =      title_str + """ for training""" if self.args.is_train else title_str + """ for inference"""

								if not self.args.no_log_scale:
														# set logarithm scales
														ax.set_xscale("""log""" )
														ax.set_yscale("""log""" )

								for axis in [ax.xaxis, ax.yaxis]:
														axis.set_major_formatter(ScalarFormatter() )

								for model_name_idx, model_name in enumerate(self.result_dict.keys() ):
														snake_case_ :			str            =      sorted(set(self.result_dict[model_name]["""bsz"""] ) )
														snake_case_ :			Tuple            =      sorted(set(self.result_dict[model_name]["""seq_len"""] ) )
														snake_case_ :			Tuple            =      self.result_dict[model_name]["""result"""]

														((snake_case_)      ,		(snake_case_)) :			int            =      (
														    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
														)

														snake_case_ :			Optional[Any]            =      (
														    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
														)

														for inner_loop_value in inner_loop_array:
																				if self.args.plot_along_batch:
																										snake_case_ :			Optional[Any]            =      np.asarray(
																										    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]				,     dtype=_lowercase				,     )
																				else:
																										snake_case_ :			Dict            =      np.asarray(
																										    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]				,     dtype=np.floataa				,     )

																				((snake_case_)      ,		(snake_case_)) :			Tuple            =      (
																				    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
																				)

																				snake_case_ :			Dict            =      np.asarray(_lowercase				,     _lowercase )[: len(_lowercase )]
																				plt.scatter(
																				    _lowercase				,     _lowercase				,     label=f'{label_model_name} - {inner_loop_label}: {inner_loop_value}' )
																				plt.plot(_lowercase				,     _lowercase				,     """--""" )

														title_str += f' {label_model_name} vs.'

								snake_case_ :			Tuple            =      title_str[:-4]
								snake_case_ :			Union[str, Any]            =      """Time in s""" if self.args.is_time else """Memory in MB"""

								# plot
								plt.title(_lowercase )
								plt.xlabel(_lowercase )
								plt.ylabel(_lowercase )
								plt.legend()

								if self.args.figure_png_file is not None:
														plt.savefig(self.args.figure_png_file )
								else:
														plt.show()




def 						__lowerCAmelCase    (		):


						'''simple docstring'''






						snake_case_ :			List[Any]            =      HfArgumentParser(__UpperCamelCase     )
						snake_case_ :			List[Any]            =      parser.parse_args_into_dataclasses()[0]
						snake_case_ :			Dict            =      Plot(args=__UpperCamelCase     )
						plot.plot()


if __name__ == "__main__":
					main()








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
    )



