






import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


__UpperCamelCase      :      Optional[int]     						=						logging.getLogger(__name__)
__UpperCamelCase      :      Optional[int]     						=						list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__UpperCamelCase      :      List[str]     						=						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class        __UpperCamelCase    :
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={
	        'help': (
	            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
	        )
	    }   ,			)
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(_lowerCAmelCase   )}   ,			)
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={
	        'help': (
	            'Override some existing default config settings when a model is trained from scratch. Example: '
	            'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
	        )
	    }   ,			)
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Pretrained config name or path if not the same as model_name'}   )
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}   )
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}   ,			)
	__snake_case	:bool   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}   ,			)
	__snake_case	:str   =       field(
	    default='main'   ,			metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}   ,			)
	__snake_case	:bool   =       field(
	    default=_lowerCAmelCase   ,			metadata={
	        'help': (
	            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
	            'with private models).'
	        )
	    }   ,			)







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







		if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
			raise ValueError(
			    """--config_overrides can't be used in combination with --config_name or --model_name_or_path"""			)


@dataclass
class        __UpperCamelCase    :
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'The name of the dataset to use (via the datasets library).'}   )
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}   )
	__snake_case	:Optional[str]   =       field(default=_lowerCAmelCase   ,			metadata={'help': 'The input training data file (a text file).'}   )
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}   ,			)
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'}   ,			)
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'}   ,			)
	__snake_case	:bool   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Overwrite the cached training and evaluation sets'}   )
	__snake_case	:Optional[int]   =       field(
	    default=5   ,			metadata={
	        'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
	    }   ,			)
	__snake_case	:Optional[int]   =       field(
	    default=_lowerCAmelCase   ,			metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated. Default to the max input length of the model.'
	        )
	    }   ,			)
	__snake_case	:Optional[int]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'The number of processes to use for the preprocessing.'}   ,			)
	__snake_case	:float   =       field(
	    default=0.15   ,			metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'}   )
	__snake_case	:bool   =       field(
	    default=_lowerCAmelCase   ,			metadata={
	        'help': (
	            'Whether to pad all samples to `max_seq_length`. '
	            'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
	        )
	    }   ,			)







	def  _a   (  self      :							str			)		-> int:
		"""simple docstring"""







		if self.train_file is not None:
			__lowercase             =  self.train_file.split("""."""			)[-1]
			assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
		if self.validation_file is not None:
			__lowercase             =  self.validation_file.split("""."""			)[-1]
			assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	with open(lowerCamelCase				,  """r"""				,  encoding="""utf-8"""					) as f:
		__lowercase             =  [json.loads(lowerCamelCase					) for line in f.read().splitlines() if (len(lowerCamelCase					) > 0 and not line.isspace())]
	assert len(lowerCamelCase					) == len(lowerCamelCase					)

	__lowercase             =  {c: dataset[c] for c in dataset.column_names}
	__lowercase             =  refs
	return Dataset.from_dict(lowerCamelCase					)





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
	if len(sys.argv					) == 2 and sys.argv[1].endswith(""".json"""					):
		# If we pass only one argument to the script and it's the path to a json file,
		# let's parse it to get our arguments.
		__lowercase       ,						__lowercase       ,						__lowercase             =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
	else:
		__lowercase       ,						__lowercase       ,						__lowercase             =  parser.parse_args_into_dataclasses()

	# Detecting last checkpoint.
	__lowercase             =  None
	if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
		__lowercase             =  get_last_checkpoint(training_args.output_dir					)
		if last_checkpoint is None and len(os.listdir(training_args.output_dir					)					) > 0:
			raise ValueError(
			    F'Output directory ({training_args.output_dir}) already exists and is not empty. '
			    """Use --overwrite_output_dir to overcome."""					)
		elif last_checkpoint is not None:
			logger.info(
			    F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
			    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""					)

    # Setup logging
	logging.basicConfig(
	    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""				,  datefmt="""%m/%d/%Y %H:%M:%S"""				,  handlers=[logging.StreamHandler(sys.stdout					)]				,  )
	logger.setLevel(logging.INFO if is_main_process(training_args.local_rank					) else logging.WARN					)

	# Log on each process the small summary:
	logger.warning(
	    F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
	    + F'distributed training: {bool(training_args.local_rank != -1					)}, 16-bits training: {training_args.fpaa}'					)
	# Set the verbosity to info of the Transformers logger (on main process only):
	if is_main_process(training_args.local_rank					):
		transformers.utils.logging.set_verbosity_info()
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()
	logger.info("""Training/evaluation parameters %s"""				,  lowerCamelCase					)

	# Set seed before initializing model.
	set_seed(training_args.seed					)

	# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
	# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
	# (the dataset will be downloaded automatically from the datasets Hub).
	#
	# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
	# 'text' is found. You can easily tweak this behavior (see below).
	#
	# In distributed training, the load_dataset function guarantee that only one local process can concurrently
	# download the dataset.
	if data_args.dataset_name is not None:
		# Downloading and loading a dataset from the hub.
		__lowercase             =  load_dataset(data_args.dataset_name				,  data_args.dataset_config_name					)
		if "validation" not in datasets.keys():
			__lowercase             =  load_dataset(
			    data_args.dataset_name				,  data_args.dataset_config_name				,  split=F'train[:{data_args.validation_split_percentage}%]'				,  )
			__lowercase             =  load_dataset(
			    data_args.dataset_name				,  data_args.dataset_config_name				,  split=F'train[{data_args.validation_split_percentage}%:]'				,  )
	else:
		__lowercase             =  {}
		if data_args.train_file is not None:
			__lowercase             =  data_args.train_file
		if data_args.validation_file is not None:
			__lowercase             =  data_args.validation_file
		__lowercase             =  data_args.train_file.split("""."""					)[-1]
		if extension == "txt":
			__lowercase             =  """text"""
		__lowercase             =  load_dataset(lowerCamelCase				,  data_files=lowerCamelCase					)
	# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
	# https://huggingface.co/docs/datasets/loading_datasets.html.

	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.
	__lowercase             =  {
	    """cache_dir""": model_args.cache_dir,
	    """revision""": model_args.model_revision,
	    """use_auth_token""": True if model_args.use_auth_token else None,
	}
	if model_args.config_name:
		__lowercase             =  AutoConfig.from_pretrained(model_args.config_name				,  **lowerCamelCase					)
	elif model_args.model_name_or_path:
		__lowercase             =  AutoConfig.from_pretrained(model_args.model_name_or_path				,  **lowerCamelCase					)
	else:
		__lowercase             =  CONFIG_MAPPING[model_args.model_type]()
		logger.warning("""You are instantiating a new config instance from scratch."""					)
		if model_args.config_overrides is not None:
			logger.info(F'Overriding config: {model_args.config_overrides}'					)
			config.update_from_string(model_args.config_overrides					)
			logger.info(F'New config: {config}'					)

	__lowercase             =  {
	    """cache_dir""": model_args.cache_dir,
	    """use_fast""": model_args.use_fast_tokenizer,
	    """revision""": model_args.model_revision,
	    """use_auth_token""": True if model_args.use_auth_token else None,
	}
	if model_args.tokenizer_name:
		__lowercase             =  AutoTokenizer.from_pretrained(model_args.tokenizer_name				,  **lowerCamelCase					)
	elif model_args.model_name_or_path:
		__lowercase             =  AutoTokenizer.from_pretrained(model_args.model_name_or_path				,  **lowerCamelCase					)
	else:
		raise ValueError(
		    """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
		    """You can do it from another script, save it, and load it from here, using --tokenizer_name."""					)

	if model_args.model_name_or_path:
		__lowercase             =  AutoModelForMaskedLM.from_pretrained(
		    model_args.model_name_or_path				,  from_tf=bool(""".ckpt""" in model_args.model_name_or_path					)				,  config=lowerCamelCase				,  cache_dir=model_args.cache_dir				,  revision=model_args.model_revision				,  use_auth_token=True if model_args.use_auth_token else None				,  )
	else:
		logger.info("""Training new model from scratch"""					)
		__lowercase             =  AutoModelForMaskedLM.from_config(lowerCamelCase					)

	model.resize_token_embeddings(len(lowerCamelCase					)					)

	# Preprocessing the datasets.
	# First we tokenize all the texts.
	if training_args.do_train:
		__lowercase             =  datasets["""train"""].column_names
	else:
		__lowercase             =  datasets["""validation"""].column_names
	__lowercase             =  """text""" if """text""" in column_names else column_names[0]

	__lowercase             =  """max_length""" if data_args.pad_to_max_length else False

	def tokenize_function(lowerCamelCase					):
		# Remove empty lines
		__lowercase             =  [line for line in examples["""text"""] if len(lowerCamelCase					) > 0 and not line.isspace()]
		return tokenizer(examples["""text"""]				,  padding=lowerCamelCase				,  truncation=lowerCamelCase				,  max_length=data_args.max_seq_length					)

	__lowercase             =  datasets.map(
	    lowerCamelCase				,  batched=lowerCamelCase				,  num_proc=data_args.preprocessing_num_workers				,  remove_columns=[text_column_name]				,  load_from_cache_file=not data_args.overwrite_cache				,  )

	# Add the chinese references if provided
	if data_args.train_ref_file is not None:
		__lowercase             =  add_chinese_references(tokenized_datasets["""train"""]				,  data_args.train_ref_file					)
	if data_args.validation_ref_file is not None:
		__lowercase             =  add_chinese_references(
		    tokenized_datasets["""validation"""]				,  data_args.validation_ref_file					)
	# If we have ref files, need to avoid it removed by trainer
	__lowercase             =  data_args.train_ref_file or data_args.validation_ref_file
	if has_ref:
		__lowercase             =  False

	# Data collator
	# This one will take care of randomly masking the tokens.
	__lowercase             =  DataCollatorForWholeWordMask(tokenizer=lowerCamelCase				,  mlm_probability=data_args.mlm_probability					)

	# Initialize our Trainer
	__lowercase             =  Trainer(
	    model=lowerCamelCase				,  args=lowerCamelCase				,  train_dataset=tokenized_datasets["""train"""] if training_args.do_train else None				,  eval_dataset=tokenized_datasets["""validation"""] if training_args.do_eval else None				,  tokenizer=lowerCamelCase				,  data_collator=lowerCamelCase				,  )

	# Training
	if training_args.do_train:
		if last_checkpoint is not None:
			__lowercase             =  last_checkpoint
		elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path					):
			__lowercase             =  model_args.model_name_or_path
		else:
			__lowercase             =  None
		__lowercase             =  trainer.train(resume_from_checkpoint=lowerCamelCase					)
		trainer.save_model()  # Saves the tokenizer too for easy upload

		__lowercase             =  os.path.join(training_args.output_dir				,  """train_results.txt"""					)
		if trainer.is_world_process_zero():
			with open(lowerCamelCase				,  """w"""					) as writer:
				logger.info("""***** Train results *****"""					)
				for key, value in sorted(train_result.metrics.items()					):
					logger.info(F'  {key} = {value}'					)
					writer.write(F'{key} = {value}\n'					)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
			trainer.state.save_to_json(os.path.join(training_args.output_dir				,  """trainer_state.json"""					)					)

    # Evaluation
	__lowercase             =  {}
	if training_args.do_eval:
		logger.info("""*** Evaluate ***"""					)

		__lowercase             =  trainer.evaluate()

		__lowercase             =  math.exp(eval_output["""eval_loss"""]					)
		__lowercase             =  perplexity

		__lowercase             =  os.path.join(training_args.output_dir				,  """eval_results_mlm_wwm.txt"""					)
		if trainer.is_world_process_zero():
			with open(lowerCamelCase				,  """w"""					) as writer:
				logger.info("""***** Eval results *****"""					)
				for key, value in sorted(results.items()					):
					logger.info(F'  {key} = {value}'					)
					writer.write(F'{key} = {value}\n'					)

	return results





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	main()


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      :      Optional[int]     						=						logging.get_logger(__name__)





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







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

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

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

	return config





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  []
	# 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 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  dct.pop(lowerCamelCase					)
	__lowercase             =  val





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







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





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







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





def 				snake_case  (     ):




	'''simple docstring'''







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





@torch.no_grad()
def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase = False					):




	'''simple docstring'''







	__lowercase             =  get_maskformer_config(lowerCamelCase					)

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

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

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

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

	# load 🤗 model
	__lowercase             =  MaskFormerForInstanceSegmentation(lowerCamelCase					)
	model.eval()

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

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

	# verify results
	__lowercase             =  prepare_img()
	if "vistas" in model_name:
		__lowercase             =  65
	elif "cityscapes" in model_name:
		__lowercase             =  65_535
	else:
		__lowercase             =  255
	__lowercase             =  True if """ade""" in model_name else False
	__lowercase             =  MaskFormerImageProcessor(ignore_index=lowerCamelCase				,  reduce_labels=lowerCamelCase					)

	__lowercase             =  image_processor(lowerCamelCase				,  return_tensors="""pt"""					)

	__lowercase             =  model(**lowerCamelCase					)

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

	if model_name == "maskformer-swin-tiny-ade":
		__lowercase             =  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]				,  lowerCamelCase				,  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(lowerCamelCase					).mkdir(exist_ok=lowerCamelCase					)
		model.save_pretrained(lowerCamelCase					)
		image_processor.save_pretrained(lowerCamelCase					)

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

