






import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__UpperCamelCase      :      List[Any]     						=						logging.getLogger(__name__)


__UpperCamelCase      :      Optional[Any]     						=						list(MODEL_WITH_LM_HEAD_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. Leave None 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': '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'}   ,			)


@dataclass
class        __UpperCamelCase    :
	__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': (
	            'The input training data files (multiple files in glob format). '
	            'Very often splitting large files to smaller files can prevent tokenizer going out of memory'
	        )
	    }   ,			)
	__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 mask in Chinese.'}   ,			)
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'An optional input eval ref data file for whole word mask in Chinese.'}   ,			)
	__snake_case	:bool   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Whether distinct lines of text in the dataset are to be handled as distinct sequences.'}   ,			)

	__snake_case	:bool   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Train with masked-language modeling loss instead of language modeling.'}   )
	__snake_case	:bool   =       field(default=_lowerCAmelCase   ,			metadata={'help': 'Whether ot not to use whole word mask.'}   )
	__snake_case	:float   =       field(
	    default=0.15   ,			metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'}   )
	__snake_case	:float   =       field(
	    default=1 / 6   ,			metadata={
	        'help': (
	            'Ratio of length of a span of masked tokens to surrounding context length for permutation language'
	            ' modeling.'
	        )
	    }   ,			)
	__snake_case	:int   =       field(
	    default=5   ,			metadata={'help': 'Maximum length of a span of masked tokens for permutation language modeling.'}   )

	__snake_case	:int   =       field(
	    default=-1   ,			metadata={
	        'help': (
	            'Optional input sequence length after tokenization.'
	            'The training dataset will be truncated in block of this size for training.'
	            'Default to the model max input length for single sentence inputs (take into account special tokens).'
	        )
	    }   ,			)
	__snake_case	:bool   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Overwrite the cached training and evaluation sets'}   )





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase = False				,  lowerCamelCase = None				,  ):




	'''simple docstring'''







	def _dataset(lowerCamelCase				,  lowerCamelCase=None					):
		if args.line_by_line:
			if ref_path is not None:
				if not args.whole_word_mask or not args.mlm:
					raise ValueError("""You need to set world whole masking and mlm to True for Chinese Whole Word Mask"""					)
				return LineByLineWithRefDataset(
				    tokenizer=lowerCamelCase				,  file_path=lowerCamelCase				,  block_size=args.block_size				,  ref_path=lowerCamelCase				,  )

			return LineByLineTextDataset(tokenizer=lowerCamelCase				,  file_path=lowerCamelCase				,  block_size=args.block_size					)
		else:
			return TextDataset(
			    tokenizer=lowerCamelCase				,  file_path=lowerCamelCase				,  block_size=args.block_size				,  overwrite_cache=args.overwrite_cache				,  cache_dir=lowerCamelCase				,  )

	if evaluate:
		return _dataset(args.eval_data_file				,  args.eval_ref_file					)
	elif args.train_data_files:
		return ConcatDataset([_dataset(lowerCamelCase					) for f in glob(args.train_data_files					)]					)
	else:
		return _dataset(args.train_data_file				,  args.train_ref_file					)





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
	__lowercase       ,						__lowercase       ,						__lowercase             =  parser.parse_args_into_dataclasses()

	if data_args.eval_data_file is None and training_args.do_eval:
		raise ValueError(
		    """Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file """
		    """or remove the --do_eval argument."""					)
	if (
	    os.path.exists(training_args.output_dir					)
	    and os.listdir(training_args.output_dir					)
	    and training_args.do_train
	    and not training_args.overwrite_output_dir
	):
		raise ValueError(
		    F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
		    """ --overwrite_output_dir to overcome."""					)

	# Setup logging
	logging.basicConfig(
	    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""				,  datefmt="""%m/%d/%Y %H:%M:%S"""				,  level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN				,  )
	logger.warning(
	    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""				,  training_args.local_rank				,  training_args.device				,  training_args.n_gpu				,  bool(training_args.local_rank != -1					)				,  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
	set_seed(training_args.seed					)

	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.

	if model_args.config_name:
		__lowercase             =  AutoConfig.from_pretrained(model_args.config_name				,  cache_dir=model_args.cache_dir					)
	elif model_args.model_name_or_path:
		__lowercase             =  AutoConfig.from_pretrained(model_args.model_name_or_path				,  cache_dir=model_args.cache_dir					)
	else:
		__lowercase             =  CONFIG_MAPPING[model_args.model_type]()
		logger.warning("""You are instantiating a new config instance from scratch."""					)

	if model_args.tokenizer_name:
		__lowercase             =  AutoTokenizer.from_pretrained(model_args.tokenizer_name				,  cache_dir=model_args.cache_dir					)
	elif model_args.model_name_or_path:
		__lowercase             =  AutoTokenizer.from_pretrained(model_args.model_name_or_path				,  cache_dir=model_args.cache_dir					)
	else:
		raise ValueError(
		    """You are instantiating a new tokenizer from scratch. This is not supported, but 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             =  AutoModelWithLMHead.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				,  )
	else:
		logger.info("""Training new model from scratch"""					)
		__lowercase             =  AutoModelWithLMHead.from_config(lowerCamelCase					)

	model.resize_token_embeddings(len(lowerCamelCase					)					)

	if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
		raise ValueError(
		    """BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"""
		    """--mlm flag (masked language modeling)."""					)

	if data_args.block_size <= 0:
		__lowercase             =  tokenizer.max_len
		# Our input block size will be the max possible for the model
	else:
		__lowercase             =  min(data_args.block_size				,  tokenizer.max_len					)

	# Get datasets

	__lowercase             =  (
	    get_dataset(lowerCamelCase				,  tokenizer=lowerCamelCase				,  cache_dir=model_args.cache_dir					) if training_args.do_train else None
	)
	__lowercase             =  (
	    get_dataset(lowerCamelCase				,  tokenizer=lowerCamelCase				,  evaluate=lowerCamelCase				,  cache_dir=model_args.cache_dir					)
	    if training_args.do_eval
	    else None
	)
	if config.model_type == "xlnet":
		__lowercase             =  DataCollatorForPermutationLanguageModeling(
		    tokenizer=lowerCamelCase				,  plm_probability=data_args.plm_probability				,  max_span_length=data_args.max_span_length				,  )
	else:
		if data_args.mlm and data_args.whole_word_mask:
			__lowercase             =  DataCollatorForWholeWordMask(
			    tokenizer=lowerCamelCase				,  mlm_probability=data_args.mlm_probability					)
		else:
			__lowercase             =  DataCollatorForLanguageModeling(
			    tokenizer=lowerCamelCase				,  mlm=data_args.mlm				,  mlm_probability=data_args.mlm_probability					)

    # Initialize our Trainer
	__lowercase             =  Trainer(
	    model=lowerCamelCase				,  args=lowerCamelCase				,  data_collator=lowerCamelCase				,  train_dataset=lowerCamelCase				,  eval_dataset=lowerCamelCase				,  prediction_loss_only=lowerCamelCase				,  )

	# Training
	if training_args.do_train:
		__lowercase             =  (
		    model_args.model_name_or_path
		    if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path					)
		    else None
		)
		trainer.train(model_path=lowerCamelCase					)
		trainer.save_model()
		# For convenience, we also re-save the tokenizer to the same directory,
		# so that you can share your model easily on huggingface.co/models =)
		if trainer.is_world_master():
			tokenizer.save_pretrained(training_args.output_dir					)

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

		__lowercase             =  trainer.evaluate()

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

		__lowercase             =  os.path.join(training_args.output_dir				,  """eval_results_lm.txt"""					)
		if trainer.is_world_master():
			with open(lowerCamelCase				,  """w"""					) as writer:
				logger.info("""***** Eval results *****"""					)
				for key in sorted(result.keys()					):
					logger.info("""  %s = %s"""				,  lowerCamelCase				,  str(result[key]					)					)
					writer.write("""%s = %s\n""" % (key, str(result[key]					))					)

		results.update(lowerCamelCase					)

	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
						)

