





import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


_lowerCamelCase 					=      {
    'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
    'gpt2': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]     ):
		assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
		assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
		if args.mlm:
				assert os.path.isfile(args.token_counts     )
				assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
		else:
				assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

		assert args.teacher_type == args.student_type or (
		    args.student_type == "distilbert" and args.teacher_type == "bert"
		)
		assert os.path.isfile(args.student_config     )
		if args.student_pretrained_weights is not None:
				assert os.path.isfile(args.student_pretrained_weights     )

		if args.freeze_token_type_embds:
				assert args.student_type in ["roberta"]

		assert args.alpha_ce >= 0.0
		assert args.alpha_mlm >= 0.0
		assert args.alpha_clm >= 0.0
		assert args.alpha_mse >= 0.0
		assert args.alpha_cos >= 0.0
		assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[str]       , UpperCamelCase__:				int     ):
		if args.student_type == "roberta":
				SCREAMING_SNAKE_CASE__								=   False
		elif args.student_type == "gpt2":
				SCREAMING_SNAKE_CASE__								=   False






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[Any]       , UpperCamelCase__:				int     ):
		if args.student_type == "roberta":
				SCREAMING_SNAKE_CASE__								=   False






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   argparse.ArgumentParser(description="""Training"""     )
		parser.add_argument("""--force"""       , action="""store_true"""       , help="""Overwrite dump_path if it already exists."""     )

		parser.add_argument(
		    """--dump_path"""       , type=UpperCamelCase__       , required=UpperCamelCase__       , help="""The output directory (log, checkpoints, parameters, etc.)"""     )
		parser.add_argument(
		    """--data_file"""       , type=UpperCamelCase__       , required=UpperCamelCase__       , help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence."""       , )

		parser.add_argument(
		    """--student_type"""       , type=UpperCamelCase__       , choices=["""distilbert""", """roberta""", """gpt2"""]       , required=UpperCamelCase__       , help="""The student type (DistilBERT, RoBERTa)."""       , )
		parser.add_argument("""--student_config"""       , type=UpperCamelCase__       , required=UpperCamelCase__       , help="""Path to the student configuration."""     )
		parser.add_argument(
		    """--student_pretrained_weights"""       , default=UpperCamelCase__       , type=UpperCamelCase__       , help="""Load student initialization checkpoint."""     )

		parser.add_argument(
		    """--teacher_type"""       , choices=["""bert""", """roberta""", """gpt2"""]       , required=UpperCamelCase__       , help="""Teacher type (BERT, RoBERTa)."""     )
		parser.add_argument("""--teacher_name"""       , type=UpperCamelCase__       , required=UpperCamelCase__       , help="""The teacher model."""     )

		parser.add_argument("""--temperature"""       , default=2.0       , type=UpperCamelCase__       , help="""Temperature for the softmax temperature."""     )
		parser.add_argument(
		    """--alpha_ce"""       , default=0.5       , type=UpperCamelCase__       , help="""Linear weight for the distillation loss. Must be >=0."""     )
		parser.add_argument(
		    """--alpha_mlm"""       , default=0.0       , type=UpperCamelCase__       , help="""Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."""       , )
		parser.add_argument("""--alpha_clm"""       , default=0.5       , type=UpperCamelCase__       , help="""Linear weight for the CLM loss. Must be >=0."""     )
		parser.add_argument("""--alpha_mse"""       , default=0.0       , type=UpperCamelCase__       , help="""Linear weight of the MSE loss. Must be >=0."""     )
		parser.add_argument(
		    """--alpha_cos"""       , default=0.0       , type=UpperCamelCase__       , help="""Linear weight of the cosine embedding loss. Must be >=0."""     )

		parser.add_argument(
		    """--mlm"""       , action="""store_true"""       , help="""The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."""     )
		parser.add_argument(
		    """--mlm_mask_prop"""       , default=0.1_5       , type=UpperCamelCase__       , help="""Proportion of tokens for which we need to make a prediction."""       , )
		parser.add_argument("""--word_mask"""       , default=0.8       , type=UpperCamelCase__       , help="""Proportion of tokens to mask out."""     )
		parser.add_argument("""--word_keep"""       , default=0.1       , type=UpperCamelCase__       , help="""Proportion of tokens to keep."""     )
		parser.add_argument("""--word_rand"""       , default=0.1       , type=UpperCamelCase__       , help="""Proportion of tokens to randomly replace."""     )
		parser.add_argument(
		    """--mlm_smoothing"""       , default=0.7       , type=UpperCamelCase__       , help="""Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."""       , )
		parser.add_argument("""--token_counts"""       , type=UpperCamelCase__       , help="""The token counts in the data_file for MLM."""     )

		parser.add_argument(
		    """--restrict_ce_to_mask"""       , action="""store_true"""       , help="""If true, compute the distillation loss only the [MLM] prediction distribution."""       , )
		parser.add_argument(
		    """--freeze_pos_embs"""       , action="""store_true"""       , help="""Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only."""       , )
		parser.add_argument(
		    """--freeze_token_type_embds"""       , action="""store_true"""       , help="""Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only."""       , )

		parser.add_argument("""--n_epoch"""       , type=UpperCamelCase__       , default=3       , help="""Number of pass on the whole dataset."""     )
		parser.add_argument("""--batch_size"""       , type=UpperCamelCase__       , default=5       , help="""Batch size (for each process)."""     )
		parser.add_argument(
		    """--group_by_size"""       , action="""store_false"""       , help="""If true, group sequences that have similar length into the same batch. Default is true."""       , )

		parser.add_argument(
		    """--gradient_accumulation_steps"""       , type=UpperCamelCase__       , default=50       , help="""Gradient accumulation for larger training batches."""       , )
		parser.add_argument("""--warmup_prop"""       , default=0.0_5       , type=UpperCamelCase__       , help="""Linear warmup proportion."""     )
		parser.add_argument("""--weight_decay"""       , default=0.0       , type=UpperCamelCase__       , help="""Weight decay if we apply some."""     )
		parser.add_argument("""--learning_rate"""       , default=5e-4       , type=UpperCamelCase__       , help="""The initial learning rate for Adam."""     )
		parser.add_argument("""--adam_epsilon"""       , default=1e-6       , type=UpperCamelCase__       , help="""Epsilon for Adam optimizer."""     )
		parser.add_argument("""--max_grad_norm"""       , default=5.0       , type=UpperCamelCase__       , help="""Max gradient norm."""     )
		parser.add_argument("""--initializer_range"""       , default=0.0_2       , type=UpperCamelCase__       , help="""Random initialization range."""     )

		parser.add_argument(
		    """--fp16"""       , action="""store_true"""       , help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""       , )
		parser.add_argument(
		    """--fp16_opt_level"""       , type=UpperCamelCase__       , default="""O1"""       , help=(
		        """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
		        """See details at https://nvidia.github.io/apex/amp.html"""
		    )       , )
		parser.add_argument("""--n_gpu"""       , type=UpperCamelCase__       , default=1       , help="""Number of GPUs in the node."""     )
		parser.add_argument("""--local_rank"""       , type=UpperCamelCase__       , default=-1       , help="""Distributed training - Local rank"""     )
		parser.add_argument("""--seed"""       , type=UpperCamelCase__       , default=56       , help="""Random seed"""     )

		parser.add_argument("""--log_interval"""       , type=UpperCamelCase__       , default=500       , help="""Tensorboard logging interval."""     )
		parser.add_argument("""--checkpoint_interval"""       , type=UpperCamelCase__       , default=4_000       , help="""Checkpoint interval."""     )
		SCREAMING_SNAKE_CASE__								=   parser.parse_args()
		sanity_checks(UpperCamelCase__     )

		# ARGS #
		init_gpu_params(UpperCamelCase__     )
		set_seed(UpperCamelCase__     )
		if args.is_master:
				if os.path.exists(args.dump_path     ):
						if not args.force:
								raise ValueError(
								    f'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
								    """ itUse `--force` if you want to overwrite it"""     )
						else:
								shutil.rmtree(args.dump_path     )

				if not os.path.exists(args.dump_path     ):
						os.makedirs(args.dump_path     )
				logger.info(f'''Experiment will be dumped and logged in {args.dump_path}'''     )

				# SAVE PARAMS #
				logger.info(f'''Param: {args}'''     )
				with open(os.path.join(args.dump_path       , """parameters.json"""     )       , """w"""     ) as f:
						json.dump(vars(UpperCamelCase__     )       , UpperCamelCase__       , indent=4     )
				git_log(args.dump_path     )

		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   MODEL_CLASSES[args.student_type]
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   MODEL_CLASSES[args.teacher_type]

		# TOKENIZER #
		SCREAMING_SNAKE_CASE__								=   teacher_tokenizer_class.from_pretrained(args.teacher_name     )
		SCREAMING_SNAKE_CASE__								=   {}
		for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
				SCREAMING_SNAKE_CASE__								=   tokenizer.all_special_tokens.index(UpperCamelCase__     )
				SCREAMING_SNAKE_CASE__								=   tokenizer.all_special_ids[idx]
		logger.info(f'''Special tokens {special_tok_ids}'''     )
		SCREAMING_SNAKE_CASE__								=   special_tok_ids
		SCREAMING_SNAKE_CASE__								=   tokenizer.max_model_input_sizes[args.teacher_name]

		# DATA LOADER #
		logger.info(f'''Loading data from {args.data_file}'''     )
		with open(args.data_file       , """rb"""     ) as fp:
				SCREAMING_SNAKE_CASE__								=   pickle.load(UpperCamelCase__     )

		if args.mlm:
				logger.info(f'''Loading token counts from {args.token_counts} (already pre-computed)'''     )
				with open(args.token_counts       , """rb"""     ) as fp:
						SCREAMING_SNAKE_CASE__								=   pickle.load(UpperCamelCase__     )

				SCREAMING_SNAKE_CASE__								=   np.maximum(UpperCamelCase__       , 1     ) ** -args.mlm_smoothing
				for idx in special_tok_ids.values():
						SCREAMING_SNAKE_CASE__								=   0.0  # do not predict special tokens
				SCREAMING_SNAKE_CASE__								=   torch.from_numpy(UpperCamelCase__     )
		else:
				SCREAMING_SNAKE_CASE__								=   None

		SCREAMING_SNAKE_CASE__								=   LmSeqsDataset(params=UpperCamelCase__       , data=UpperCamelCase__     )
		logger.info("""Data loader created."""     )

		# STUDENT #
		logger.info(f'''Loading student config from {args.student_config}'''     )
		SCREAMING_SNAKE_CASE__								=   student_config_class.from_pretrained(args.student_config     )
		SCREAMING_SNAKE_CASE__								=   True

		if args.student_pretrained_weights is not None:
				logger.info(f'''Loading pretrained weights from {args.student_pretrained_weights}'''     )
				SCREAMING_SNAKE_CASE__								=   student_model_class.from_pretrained(args.student_pretrained_weights       , config=UpperCamelCase__     )
		else:
				SCREAMING_SNAKE_CASE__								=   student_model_class(UpperCamelCase__     )

		if args.n_gpu > 0:
				student.to(f'''cuda:{args.local_rank}'''     )
		logger.info("""Student loaded."""     )

		# TEACHER #
		SCREAMING_SNAKE_CASE__								=   teacher_model_class.from_pretrained(args.teacher_name       , output_hidden_states=UpperCamelCase__     )
		if args.n_gpu > 0:
				teacher.to(f'''cuda:{args.local_rank}'''     )
		logger.info(f'''Teacher loaded from {args.teacher_name}.'''     )

		# FREEZING #
		if args.freeze_pos_embs:
				freeze_pos_embeddings(UpperCamelCase__       , UpperCamelCase__     )
		if args.freeze_token_type_embds:
				freeze_token_type_embeddings(UpperCamelCase__       , UpperCamelCase__     )

		# SANITY CHECKS #
		assert student.config.vocab_size == teacher.config.vocab_size
		assert student.config.hidden_size == teacher.config.hidden_size
		assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
		if args.mlm:
				assert token_probs.size(0     ) == stu_architecture_config.vocab_size

		# DISTILLER #
		torch.cuda.empty_cache()
		SCREAMING_SNAKE_CASE__								=   Distiller(
		    params=UpperCamelCase__       , dataset=UpperCamelCase__       , token_probs=UpperCamelCase__       , student=UpperCamelCase__       , teacher=UpperCamelCase__     )
		distiller.train()
		logger.info("""Let's go get some drinks."""     )


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()
_lowerCamelCase 					=      logging.get_logger(__name__)






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

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

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

		return config






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]     ):
		SCREAMING_SNAKE_CASE__								=   []
		# stem
		# fmt: off
		rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""")     )
		rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""")     )
		rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""")     )
		rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias""")     )
		# stages
		for i in range(len(config.backbone_config.depths     )     ):
				for j in range(config.backbone_config.depths[i]     ):
						rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm1.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')     )
						rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm1.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')     )
						rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')     )
						rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')     )
						rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')     )
						rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')     )
						rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm2.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')     )
						rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm2.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')     )
						rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')     )
						rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')     )
						rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')     )
						rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')     )

				if i < 3:
						rename_keys.append((f'''backbone.layers.{i}.downsample.reduction.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''')     )
						rename_keys.append((f'''backbone.layers.{i}.downsample.norm.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''')     )
						rename_keys.append((f'''backbone.layers.{i}.downsample.norm.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''')     )
				rename_keys.append((f'''backbone.norm{i}.weight''', f'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''')     )
				rename_keys.append((f'''backbone.norm{i}.bias''', f'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''')     )

		# FPN
		rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""")     )
		rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""")     )
		rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""")     )
		for source_index, target_index in zip(range(3       , 0       , -1     )       , range(0       , 3     )     ):
				rename_keys.append((f'''sem_seg_head.adapter_{source_index}.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''')     )
				rename_keys.append((f'''sem_seg_head.adapter_{source_index}.norm.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''')     )
				rename_keys.append((f'''sem_seg_head.adapter_{source_index}.norm.bias''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''')     )
				rename_keys.append((f'''sem_seg_head.layer_{source_index}.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''')     )
				rename_keys.append((f'''sem_seg_head.layer_{source_index}.norm.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''')     )
				rename_keys.append((f'''sem_seg_head.layer_{source_index}.norm.bias''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''')     )
		rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""")     )
		rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""")     )

		# Transformer decoder
		for idx in range(config.decoder_config.decoder_layers     ):
				# self-attention out projection
				rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', f'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''')     )
				rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', f'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''')     )
				# cross-attention out projection
				rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''')     )
				rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''')     )
				# MLP 1
				rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', f'''model.transformer_module.decoder.layers.{idx}.fc1.weight''')     )
				rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', f'''model.transformer_module.decoder.layers.{idx}.fc1.bias''')     )
				# MLP 2
				rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', f'''model.transformer_module.decoder.layers.{idx}.fc2.weight''')     )
				rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', f'''model.transformer_module.decoder.layers.{idx}.fc2.bias''')     )
				# layernorm 1 (self-attention layernorm)
				rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', f'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''')     )
				rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', f'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''')     )
				# layernorm 2 (cross-attention layernorm)
				rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''')     )
				rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''')     )
				# layernorm 3 (final layernorm)
				rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', f'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''')     )
				rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', f'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''')     )

		rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""")     )
		rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""")     )

		# heads on top
		rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""")     )

		rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""")     )
		rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""")     )

		rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""")     )
		rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""")     )

		for i in range(3     ):
				rename_keys.append((f'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', f'''mask_embedder.{i}.0.weight''')     )
				rename_keys.append((f'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', f'''mask_embedder.{i}.0.bias''')     )
		# fmt: on

		return rename_keys






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int       , UpperCamelCase__:				Optional[int]       , UpperCamelCase__:				Optional[int]     ):
		SCREAMING_SNAKE_CASE__								=   dct.pop(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   val






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






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int       , UpperCamelCase__:				Optional[Any]     ):
		# fmt: off
		SCREAMING_SNAKE_CASE__								=   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)
				SCREAMING_SNAKE_CASE__								=   state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''     )
				SCREAMING_SNAKE_CASE__								=   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
				SCREAMING_SNAKE_CASE__								=   in_proj_weight[: hidden_size, :]
				SCREAMING_SNAKE_CASE__								=   in_proj_bias[:config.hidden_size]
				SCREAMING_SNAKE_CASE__								=   in_proj_weight[hidden_size : hidden_size * 2, :]
				SCREAMING_SNAKE_CASE__								=   in_proj_bias[hidden_size : hidden_size * 2]
				SCREAMING_SNAKE_CASE__								=   in_proj_weight[-hidden_size :, :]
				SCREAMING_SNAKE_CASE__								=   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)
				SCREAMING_SNAKE_CASE__								=   state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''     )
				SCREAMING_SNAKE_CASE__								=   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
				SCREAMING_SNAKE_CASE__								=   in_proj_weight[: hidden_size, :]
				SCREAMING_SNAKE_CASE__								=   in_proj_bias[:config.hidden_size]
				SCREAMING_SNAKE_CASE__								=   in_proj_weight[hidden_size : hidden_size * 2, :]
				SCREAMING_SNAKE_CASE__								=   in_proj_bias[hidden_size : hidden_size * 2]
				SCREAMING_SNAKE_CASE__								=   in_proj_weight[-hidden_size :, :]
				SCREAMING_SNAKE_CASE__								=   in_proj_bias[-hidden_size :]
		# fmt: on






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






@torch.no_grad()
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str       , UpperCamelCase__:				str       , UpperCamelCase__:				str       , UpperCamelCase__:				bool = False     ):
		SCREAMING_SNAKE_CASE__								=   get_maskformer_config(UpperCamelCase__     )

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

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

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

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

		# load 🤗 model
		SCREAMING_SNAKE_CASE__								=   MaskFormerForInstanceSegmentation(UpperCamelCase__     )
		model.eval()

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

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

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

		SCREAMING_SNAKE_CASE__								=   image_processor(UpperCamelCase__       , return_tensors="""pt"""     )

		SCREAMING_SNAKE_CASE__								=   model(**UpperCamelCase__     )

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

		if model_name == "maskformer-swin-tiny-ade":
				SCREAMING_SNAKE_CASE__								=   torch.tensor(
				    [[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]]     )
		assert torch.allclose(outputs.class_queries_logits[0, :3, :3]       , UpperCamelCase__       , 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(UpperCamelCase__     ).mkdir(exist_ok=UpperCamelCase__     )
				model.save_pretrained(UpperCamelCase__     )
				image_processor.save_pretrained(UpperCamelCase__     )

		if push_to_hub:
				print("""Pushing model and image processor to the hub..."""     )
				model.push_to_hub(f'''nielsr/{model_name}'''     )
				image_processor.push_to_hub(f'''nielsr/{model_name}'''     )


if __name__ == "__main__":
	_lowerCamelCase 					=      argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--model_name',
	    default='maskformer-swin-tiny-ade',
	    type=str,
	    help=('Name of the MaskFormer model you\'d like to convert',),
	)
	parser.add_argument(
	    '--checkpoint_path',
	    default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
	    type=str,
	    help='Path to the original state dict (.pth file).',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)
	parser.add_argument(
	    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
	)

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