

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()
a		=logging.get_logger(__name__)






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     Dict:
	__lowerCamelCase				:				List[str]					       = SwinConfig.from_pretrained(
	    'microsoft/swin-tiny-patch4-window7-224'	, out_features=['stage1', 'stage2', 'stage3', 'stage4']			)
	__lowerCamelCase				:				Any					       = MaskFormerConfig(backbone_config=lowerCamelCase__			)

	__lowerCamelCase				:				Tuple					       = 'huggingface/label-files'
	if "ade20k-full" in model_name:
		# this should be ok
		__lowerCamelCase				:				int					       = 8_4_7
		__lowerCamelCase				:				str					       = 'maskformer-ade20k-full-id2label.json'
	elif "ade" in model_name:
		# this should be ok
		__lowerCamelCase				:				Tuple					       = 1_5_0
		__lowerCamelCase				:				Any					       = 'ade20k-id2label.json'
	elif "coco-stuff" in model_name:
		# this should be ok
		__lowerCamelCase				:				Union[str, Any]					       = 1_7_1
		__lowerCamelCase				:				Union[str, Any]					       = 'maskformer-coco-stuff-id2label.json'
	elif "coco" in model_name:
		# TODO
		__lowerCamelCase				:				Dict					       = 1_3_3
		__lowerCamelCase				:				str					       = 'coco-panoptic-id2label.json'
	elif "cityscapes" in model_name:
		# this should be ok
		__lowerCamelCase				:				str					       = 1_9
		__lowerCamelCase				:				Union[str, Any]					       = 'cityscapes-id2label.json'
	elif "vistas" in model_name:
		# this should be ok
		__lowerCamelCase				:				str					       = 6_5
		__lowerCamelCase				:				Union[str, Any]					       = 'mapillary-vistas-id2label.json'

	__lowerCamelCase				:				Tuple					       = json.load(open(hf_hub_download(lowerCamelCase__	, lowerCamelCase__	, repo_type='dataset'			)	, 'r'			)			)
	__lowerCamelCase				:				List[str]					       = {int(lowerCamelCase__			): v for k, v in idalabel.items()}

	return config






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

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

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

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

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

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

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

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

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

	return rename_keys






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__			)  ->     str:
	__lowerCamelCase				:				str					       = dct.pop(lowerCamelCase__			)
	__lowerCamelCase				:				List[Any]					       = val






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__			)  ->     Optional[int]:
	__lowerCamelCase				:				Any					       = [int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
	for i in range(len(backbone_config.depths			)			):
		__lowerCamelCase				:				List[Any]					       = num_features[i]
		for j in range(backbone_config.depths[i]			):
			# fmt: off
			# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
			__lowerCamelCase				:				Optional[int]					       = state_dict.pop(F"backbone.layers.{i}.blocks.{j}.attn.qkv.weight"			)
			__lowerCamelCase				:				List[Any]					       = state_dict.pop(F"backbone.layers.{i}.blocks.{j}.attn.qkv.bias"			)
			# next, add query, keys and values (in that order) to the state dict
			__lowerCamelCase				:				str					       = in_proj_weight[:dim, :]
			__lowerCamelCase				:				Optional[Any]					       = in_proj_bias[: dim]
			__lowerCamelCase				:				Optional[Any]					       = in_proj_weight[
			    dim : dim * 2, :
			]
			__lowerCamelCase				:				Union[str, Any]					       = in_proj_bias[
			    dim : dim * 2
			]
			__lowerCamelCase				:				str					       = in_proj_weight[
			    -dim :, :
			]
			__lowerCamelCase				:				int					       = in_proj_bias[-dim :]
			# fmt: on






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__			)  ->     Tuple:
	# fmt: off
	__lowerCamelCase				:				Tuple					       = 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)
		__lowerCamelCase				:				List[str]					       = state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"			)
		__lowerCamelCase				:				int					       = 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
		__lowerCamelCase				:				Union[str, Any]					       = in_proj_weight[: hidden_size, :]
		__lowerCamelCase				:				str					       = in_proj_bias[:config.hidden_size]
		__lowerCamelCase				:				List[str]					       = in_proj_weight[hidden_size : hidden_size * 2, :]
		__lowerCamelCase				:				Dict					       = in_proj_bias[hidden_size : hidden_size * 2]
		__lowerCamelCase				:				List[str]					       = in_proj_weight[-hidden_size :, :]
		__lowerCamelCase				:				List[Any]					       = in_proj_bias[-hidden_size :]
		# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
		__lowerCamelCase				:				Optional[Any]					       = state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"			)
		__lowerCamelCase				:				List[str]					       = state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"			)
		# next, add query, keys and values (in that order) to the state dict
		__lowerCamelCase				:				List[Any]					       = in_proj_weight[: hidden_size, :]
		__lowerCamelCase				:				Tuple					       = in_proj_bias[:config.hidden_size]
		__lowerCamelCase				:				Any					       = in_proj_weight[hidden_size : hidden_size * 2, :]
		__lowerCamelCase				:				List[Any]					       = in_proj_bias[hidden_size : hidden_size * 2]
		__lowerCamelCase				:				str					       = in_proj_weight[-hidden_size :, :]
		__lowerCamelCase				:				List[Any]					       = in_proj_bias[-hidden_size :]
	# fmt: on






def    SCREAMING_SNAKE_CASE__       (  )  ->     List[Any]:
	__lowerCamelCase				:				Dict					       = 'http://images.cocodataset.org/val2017/000000039769.jpg'
	__lowerCamelCase				:				List[Any]					       = Image.open(requests.get(lowerCamelCase__	, stream=lowerCamelCase__			).raw			)
	return im






@torch.no_grad()
def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__ = False			)  ->     int:
	__lowerCamelCase				:				Union[str, Any]					       = get_maskformer_config(lowerCamelCase__			)

	# load original state_dict
	with open(lowerCamelCase__	, 'rb'			) as f:
		__lowerCamelCase				:				Union[str, Any]					       = pickle.load(lowerCamelCase__			)
	__lowerCamelCase				:				Any					       = data['model']

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

	# rename keys
	__lowerCamelCase				:				Any					       = 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():
		__lowerCamelCase				:				List[str]					       = torch.from_numpy(lowerCamelCase__			)

	# load 🤗 model
	__lowerCamelCase				:				int					       = MaskFormerForInstanceSegmentation(lowerCamelCase__			)
	model.eval()

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

	__lowerCamelCase       , __lowerCamelCase				:				int					       = 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
	__lowerCamelCase				:				Union[str, Any]					       = prepare_img()
	if "vistas" in model_name:
		__lowerCamelCase				:				Dict					       = 6_5
	elif "cityscapes" in model_name:
		__lowerCamelCase				:				Any					       = 6_5_5_3_5
	else:
		__lowerCamelCase				:				Union[str, Any]					       = 2_5_5
	__lowerCamelCase				:				Any					       = True if 'ade' in model_name else False
	__lowerCamelCase				:				str					       = MaskFormerImageProcessor(ignore_index=lowerCamelCase__	, reduce_labels=lowerCamelCase__			)

	__lowerCamelCase				:				List[str]					       = image_processor(lowerCamelCase__	, return_tensors='pt'			)

	__lowerCamelCase				:				Union[str, Any]					       = model(**lowerCamelCase__			)

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

	if model_name == "maskformer-swin-tiny-ade":
		__lowerCamelCase				:				Optional[Any]					       = torch.tensor(
		    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]			)
	assert torch.allclose(outputs.class_queries_logits[0, :3, :3]	, 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__":
					a		=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."""
					)

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








'''simple docstring'''




import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__snake_case    						=logging.getLogger(__name__)

@dataclass
@add_start_docstrings(TrainingArguments.__doc__		)
class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	bool		=     field(default=__lowercase       ,	metadata={'''help''': '''Whether to use SortishSampler or not.'''}		)
     lowerCamelCase				:	bool		=     field(
         default=__lowercase       ,	metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}		)
     lowerCamelCase				:	Optional[int]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': (
                 '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `max_length` value of the model configuration.'''
             )
         }       ,	)
     lowerCamelCase				:	Optional[int]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': (
                 '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `num_beams` value of the model configuration.'''
             )
         }       ,	)
     lowerCamelCase				:	Optional[Union[str, Path, GenerationConfig]]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
         }       ,	)





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  List[str]:
        lowerCAmelCase     =			super().to_dict()
        for k, v in d.items():
           if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ):
              lowerCAmelCase     =			v.to_dict()
        return d





